research analysis course

Data Analysis Courses

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Case Studies in Functional Genomics

Perform RNA-Seq, ChIP-Seq, and DNA methylation data analyses, using open source software, including R and Bioconductor.

Introduction to Bioconductor

The structure, annotation, normalization, and interpretation of genome scale assays.

Advanced Bioconductor

Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale consortium-generated genomic data.

High-Dimensional Data Analysis

A focus on several techniques that are widely used in the analysis of high-dimensional data.

Statistical Inference and Modeling for High-throughput Experiments

A focus on the techniques commonly used to perform statistical inference on high throughput data.

Introduction to Linear Models and Matrix Algebra

Learn to use R programming to apply linear models to analyze data in life sciences.

Statistics and R

An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.

Quantitative Methods for Biology

Learn introductory programming and data analysis in MATLAB, with applications to biology and medicine.

Principles, Statistical and Computational Tools for Reproducible Data Science

Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others.

Machine Learning and AI with Python

Learn how to use decision trees, the foundational algorithm for your understanding of machine learning and artificial intelligence.

Data Science: R Basics

Build a foundation in R and learn how to wrangle, analyze, and visualize data.

Data Science: Visualization

Learn basic data visualization principles and how to apply them using ggplot2.

Data Science: Probability

Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.

Data Science: Inference and Modeling

Learn inference and modeling: two of the most widely used statistical tools in data analysis.

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Data Analysis Courses and Certifications

Learn Data Analysis, earn certificates with paid and free online courses from Harvard, Stanford, MIT, University of Pennsylvania and other top universities around the world. Read reviews to decide if a class is right for you.

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Class Central's Top Data Analysis Courses

We've picked the best online courses to learn Data Analysis from the Class Central catalog.

Some courses are concise and get you up to speed in no time, others will be more comprehensive.

Data Analysis with Python

Google data analytics, data analyst bootcamp, the analytics edge (spring 2017), data analysis with python: zero to pandas, excel data analysis basics class - data analysis & bi made easy with excel power tools.

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• Advanced (129)
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The Analytics Edge

Through inspiring examples and stories, discover the power of data and use analytics to provide an edge to your career and your life.

• 13 weeks, 10-15 hours a week
• Free Online Course (Audit)

Introduction to Transforming with Data Analytics and the Digital Organization

Learn how data analytics powers the digital organization and gives it a competitive edge.

• 4 weeks, 2-3 hours a week

Data Analyst

Master data analysis: clean messy data, uncover insights, make predictions with machine learning, and effectively communicate findings using Python, NumPy, pandas, Matplotlib, and Seaborn.

• 2 months 4 days 18 hours 48 minutes
• Paid Course

Reproducible Research

Aprenda conceitos e ferramentas para realizar análises de dados reprodutíveis, incluindo programação literária, Markdown e knitr. Desenvolva habilidades para publicar documentos científicos verificáveis e reutilizáveis.

Data Analysis with R

Data is everywhere and so much of it is unexplored. Learn how to investigate and summarize data sets using R and eventually create your own analysis.

• Free Online Course

Learn Python-based data analysis using Numpy, Pandas, and visualization libraries. Covers data cleaning, manipulation, and visualization techniques for extracting insights from various data sources.

• freeCodeCamp
• Free Certificate

Foundations of Data Analysis - Part 1: Statistics Using R

Use R to learn fundamental statistical topics such as descriptive statistics and modeling.

• 6 weeks, 3-6 hours a week

Sabermetrics 101: Introduction to Baseball Analytics

An introduction to sabermetrics, baseball analytics, data science, the R Language, and SQL.

• 4 weeks, 6-8 hours a week

Learn Excel data analysis using Power Tools: formulas, PivotTables, Power Query, Power Pivot, and Power BI. Create reports, dashboards, and visualizations for effective business decision-making with real-world datasets.

Fundamentals of Qualitative Research Methods

Learn the fundamentals of Qualitative Research Methods, developing a qualitative research question and get data using interviews, focus groups and analyse the data in this course by Yale University

• 1 hour 30 minutes

High-Dimensional Data Analysis

A focus on several techniques that are widely used in the analysis of high-dimensional data.

• 4 weeks, 2-4 hours a week

Causal Diagrams: Draw Your Assumptions Before Your Conclusions

Learn simple graphical rules that allow you to use intuitive pictures to improve study design and data analysis for causal inference.

• 9 weeks, 2-3 hours a week

Principles, Statistical and Computational Tools for Reproducible Data Science

Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others.

• 8 weeks, 3-8 hours a week

Managing Data Analysis

Learn to effectively manage data analysis processes, from question formulation to result interpretation. Gain skills in directing teams, exploring datasets, and creating impactful presentations of statistical findings.

• 8 hours 43 minutes

People Analytics

Explore data-driven techniques for recruiting, retaining, and managing talent. Learn to leverage analytics for performance evaluation, staffing, collaboration, and talent development in organizations.

• 8 hours 35 minutes

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Data Analysis for Life Sciences

Master key concepts using the r programming language.

This HarvardX professional certificate program gives learners the necessary skills and knowledge to analyze data in the life sciences.

What You'll Learn

Technological advances have transformed fields that rely on data by providing a wealth of information ready to be analyzed. From working with single genes to comparing entire genomes, biomedical research groups around the world are producing more data than they can handle and the ability to interpret this information is a key skill for any practitioner. The skills necessary to work with these massive datasets are in high demand, and this series will help you learn those skills.

Using the open-source R programming language, you’ll gain a nuanced understanding of the tools required to work with complex life sciences and genomics data. You’ll learn the mathematical concepts — and the data analytics techniques — that you need to drive data-driven research. From a strong foundation in statistics to specialized R programming skills, this series will lead you through the data analytics landscape step-by-step.

Taught by Rafael Irizarry from the Harvard T.H. Chan School of Public Health, these courses will enable new discoveries and will help you improve individual and population health. If you’re working in the life sciences and want to learn how to analyze data, enroll now to take your research to the next level.

The course will be delivered via edX and connect learners around the world. 

Courses in this Program

2–4 hours per week, for 4 weeks An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.

2–4 hours per week, for 4 weeks Learn to use R programming to apply linear models to analyze data in life sciences.

2–4 hours per week, for 4 weeks A focus on the techniques commonly used to perform statistical inference on high throughput data.

2–4 hours per week, for 4 weeks A focus on several techniques that are widely used in the analysis of high-dimensional data.

Your Instructor

Rafael Irizarry

Professor of Biostatistics at Harvard University Read full bio.

Michael Love

Assistant Professor, Departments of Biostatistics and Genetics at UNC Gillings School of Global Public Health Read full bio.

Job Outlook

• R is listed as a required skill in 64% of data science job postings and was Glassdoor’s Best Job in America in 2016 and 2017. (source: Glassdoor)
• Companies are leveraging the power of data analysis to drive innovation. Google data analysts use R to track trends in ad pricing and illuminate patterns in search data. Pfizer created customized packages for R so scientists can manipulate their own data.
• 32% of full-time data scientists started learning machine learning or data science through a MOOC, while 27% were self-taught. (source: Kaggle, 2017)
• Data Scientists are few in number and high in demand. (source: TechRepublic)

Ways to take this program

When you enroll in this program, you will register for a Verified Certificate for all 4 courses in the Professional Certificate Series. 

Alternatively, learners can Audit the individual course for free and have access to select course material, activities, tests, and forums. Please note that Auditing the courses does not offer course or program certificates for learners who earn a passing grade.

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Qualitative Data Analysis

This course provides an applied approach to qualitative data analysis through the lens of multiple methods and methodologies.

About this Course

The analysis of qualitative research data is a fundamental yet multifaceted process that requires careful attention to the unique qualities of qualitative research design. This course provides an applied, phenomenological approach to qualitative data analysis. It is designed for an interdisciplinary audience with examples taken from the nonprofit, commercial, and government sectors in the health and social sciences.

Undergraduate/graduate students, research staff, and IRB members in particular may find this course meaningful as an introduction to qualitative research methods.

Course Preview:

Language Availability: English

Suggested Audiences: Faculty, IRB Chairs, IRB Members, Research Staff, Undergraduate and Graduate Students

Organizational Subscription Price: $675 per year/per site for government and non-profit organizations; $750 per year/per site for for-profit organizations Independent Learner Price: $99 per person

Course Content

" role="button"> introduction to qualitative data analysis.

This module discusses the data analysis considerations shared by all qualitative methods and approaches this course covers. This includes the basic qualitative data analysis process and tools and the rigorous and ethical approaches to qualitative data analysis that apply across methods.

Recommended Use: Required ID (Language): 20971 (English) Author(s): Margaret R. Roller, MA - Roller Research

" role="button"> In-Depth Interview Method

This module begins with an overview of the basic in-depth interview method and its variations. This provides the foundation for the core discussions concerning the distinctive aspects of the in-depth interview method that affect qualitative data analysis, including quality and ethical considerations.

Recommended Use: Supplemental ID (Language): 20972 (English) Author(s): Margaret R. Roller, MA - Roller Research

" role="button"> Focus Group Discussion Method

To provide a basis for the core discussions, this module begins with an overview of the fundamentals of the focus group method and its variations. This provides an understanding of the distinctive aspects of the focus group method that affect qualitative data analysis, including quality and ethical considerations.

Recommended Use: Supplemental ID (Language): 20973 (English) Author(s): Margaret R. Roller, MA - Roller Research

" role="button"> Ethnography

Understanding the ethnographic approach and its variations is important to the discussion of data analysis. For this reason, the module begins with an overview of ethnographic research and the distinctive aspects of ethnography that affect qualitative data analysis, including quality and ethical considerations.

Recommended Use: Supplemental ID (Language): 20974 (English) Author(s): Margaret R. Roller, MA - Roller Research

" role="button"> Narrative Research

This module provides an overview of narrative research and its variations. It provides an overview of narrative research, which serves as a foundation for the core discussions concerning the distinctive aspects of the narrative research approach that affect qualitative data analysis. The module concludes with a discussion of quality and ethical considerations.

Recommended Use: Supplemental ID (Language): 20975 (English) Author(s): Margaret R. Roller, MA - Roller Research

" role="button"> Case Study Research

Case study research and its variations are examined at the start of this module. Then, distinctive aspects of case study research that affect qualitative data analysis are explored, including quality and ethical considerations.

Recommended Use: Supplemental ID (Language): 20976 (English) Author(s): Margaret R. Roller, MA - Roller Research

" role="button"> Qualitative Content Analysis Method

This module reviews the basic Qualitative Content Analysis (QCA) method and its variations. It also discusses the distinctive aspects of the QCA method that affect qualitative data analysis and the quality and ethical considerations that QCA presents.

Recommended Use: Supplemental ID (Language): 20977 (English) Author(s): Margaret R. Roller, MA - Roller Research

Who should take the Qualitative Data Analysis course?

The suggested audience includes students, faculty, and staff that want to learn more about the basics of qualitative data analysis and one or more of the discussed methods.

How long does it take to complete the Qualitative Data Analysis course?

This course consists of one required module and six supplemental modules. All learners should complete module 1 and then complete the supplemental modules as needed (20-30 minutes each).

" role="button"> Why should an organization subscribe to this course?

Organizational subscriptions provide access to the organization's affiliated members. This allows organizations to train individuals across the organization on how to properly conduct qualitative data analysis.

" role="button"> What are the standard recommendations for learner groups?

This course is designed such that learners should complete the first module and then any following method modules as needed.

" role="button"> Is this course eligible for continuing medical education credits?

This course does not currently have CE/CME credits available.

Related Content

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Essentials of observational research protocol design and development.

Foundational course that orients and prepares learners to engage with the scholarly publication process in an informed way.

An in-depth review of the development and execution of protocols.

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Best Data Analytics Certificates Online

Best data analytics certificates online of 2024.

Updated: Jun 4, 2024, 10:04am

Data-related roles are some of the most versatile and in-demand careers in the United States. With more access to consumer data than ever, companies rely on data analysts to optimize their financial, marketing and production decisions. As a result, demand for data scientists is expected to triple over the next decade, the U.S. Bureau of Labor Statistics reports.

Data analysts work in nearly all sectors, including government, marketing, insurance, accounting, education and manufacturing. At any scale, they help organizations run efficiently by organizing, managing and interpreting data. Depending on the project scope, they use software, programming skills and machine learning to identify patterns and predict outcomes.

If you enjoy working with numbers, tackling complicated projects and learning new technology, a data-oriented career may suit you. Completing a data analyst certificate online can help you develop competitive skills that all industries need. Below, we’ll explore the 10 best online data analytics certificates.

Why You Can Trust Forbes Advisor Education

Forbes Advisor’s education editors are committed to producing unbiased rankings and informative articles covering online colleges, tech bootcamps and career paths. Our ranking methodologies use data from the National Center for Education Statistics , education providers, and reputable educational and professional organizations. An advisory board of educators and other subject matter experts reviews and verifies our content to bring you trustworthy, up-to-date information. Advertisers do not influence our rankings or editorial content.

• 6,290 accredited, nonprofit colleges and universities analyzed nationwide
• 52 reputable tech bootcamp providers evaluated for our rankings
• All content is fact-checked and updated on an annual basis
• Rankings undergo five rounds of fact-checking
• Only 7.12% of all colleges, universities and bootcamp providers we consider are awarded

Our Methodology

We ranked 24 accredited, nonprofit colleges offering online undergraduate data analytics certificates in the U.S. using 12 data points in the categories of credibility, affordability, student outcomes and student experience. We pulled data for these categories from reliable resources such as the Integrated Postsecondary Education Data System ; private, third-party data sources; and individual school and program websites. Data is accurate as of February 2024.

We scored schools based on the following metrics:

Student Outcomes:

• Graduation rate within eight years of normal time
• Pell Grant recipient graduation rate
• Retention rate
• Pell Grant graduation rate vs. overall graduation rate
• Ability to transfer certificate credits to a degree program

Student Experience:

• Student-to-faculty ratio
• Socioeconomic diversity
• Program’s coursework (excluding student orientations, field experiences and labs) is available 100% online
• Portion of undergraduate students enrolled in at least some distance learning courses

Credibility:

• Fully accredited
• Nonprofit status

Affordability:

• Per-credit tuition rate

We chose the 10 best schools to display based on those receiving a curved final score of 88% or higher.

Find our full list of methodologies here .

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Should You Earn a Data Analyst Certificate Online?

Accreditation for online data analytics certificates, how to find the best online data analytics certificate for you, frequently asked questions (faqs) about online data analytics certificates, texas a&m university – college station, university of richmond, mount saint mary’s university, champlain college, maryville university of saint louis, university of north texas, franklin university, pennsylvania western university, grand valley state university, university of arizona, best for students on a budget.

Certificate Tuition

$260/credit

Time Commitment

Can Certificate Credits Transfer to a Degree?

Texas A&M University offers an undergraduate data analytics certificate for students with some college experience. To enroll in the upper-level university, learners should have at least 30 transferable college credits. Applicants with a bachelor’s degree can opt for the graduate-level certificate.

The undergrad certificate emphasizes business analytics with real-world applications, covering theory and practice, data visualization, data mining, and web and social analytics. The College of Business Administration carries accreditation from the Association to Advance Collegiate Schools of Business.

• School Type: Public
• Certificate Prerequisites: At least 30 transferable college credits; minimum 2.0 transfer GPA
• Certificate Credit Requirements: 18 credits
• Example Courses: Data mining, web and social analytics
• Certificate Graduation Requirements: Minimum 2.0 GPA
• In-Person Requirements: No

Best for customization

$575/credit

27-36 hours per week for 30 weeks

At the Virginia-based University of Richmond , learners with bachelor’s degrees can pursue a certificate of applied studies in data analytics. Instead of following a set curriculum, students complete two core courses and choose four electives, with options focusing on big data, ethics, Java programming and predictive analytics.

Students can also set their learning pace, finishing the program in two semesters, in one year or one class at a time. Enrollees can complete the mostly asynchronous coursework online but might have the opportunity to take some on-campus classes. Graduates gain lifetime access to the school’s career services.

• School Type: Private
• Certificate Prerequisites: Bachelor’s degree, minimum 2.0 cumulative GPA
• Example Courses: Introduction to analytics, data visualization
• Certificate Graduation Requirements: N/A

Best for beginners

$350/credit

Mount Saint Mary’s University (MSMU) in Los Angeles creates opportunities by prioritizing social mobility, diversity and financial accessibility. Its six-course data analytics certificate helps learners build foundational skills, with a two-part “programming for everyone” series that introduces Python, SQL and database design. Throughout the project-based curriculum, students learn to design experiments, evaluate results, solve business problems using machine learning algorithms and improve predictive models.

MSMU offers relatively affordable tuition and several avenues for financial aid, including merit scholarships and support for undocumented learners.

• Certificate Prerequisites: High school diploma or GED® credential
• Example Courses: Programming for everyone, principles and techniques of data analytics

Best for career switchers

$335/credit

Based in Burlington, Vermont, Champlain College ’s data science certificate program is open to undergrads and bachelor’s degree holders. The coursework emphasizes career-ready skills that learners can apply to their jobs right away, including Python, data storage and manipulation, statistical analysis and presenting solutions.

Learners hoping to make a career switch can add on the fast-start formula career bundle, which includes a self-paced course, webinars and an ebook. Students learn to identify their career goals, create a résumé, develop a personal brand and create a job search strategy.

• Certificate Prerequisites: High school diploma or GED credential
• Example Courses: Introduction to Python, advanced data analytics

Best for students with some experience

$540/credit

10-15 hours/week per eight-week course; 48 weeks

Maryville University of Saint Louis , based in Missouri, offers a stackable, customizable data science certificate. The coursework explores industry-standard programming languages and tools like Amazon web services, MySQL and Databricks. The program’s 18 credits count toward a B.S. in data science or computer science.

Aside from the six required courses, applicants must complete a college algebra class. Students with prior experience in data science can replace two core courses with electives, allowing them to focus on new skills. After completing their certificate, learners can stack on related certificates in AI, cybersecurity or software development.

• Certificate Prerequisites: College algebra course
• Example Courses: Machine learning, big data analytics

Best for flexibility

$387/credit

Denton-based University of North Texas (UNT) allows students to earn an undergraduate data analytics certificate in a way that works for them. Learners can get facetime with instructors through optional weekly meetings, but the program does not require any synchronous participation. Enrollees have access to online tutoring, a career center and a writing center.

Students can finish the program in seven months by taking two courses per eight-week term, and UNT Online offers five start dates each year. Additionally, learners can enroll in focused, four-week micro-courses worth one credit each, which build into a certificate.

• Certificate Credit Requirements: 15 credits
• Example Courses: Principles of data structures, harvesting and wrangling; methods for discovery and learning from data

Best for accounting professionals

$398/credit

Franklin University in Columbus, Ohio, offers the only accounting-focused certificate on our ranking. The online accounting data analytics certificate teaches learners to create financial decision-making models, identify causes and solutions to business problems, design efficient systems, and communicate their findings. The curriculum covers Excel, Python and Tableau.

The 16-credit program can help graduates prepare for the CPA exam or earn licensure. Potential job titles include accounting analyst, auditor, audit analyst and financial analyst.

• Certificate Credit Requirements: 16 credits
• Example Courses: Accounting data analytics, accounting information systems

Best for SAS® certification

$322/credit

Pennsylvania Western University partnered with SAS, an industry-standard analytics platform, to create a program that leads to professional certification. The data science certificate helps learners prepare for two SAS exams, which help them earn certification. They also receive 50% off exam fees.

The 15-credit program includes a capstone course, allowing students to practice using SAS in a hands-on project. PennWest operates three Pennsylvania campuses but charges roughly the same tuition for in-state and out-of-state students.

• Example Courses: Big data tools, data preparation and cleaning

Best for students with college credit

$500/credit

Grand Valley State University (GVSU), located in Allendale, Michigan, delivers its applied data analytics certificate through six-week accelerated courses. Students get an introduction to applied statistics, learn to code in Python and R, and develop data visualization skills. The program requires four core courses and offers two elective options.

GVSU also offers stackable, short-term badges that can supplement students’ learning in areas such as data analytics, database management and cybersecurity.

All learners pay the same tuition, regardless of state residency.

• Certificate Prerequisites: Junior standing or 55 college credits
• Example Courses: Predictive analytics, statistics in the media

Best for career-ready training

The University of Arizona offers a data science and visualization undergraduate certificate through its online campus. Students gain job-ready skills and practical experience by using various data science techniques, platforms and programming languages to address realistic data problems.

The curriculum provides many choices for electives courses, including computational social science, introduction to machine learning and applied cyberinfrastructure concepts.

Learners can complete this certificate on its own or in tandem with an undergraduate degree. Up to six credits can count toward both the certificate and a degree from UArizona.

• Certificate Prerequisites: High school diploma or equivalent credential, completion of Arizona’s core competency requirements; first-year admission guaranteed for students in the top 25% of their high school class or who have a minimum 3.0 unweighted GPA
• Certificate Credit Requirements: 12 credits
• Example Courses: Statistical foundations of the information age, applied data visualization
• Certificate Graduation Requirements: Minimum 2.0 GPA, $15 graduation fee

An online data analytics certificate can prepare you for new opportunities in a high-paying, high-demand field. In many cases, you can pursue a beginner-friendly certificate instead of a four-year degree, which might save you significant time and money. However, online college isn’t the right choice for every learner. Consider the following factors before enrolling.

• Your learning style. Flexible asynchronous programs allow you to watch lectures and complete coursework on your own schedule. While this might sound ideal, an asynchronous format requires an extra level of commitment, organization and time management. If you prefer collaborative learning, look for synchronous or hybrid components.
• Your prior commitments. How much time can you dedicate to your education? If you work full time or handle responsibilities at home, you might need to enroll in a part-time program that suits your schedule. On the other hand, seek out full-time or accelerated programs if you want to enter the workforce as quickly as possible.
• Your budget. Online programs save you money on transport, housing and food. Since certificates require fewer credits than full degrees, they offer a more affordable education. However, not all programs accept federal student aid, so assess your budget and total costs before committing.

To narrow down your search for the best data analytics certificate, check each prospective school’s accreditation status. During the college accreditation process, schools submit to third-party evaluation to assess the quality of their financial management, student outcomes and faculty. You can only access federal student aid if you attend an accredited institution.

Additionally, attending an accredited school makes it easier for your credits to transfer to another college. That’s a crucial factor if you plan to use your certificate as a starting point for a bachelor’s or master’s degree.

Institutional accreditation comes from accrediting bodies approved by the U.S. Department of Education or the Council for Higher Education Accreditation (CHEA). To confirm a school’s status, consult CHEA’s directory .

Individual departments and programs can also earn programmatic accreditation, which varies in importance depending on the field. Most certificates on our list do not hold programmatic accreditation. However, Texas A&M University’s business school is accredited by the Association to Advance Collegiate Schools of Business.

Most data analytics certificates cover basic career skills such as Python programming , data management and data visualization tools. But several of the programs on our list offer specialized curriculums focusing on particular skills, topics or softwares.

Finding the best online data analytics program for you depends on your academic history, interests, goals, timeline and budget. Below, we’ll discuss a few factors to consider.

Consider Your Future Goals

Data analysts can find lucrative work in nearly every industry, including marketing, finance, insurance, healthcare and education. To identify your ideal program, clarify your long-term goals, and research interesting industries and job titles. Evaluate your existing strengths and identify your improvement areas. For example, you might need to become proficient in industry software like SAS or Ersi.

If you already have professional experience, look for schools that allow you to test out of basic courses or pursue new electives. Otherwise, many programs can take you from a total beginner to an entry-level practitioner.

Your enrollment options will be determined by your level of education. Some programs welcome learners with only a GED credential, while others require a bachelor’s degree. The right certificate gets you closer to your academic goals, whether you want to pursue a more advanced degree later or supplement your existing degrees.

Know Your Budget and Financing Options

Earning a data analyst certificate online can offer a more affordable education. Online programs typically cost less than their in-person counterparts, and certificates require fewer credits than full degrees.

Total tuition for the schools on our list ranges from $4,680 to $10,350. Per-credit tuition rates depend on factors like your enrollment status, whether you attend a public or private school, and where you live.

Keep in mind that while our ranked programs only require 12 to 18 credits, you may have to earn a certain number of credits before enrolling, which may incur additional costs. For example, some programs require incoming certificate students to have 30 to 55 transferable credits, which increases your investment significantly.

To lower your overall cost, look for financial aid opportunities. Start by filling out the FAFSA ®, which determines your eligibility for federal grants. You can also look for merit- and need-based scholarships from colleges, nonprofit groups and government agencies. Finally, you may consider taking out a loan, but this option requires repayment with interest.

Which certificate is good for data analysts?

The best data analyst certificate depends on your career goals and current skills. In general, look for programs from accredited colleges that cover foundational skills, including data analysis techniques, Python programming, data visualization and machine learning.

Is a data analytics certificate worth it?

Yes, data analytics certificates can help you advance in your current role or start a new career. Academic certificates range from $4,600 to $10,000 in total tuition, which could eventually pay off in a high-paying, in-demand career.

Can you be a data analyst with just a certificate?

You typically need a bachelor’s degree to work as a data analyst. However, a certificate may help you land an entry-level job or move into a data-oriented role from another field.

Which course is best for data analytics?

Data analytics courses should cover programming languages like Python and R, software like Excel and Tableau, data analysis theory and practice, big data, machine learning and predictive analytics.

Kimberlee Leonard has 22 years of experience as a freelance writer. Her work has been featured on US News and World Report, Business.com and Fit Small Business. She brings practical experience as a business owner and insurance agent to her role as a small business writer.

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• Introduction to Data Analysis

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• Market Research

This online Principles Express course will introduce you to the critical concepts common to the analysis of quantitative research data, with special attention to survey data analysis. These concepts will help analysts, buyers of research services, and those designing research. Knowing how best to look at data and derive insights is critical to ensuring that the information has a positive impact on the business.

The numbers tell a story. Learn how to read it.

The business intelligence world is developing quickly, and industry leaders are noticing. This online Principles Express course is the career opportunity you’ve been looking for to get in on the ground floor. A world of strategic assets awaits your translation. Deciphering vast amounts of raw data into intelligence allows you to navigate endless possibilities. Navigate the information age with confidence.

The data is there. Listen and transform it into valuable insight.

This online Principles Express course introduces you to the critical concepts familiar to quantitative research data analysis, with special attention to survey data analysis. Learn concepts that help analysts, buyers of research services, and those designing research knowi how best to derive insights from data critical to ensuring the information has a positive impact on the business.

Learning Objectives

Who should attend, course information.

After completing this course you should be able to:

• Describe the process of creating an analysis plan, and give examples of alternative analytic purposes (e.g., explanatory versus confirmatory).
• Describe the key data sources.
• Name and define the key data types (nominal, ordinal, interval, ratio, etc.).
• Explain the process of matching analytic techniques to different situations and needs, and give examples.
• Summarize descriptive and visual approaches used to familiarize oneself with the data and to identify problems with the data.
• Explain how to assess the impact of missing responses, and select and apply appropriate remedies.
• State the reasons for and methods of statistically adjusting data; e.g., weighting, variable re-specification, and scale transformation.
• Assess the characteristics of the distribution of the data and explain the implications of normality, non-normality, skewness, and multimodal data.
• Illustrate the process for creating and testing hypotheses.
• Compare and contrast the differences between type I and type II errors, and their potential impact on business decisions.
• Describe the difference between statistical and business significance in the context of group comparisons, and explain the factors that have an impact on statistical significance.
• Describe the difference between association and causality, and the potential impact on business decisions and outcomes.
• Identify the major computer programs in current use in market research for the analysis of data.
• Explain how to turn findings into market research conclusions, link findings to business decisions, and create actionable recommendations.

Successful enrollees earn a Digital Badge and 1.2 University of Georgia Continuing Education Units (CEU).

• Entry-level researchers looking for a solid introduction to quantitative data analysis.
• Mid-level staff seeking to expand their skillset.
• Experienced researchers looking to catch up with the latest developments.
• Corporations seeking professional development options for their internal training portfolio.
• Suppliers seeking courses for new-employee onboarding.
• Researchers whose job involves leading or contributing to project design.
• Analysts needing to understand how best to analyze quantitative data, and the pitfalls to avoid.
• Client-side researchers responsible for designing research and ensuring that the analysis leads to reliable insights.
• People just entering the research field who want to understand this important aspect of the research process.
• Enroll at any time
• Complete the course's required graded components within 30 days
• For more details on How Does “Introduction to Data Analysis” Course Work , please download the file.
• For Frequently Asked Questions , please download the file.

$359 - Standard Fee

$329 - Association Discount (Members* of: Insights Association; ESOMAR; Canadian Research Insights Council, The Research Society, Intellus Worldwide, QRCA, AMAI, WAPOR-Latinoamérica, MRII Board of Directors, UGA MMR Advisory Board.)

$50 - One-Month Extension (only one extension is granted per participant)

*Membership will be verified.

Prepayment is required to be registered. The prices listed are per person (US Funds). Prices are subject to change.

Ray Poynter – Managing Director, The Future Place & Founder of NewMR

Ray has spent the last 35 years at the intersection of innovation, technology, and Market Research, during which time Ray has held director level positions with Vision Critical, Virtual Surveys, The Research Business, Millward Brown, Sandpiper and IntelliQuest.

Continuing Education Information

Students successfully completing graded components earn a Digital Badge and 1.2 University of Georgia Continuing Education Unit (CEU) from The University of Georgia.

As a graduate of the course you will be recognized by industry associations, employers, peer groups and other professionals as understanding how to translate your research findings into reports and presentations that grab your audience’s attention, address the business decision your client needs to make, and offer sound and useful recommendations. This recognition will help you advance in your company and the industry.

There are no prerequisites for enrolling in Introduction to Data Analysis . However, the course assumes some knowledge of basic research design and quantitative research practices.

What knowledge is assumed by the Course? You should be familiar with:

• Sampling and sampling error (which is covered in a separate Course, Sampling in Market Research )
• Types of data variables (which is covered in a separate Course, Measurement and Questionnaire Design )
• Weighting (which is covered in a separate Course, Sampling in Market Research )
• It would be helpful to have some familiarity with secondary data, which is covered in detail in a separate course, Working with Secondary Data: Syndicated and Big Data .

See Principles Express courses for more details.

Preferred Browser: To take advantage of the different features (PDF files, URLs/links to external websites, animated exercises, audio and video clips) you should use a Windows or Macintosh-based browser . A robust browser such as Chrome , Firefox , Microsoft Edge , or Safari and a fast internet connection provide the best experience. The online platform supports many popular web browser versions. To find out if your computer's current software configuration is compatible, see System & Software Requirements .

Preferred Browser: For optimal use of our platform's features, including PDF files, URLs to external websites, animated exercises, and audio and video clips, we recommend using a laptop or desktop computer. To ensure the best experience, we suggest using web browsers such as Chrome , Firefox , Microsoft Edge , or Safari , along with a fast internet connection. You can check our System & Software Requirements to confirm if your computer's current software configuration is compatible.

Suggested Textbook (not required) Malhotra, Naresh K.,  Essentials of Marketing Research: A Hands-On Orientation , Pearson Education: Upper Saddle River, NJ. ISBN-13: 978-0-13-340182-0 (digital subscription edition)

Included in the online course are suggested reading assignments from the above textbook. These readings are not required content and will not be part of the testing for the course. The textbook suggestions are simply intended to add additional depth to your understanding of the topic.

Details are subject to change.

Select the organization to visit their websites.

Founding Organizations

Proud Corporate Sponsors of MRII

Supporting Organizations

According to a comprehensive study by the American Statistical Association, ineffective data analysis practices account for nearly 40% of flawed research conclusions . Additionally, a survey across 500 universities found that over 90% of research projects require advanced statistical and data skills to ensure research integrity and impact.

This certified program is designed to empower your expertise in practical statistical analysis, data wrangling, and visualization techniques for rigorous research practices . You will gain in-depth proficiency in key statistical methods including regression, ANOVA, factor analysis, and structural equation modeling . Additionally, this program will provide hands-on training in essential data skills like cleaning, merging, reshaping, and manipulating large datasets . Through real-world examples and coding templates, you will master data visualization and reporting best practices to effectively communicate insights.

With a focus on current research standards, this program will guide you through selecting optimal techniques, avoiding pitfalls, and correctly interpreting results. By working through real research case studies, you will gain invaluable perspective into leveraging statistics and data science to maximize research credibility and influence . With an emphasis on reproducibility and ethics, this program will equip you with the skills to design robust analyses, transparently convey limitations, and reinforce conclusions .

Upon successfully completing the program, you will be awarded the Certification in Advanced Statistical Techniques, Data Analysis and Visualization for Research Analysis , elevating your professional credentials and demonstrate your expertise in developing, evaluating, and deploying statistical and data visualization techniques for research analysis. As an industry-recognized credential, it affirms your commitment to statistical and data proficiency for impactful, ethical research. The certification has lifelong validity to serve as an ongoing testament to your specialized research capabilities.

1 Chartered Institute of Professional Certifications' reviews and ratings are a comprehensive collection of participants feedback we have gathered over the last decade. To view authentic and personal testimonials that are handwritten by our participants, please download our participants' written reviews here .

This program will entitle you:

• Over 10 hours of powerful course content on research analysis, including statistical concepts, data cleaning, preparation, and visualization, various research techniques, research design, hypothesis and corelation tips.
• By the end of the program, you will have mastered advanced research analysis, opening doors to exciting new data-driven careers where your skills will be highly valued. You will be recognized for your expertise in statistical techniques and data visualization which is highly sought after in various industries.
• Full lifetime access
• Singapore and Asia Pacific: +65 6716 9980
• Australia and New Zealand: +61 3 9909 7310
• UK, Europe and Middle East: +44 (020) 335 57898
• USA : +1 888 745 8875

7 days money back guarantee

Accreditations

This Certification in Advanced Statistical Techniques, Data Analysis and Visualization for Research Analysis is a mark of distinction that signifies a fully qualified expert in conducting complex research analysis while applying sophisticated analytical methods and data visualization to unlock impactful insights. It is accredited by the Chartered Institute of Professional Certifications, which maintains the governing standards for all members. The certification is governed and maintained by Chartered Institute of Professional Certifications with over 90,000 members around the world. It is also endorsed by prominent associations and organizations worldwide.

The content of this program has been fully certified by CPD as adhering to Continuing Professional Principles.

What Can You Expect From This Certified Program

As the world becomes increasingly data-driven, skilled research analysis is critical for organizations to gain actionable insights. With IDC estimating that global data volume will exponentially grow to 175 zettabytes by 2025, the ability to draw meaningful insights through rigorous analysis techniques separates pioneering research from the ordinary.

Led by Thanos Petsakos, an award-winning data scientist and research analysis expert, this comprehensive certification program will equip you with cutting-edge strategies and latest research analysis capabilities needed to derive impactful insights. The program provides an in-depth exploration of critical topics and skills needed to master complex data analysis, building from fundamental statistical concepts and inductive/deductive approaches to formulating incisive research questions . You will delve into descriptive and inferential statistical analysis, quantitative methods like regression and ANOVA, and principles of impactful data visualization and representation to help you to effectively communicate data insights .

Throughout the program, you will obtain hands-on experience in exploratory data analysis (EDA), sampling techniques, data correlation, and evaluating how research philosophy influences analysis . This program will also pay special attention to ethical considerations in statistical analysis and data manipulation , enabling you to uphold the highest standards of research integrity. You will also learn how to assess and compare international regulatory guidelines, academic standards, and ethical norms in data analysis , offering you a broad perspective on global research practices.

By the end of the program, you will have obtained the sophisticated analytical skillset and knowledge to tackle real-world research challenges and establish yourself as a skilled research analysts ready to extract and translate insights from multifaceted data.

Upon successful completion of the program, you will earn the Certification in Advanced Statistical Techniques, Data Analysis and Visualization for Research Analysis . This distinguished certification will serve as a testament to your mastery of advanced analytical skills and proficiency in translating data insights, establishing you as an esteemed expert in driving complex research analysis and data-driven decision-making. Globally recognized and valued, this industry-recognized certification holds lifelong validity underscores your expertise and excellence in the field of research analysis.

Key Skills You Will Gain

• Inductive Approach
• Deductive Approach
• Statistical Analysis
• Data Visualization
• Data Inference
• Statistical Hypothesis
• Research Design
• Exploratory Data Analysis (EDA)
• Quantitative Data Analysis
• Data Sampling
• Data Correlation
• Research Philosophy
• Data Analysis
• Regression Model
• Analytical Thinking
• Data Modelling
• Advanced Statistical Techniques
• Time Series Analysis
• Model Selection and Evaluation Strategies
• Analysis of Variance (ANOVA)
• Identifying Data Trends
• Research Formulation

Your Faculty Director

Thanos Petsakos is an award-winning presenter, renowned for his expertise in the fields of data science and machine learning. With a rich background as a data scientist in prominent Fortune 500 companies such as Ernst & Young, Thanos has honed his skills and acquired extensive experience in delivering impactful data-driven solutions . His profound knowledge and ability to effectively present complex concepts have made him a sought-after guest speaker at prestigious seminars and conferences around the globe. Notably, he was bestowed with the Best Presenter title at the DSIT 2021 conference in Shanghai, China , solidifying his reputation as an exceptional presenter.

In addition, Thanos shares his expertise as a distinguished instructor in influential universities across Europe and renowned institutions worldwide . He has also contributed significantly to the field through his authorship of numerous articles and co-authorship of books dedicated to advancing the field of data science . Thanos Petsakos's impressive career trajectory, profound expertise, and remarkable contributions continue to shape and inspire the field, solidifying his status as a leading expert in the industry.

Advanced Statistical Techniques, Data Analysis and Visualization for Research Analysis Program Agenda

Expand all Collapse all

46 Lectures

10 hours 1 minute

Overview of 'Statistical Techniques and Data Analysis'

Lesson 1 - Importance of Statistical Techniques in Research Analysis

Lesson 2 - Exploring Different Types of Data and Their Characteristics

Overview of 'Inductive and Deductive Approaches in Research Analysis'

Lesson 1 - Understanding the Inductive and Deductive Approaches and their Applications in Research

Lesson 2 - Exploring the Process of Formulating Research Questions and Hypotheses

Overview of 'Statistical Analysis Techniques and Applications'

Lesson 1 - Introduction to Various Statistical Analysis Techniques - Descriptive Statistics

Lesson 2 - Introduction to Various Statistical Analysis Techniques - Inferential Statistics

Lesson 3 - Types of Inferential Statistics and Deep Dive

Lesson 4 - Application of Statistical Techniques in Research Analysis

Quiz - Statistical Analysis Techniques and Applications

Overview of 'Data Visualization and Its Importance'

Lesson 1 - Exploring the Significance of Data Visualization in Research Analysis

Lesson 2 - Data Visualization - Heat Maps, Box Plots and More

Lesson 3 - Choosing the Right Visualization for Your Data

Lesson 4 - Introduction to Data Visualization Tools and Techniques

Lesson 5 - Bonus Section on Data Visualization

Quiz - Data Visualization and Its Importance

Overview of 'Data Inference and Statistical Hypothesis'

Lesson 1 - Understanding the Process of Data Inference

Lesson 2 - Introduction to Estimation

Lesson 3 - Exploring Statistical Hypothesis Testing and Its Applications

Lesson 4 - Understanding the Power of a Statistical Test

Overview of 'Research Design Principles and Considerations'

Lesson 1 - Understanding the Principles and Considerations for Designing Research Studies

Lesson 2 - Limitations & Biases

Lesson 3 - Exploring Different Types of Research Designs and their Appropriateness

Quiz - Research Design Principles and Considerations

Overview of 'Exploratory Data Analysis (EDA) Techniques and Approaches'

Lesson 1 - Introduction to EDA Techniques for Analyzing and Summarizing Data

Lesson 2 - Exploring Graphical and Numerical EDA Techniques

Lesson 3 - Geographical Data and Multivariate Analysis

Quiz - Exploratory Data Analysis (EDA) Techniques and Approaches

Overview of 'Quantitative Data Analysis Methods and Applications'

Lesson 1 - Introduction to Quantitative Data Analysis Methods

Lesson 2 - Types of Regression & When to Use Them

Lesson 3 - Application of Quantitative Data Analysis in Research

Overview of 'Sampling Techniques for Collecting Data'

Lesson 1 - Understanding the Importance of Sampling in Research Analysis

Lesson 2 - Exploring Different Sampling Techniques and Their Applications

Overview of 'Analyzing Data Correlation and Research Philosophy'

Lesson 1 - Exploring the Concept of Data Correlation and Its Implications

Lesson 2 - Introduction to Research Philosophy and Its Impact on Data Analysis

Quiz - Analyzing Data Correlation and Research Philosophy

Case Study 1 - IBM

Case Study 2 - Netflix

Case Study 3 - U.S. Census Bureau

Case Study 4 - Cleveland Clinic

Case Study 5 - Starbucks

Chartered Exam

At the end of the program, there will be an exam comprised of 50 multiple choice questions. Upon passing the exam, you will be accredited with a Certification in Advanced Statistical Techniques, Data Analysis and Visualization for Research Analysis .

• 98% of students who have taken this exam have passed the exam successfully. Almost all professionals who have taken our courses have passed this exam and attain their certification.
• The exam has 50 multiple choice questions and you need to answer 25 out of 50 questions correctly to pass the exam
• You can re-take the exam online as many times as you want , with no additional charges for retaking of exams
• The program cost already includes the exam fees, so there are no additional charges for taking the exam

If you have challenges passing the exam, you can secure assistance from our team and faculty leader to help you pass the exam.

Certification in Advanced Statistical Techniques, Data Analysis and Visualization for Research Analysis

Upon completing the program and passing the exam, you will receive the prestigious Certification in Advanced Statistical Techniques, Data Analysis and Visualization for Research Analysis . This distinguished certification will establish you as a recognized expert in applying effective statistical analysis techniques and data visualization for research analysis. It not only elevates your professional credibility but also enhance your marketability to potential employers or clients, making you stand out as a highly skilled and knowledgeable research analyst.

Developed by the esteemed Chartered Institute of Professional Certifications , the certification is a highly regarded industry-recognized certificate that signifies excellence in the area of robust research analysis. In addition, the program content has been independently accredited and certified by CPD , ensuring that it meets the highest standards in continuing professional development.

Program Pricing

On Demand Learning

• Lifetime access to high-quality self-paced eLearning content curated by industry experts
• 24x7 learner assistance and support
• Attain your Certificate in Advanced Statistical Techniques, Data Analysis and Visualization for Research Analysis that you can use across your professional credentials.
• Demonstrate your professional expertise in research analysis through dynamic statistical analysis and data visualization techniques.
• Over 11 hours of powerful course content on statistical techniques transforming your organization’s growth to new heights.
• Hand-on activities and 5 powerful case examples to master effective statistical analysis and data visualization techniques.

Corporate Training

Customized To Your Needs

• If you have more than 30 participants from your organizations, you can contact us to host a program customized to your training needs through a blended learning or physical classroom model
• By customizing your program, the faculty leader can tailor the content that is specific to your needs
• Prior to the commencement of the bespoke program, we will schedule conference calls to identify your training needs and map out the sessions most relevant to your organization

Download Brochure

You can download our program brochure below. If you have any inquiries on this program, please contact our Program Advisor at [email protected] or call us at:

100% Money Back Guarantee

We value the trust of our customers immensely. But, if you feel that this Advanced Statistical Techniques, Data Analysis and Visualization for Research Analysis does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!

Advanced Statistical Techniques, Data Analysis and Visualization for Research Analysis Reviews and Testimonials

Deborah Dansby - Data Scientist at Commonwealth Bank

Amie-Leigh Johanna - Data Architect at Cognizant

Melissa J. Dorsett - Business Intelligence Analyst at University of Sydney

Chartered Institute of Professional Certifications' reviews and ratings are a comprehensive collection of participants feedback we have gathered over the last decade. To view authentic and personal testimonials that are handwritten by our participants, please download our participants' written reviews here .

Convincing Your Boss To Attend This Program

Securing approval from your boss to attend this program can be difficult if you don’t know what to say. So we’ve prepared a template for you! All you need to do is to download this template, copy and paste in email and send it to your boss!

Download the Template Here

Advanced Statistical Techniques, Data Analysis and Visualization for Research Analysis

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• Business Essentials
• Leadership & Management
• Credential of Leadership, Impact, and Management in Business (CLIMB)
• Entrepreneurship & Innovation
• Digital Transformation
• Finance & Accounting
• Business in Society
• For Organizations
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• Harvard Business School →
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Business Analytics

Key concepts, who will benefit, college students and recent graduates, those considering graduate school, mid-career professionals.

What You Earn

Certificate of Completion

Boost your resume with a Certificate of Completion from HBS Online

Earn by: completing this course

Credential of Readiness

Prove your mastery of business fundamentals

Earn by: completing the three-course CORe curriculum and passing the exam

Describing and Summarizing Data

• Visualizing Data
• Descriptive Statistics
• Relationship Between Two Variables

Featured Exercises

Sampling and estimation.

• Creating Representative and Unbiased Samples
• The Normal Distribution
• Confidence Intervals
• Amazon's Inventory Sampling

Hypothesis Testing

• Designing and Performing Hypothesis Tests
• Improving the Customer Experience

Single Variable Linear Regression

• The Regression Line
• Forecasting
• Interpreting the Regression Output
• Performing Regression Analyses
• Forecasting Home Video Units

Multiple Regression

• The Multiple Regression Equation
• Adapting Concepts from Single Regression
• Performing Multiple Regression Analyses
• New Concepts in Multiple Regression
• The Caesars Staffing Problem

Advance Your Career with Essential Business Skills

Our difference, about the professor.

Janice Hammond Business Analytics

Dates & eligibility.

No current course offerings for this selection.

All learners must be at least 18 years of age, proficient in English, and committed to learning and engaging with fellow participants throughout the course.

Learn about bringing this course to your organization .

Learner Stories

Business Analytics FAQs

How does the business analytics certificate program relate to the credential of readiness program.

In addition to being a standalone certificate program, Business Analytics is also one component course of the Credential of Readiness (CORe) program , which also includes Economics for Managers and Financial Accounting . Designed for those interested in learning business fundamentals more broadly, CORe program participants progress through the three courses in tandem, and the program concludes with a final exam.

What are the learning requirements in order to successfully complete Business Analytics, and how are grades assigned?

Participants are expected to fully complete all coursework in a thoughtful and timely manner. This will mean meeting each week’s deadline to complete a module of the course and fully answering questions posed therein, including satisfactory performance on the quizzes at the end of each module (earning an average score of 50% or greater). This helps ensure your cohort proceeds through the course at a similar pace and can take full advantage of social learning opportunities. A module is composed of a series of teaching elements (such as faculty videos, simulations, reflections, or quizzes) designed to impart the learnings of the course. In addition to module and assignment completion, we expect participation in the social learning elements of the course by offering feedback on others’ reflections and contributing to conversations on the platform. Participants who fail to complete the course requirements will not receive a certificate and will not be eligible to retake the course.

More detailed information on individual course requirements will be communicated at the start of the course. No grades are assigned for Business Analytics–participants will either be evaluated as complete or not complete.

For more information on grading, please refer to the Policies Page .

Are there grants for Business Analytics? How do I qualify?

Business Analytics participants may be eligible for financial aid based on demonstrated financial need. To receive financial aid, you will be asked to provide supporting documentation. Please refer to our Payment & Financial Aid page .

What materials will I have access to after completing Business Analytics?

You will have access to the materials in every prior module as you progress through the program. Access to course materials and the course platform ends 60 days after the final deadline in the program. At the end of each course module, you will be able to download a PDF summary of the module’s key takeaways. At the end of the program, you will receive a PDF compilation of all of the module summary documents.

How should I list my certificate on my resume?

Harvard Business School Online Certificate in Business Analytics [Cohort Start Month and Year]

List your certificate on your LinkedIn profile under "Education" with the language from the Credential Verification page:

School: Harvard Business School Online Dates Attended: [The year you participated in the program] Degree: Other; Certificate in Business Analytics Field of Study: Leave blank Grade: Complete Activities and Societies: Leave blank

For the program description on LinkedIn, please use the following:

Business Analytics is an 8-week, 40-hour online certificate program from Harvard Business School. Business Analytics introduces quantitative methods used to analyze data and make better management decisions. Participants hone their understanding of key concepts, managerial judgment, and ability to apply course concepts to real business problems. Business Analytics was developed by leading Harvard Business School faculty and is delivered in an active learning environment based on the HBS signature case-based learning method.

How does HBS Online Business Analytics relate to the Harvard Business Analytics Program?

HBS Online Business Analytics and the Harvard Business Analytics Program are completely separate programs.

HBS Online Business Analytics consists of approximately 40 hours of material delivered entirely online through the HBS Online course platform over an eight-week period.

The Harvard Business Analytics Program is an online certificate program offered through a collaboration between Harvard Business School, the John A. Paulson School of Engineering and Applied Sciences, and the Faculty of Arts and Sciences. The program consists of six core courses, two seminars, and two in-person immersions, and can be completed in as little as nine months.

How does Business Analytics differ from Data Science Principles and Data Science for Business?

These three courses cover different topics related to data and analytics and do so in different ways.

Business Analytics teaches participants to apply basic statistics to real business problems and includes hands-on practice implementing analyses in Excel. The course covers descriptive statistics, sampling and estimation, hypothesis testing, and regression analysis. The course is intended for individuals at all stages of their careers who would like to strengthen their analytical skills, including college students and recent graduates without a background in statistics, those considering an MBA or other graduate program, or professionals seeking data literacy.

Data Science Principles introduces key concepts in data science—such as prediction, causality, visualization, data wrangling, privacy, and ethics—but does so without coding or mathematical application. The course is intended for organizational leaders and managers to be prepared to act on data analysis and to decide whether data science applications are appropriate tools for their businesses or organizations. The course is also well suited for business operations specialists to understand the building blocks of basic data visualization.

Data Science for Business moves beyond the spreadsheet and provides a hands-on approach for demystifying the data science ecosystem and making you a more conscientious consumer of information. Starting with the questions you need to ask when using data for decision-making, this course will help you know when to trust your data and how to interpret the results. By the end of the course, you should understand how to create a data-driven framework for your organization or yourself; develop hypotheses and insights from visualization; identify data mistakes or missing components; and, speak the language of data science across themes such as forecasting, linear regressions, and machine learning to better lead your team to long-term success. You will learn how to create a compelling story that uses proven, collected data to make core business decisions, and explore coding environments such as R and visualization software.

Can I take CORe if I've taken Business Analytics?

By enrolling in the Business Analytics certificate program, participants will be ineligible to enroll in the CORe program. By enrolling in the CORe program, participants will be ineligible to enroll in Financial Accounting.

Related Programs

Credential of Readiness (CORe)

Designed to help you achieve fluency in the language of business, CORe is a business fundamentals program that combines Business Analytics, Economics for Managers, and Financial Accounting with a final exam.

Financial Accounting

In this accounting fundamentals course, discover what's behind the numbers in financial statements, such as balance sheets and income statements.

Economics for Managers

See the world through the lens of economics and gain the knowledge and skills to craft successful business strategy.

Research Analyst Certifications

Explore the top Research Analyst certifications that are important to a successful career.

Getting Started as a Research Analyst

• What is a Research Analyst
• How To Become
• Certifications
• Tools & Software
• LinkedIn Guide
• Interview Questions
• Work-Life Balance
• Professional Goals
• Resume Examples
• Cover Letter Examples

Getting Certified as a Research Analyst

Top research analyst certifications, best research analyst certifications, certified analytics professional (cap).

• A bachelor’s degree or higher in a related field, or equivalent experience.
• A minimum of three years of professional analytics-related experience for those with a master's degree or higher, or five years for those with a bachelor's degree.
• Agreement to adhere to the CAP Code of Ethics.
• Submission of a CAP application and application fee.
• Passing the CAP examination.
• Commitment to continuous education and renewal of the certification every three years.

Microsoft Certified: Data Analyst Associate

• Fundamental understanding of data repositories and data processing both on-premises and in the cloud.
• Basic knowledge of key data concepts and familiarity with the Microsoft Power Platform.
• Experience with Excel and formulas is recommended but not required.
• Passing the Exam DA-100: Analyzing Data with Microsoft Power BI to earn the certification.
• No formal education or prior certification is required, but practical experience with data analysis is beneficial.
• Understanding of how to use Power BI and its tools for data analysis, including creating reports, dashboards, and visualizations.

Data Science Certificate

• Basic understanding of high school level mathematics (especially algebra and calculus)
• Familiarity with fundamental concepts of programming (experience with Python is recommended)
• Access to a computer with internet connection to complete online coursework
• Completion of the courses in the prescribed sequence, as the program is cumulative
• Payment of the course fee or financial aid approval for each course within the program
• Commitment to self-paced learning, as the program is designed for flexible schedules

SAS Certified Advanced Analytics Professional Using SAS 9

• A fundamental understanding of analytical concepts and methodologies, including statistics, predictive modeling, and data mining.
• Proficiency in using SAS software for data analysis, which may require prior experience or training in SAS programming.
• Completion of relevant SAS coursework or training programs, such as 'Predictive Modeling Using SAS Enterprise Miner', 'Advanced Predictive Modeling Using SAS Enterprise Miner', and 'Text Analytics, Time Series, Experimentation, and Optimization'.
• Passing the required exams that are part of the certification process, which includes 'Predictive Modeling Using SAS Enterprise Miner', 'Advanced Predictive Modeling Using SAS Enterprise Miner', and 'Text Analytics, Time Series, Experimentation, and Optimization'.
• Recommended to have at least 6 months of experience in performing advanced analytics using SAS software.
• A bachelor's degree or equivalent in a related field (such as computer science, statistics, mathematics, or engineering) is often recommended but not strictly required.

IBM Data Analyst Professional Certificate

• No prior experience in data analysis or related fields is required.
• Basic computer literacy and proficiency in navigating online platforms.
• Access to a computer with internet connectivity to complete the online course modules and hands-on projects.
• Commitment to complete the series of courses included in the professional certificate program.
• Willingness to learn and use data analysis tools such as spreadsheets, SQL, and Python.
• Understanding of basic mathematical concepts may be beneficial but not mandatory.

Google Data Analytics Professional Certificate

• No formal education or prior experience in data analytics is required.
• Basic computer literacy and comfort with navigating computer applications are recommended.
• Access to a computer with an internet connection to complete the online course modules.
• Commitment to complete the eight courses included in the certificate program.
• Understanding of basic math and statistics is helpful but not mandatory.
• Proficiency in English, as the course content is delivered in English.

Certified Business Intelligence Professional (CBIP)

• A minimum of two years of full-time work experience in computer information systems, data modeling, data planning, data definitions, metadata systems development, enterprise resource planning, systems analysis, application development and programming, or information technology management
• Bachelor's degree or equivalent from an accredited college-level institution; however, a degree is not mandatory if the experience requirement is met
• Passing scores on three examinations: two mandatory (Information Systems Core and Data Warehousing) and one specialty exam of the candidate's choosing (Business Analytics, Data Analysis and Design, Data Integration, or Leadership and Management)
• Agreement to adhere to the TDWI Professional Code of Ethics
• Continuing education or re-certification required every three years to maintain the certification
• Payment of the appropriate exam and certification fees

Cloudera Certified Associate (CCA) Data Analyst

• Familiarity with basic SQL concepts, including writing queries and understanding database structures
• Knowledge of Apache Hadoop fundamentals and basic data warehousing concepts
• Experience with Cloudera's CDH environment, specifically with tools such as Impala and Hive
• Understanding of ETL processes and data transformation techniques
• No formal education or professional experience requirements, but practical experience in data analysis is recommended
• Passing the CCA Data Analyst Exam (CCA159), which includes hands-on, performance-based tasks on a pre-configured Cloudera Enterprise cluster

Data Analyst Associate (DAA)

• Bachelor’s degree in any discipline, although a degree in a quantitative field (like statistics, mathematics, economics, or computer science) is preferred.
• Basic understanding of statistics and quantitative methods.
• Familiarity with any programming language, preferably Python or R, is beneficial.
• Understanding of databases and experience with SQL is advantageous.
• Some prior exposure to data analysis or related coursework is recommended.
• Commitment to complete the prescribed DASCA study and preparation materials before taking the certification exam.

Tableau Desktop Certified Associate

• Familiarity with Tableau Desktop and data visualization concepts, typically through at least 5 months of experience.
• Understanding of and ability to perform tasks related to data connections, organizing and simplifying data, field and chart types, calculations, mapping, and dashboards.
• Completion of Tableau Desktop I: Fundamentals and Tableau Desktop II: Intermediate training courses or equivalent working experience.
• No formal education or degree requirements, but a background in data analysis or related fields can be beneficial.
• Access to Tableau Desktop to practice and prepare for the exam, as hands-on experience is crucial.
• Registration for the exam through the Tableau website, along with payment of the exam fee.

Oracle Business Intelligence Foundation Suite 11g Certified Implementation Specialist

• Understanding of data warehousing and data modeling concepts
• Experience with Oracle BI Enterprise Edition (OBIEE) 11g, including building repositories, reports, and dashboards
• Knowledge of SQL and other database technologies
• Familiarity with Oracle BI Publisher
• Basic understanding of network and security concepts relevant to Oracle BI
• Oracle recommends prior completion of relevant Oracle BI training courses or equivalent on-the-job experience

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Benefits of having a research analyst certification, how to choose the best research analyst certification.

• Identify Specialization and Skill Gaps: Determine your area of specialization or interest within research analysis, such as quantitative analysis, data science, or market research. Then, identify any skill gaps you may have. Choose certifications that will help you specialize further or fill those gaps, ensuring that you are equipped to meet the specific demands of your desired role or industry.
• Industry Demand and Value: Research the demand for certain certifications in your target industry. Certifications that are highly sought after by employers can increase your competitiveness in the job market. Look for certifications that add the most value to your profile, such as those that teach advanced statistical methods, data visualization, or programming skills relevant to research analysis.
• Accreditation and Credibility: Verify the accreditation and credibility of the certification body. Well-respected certifications from established organizations or educational institutions can lend credibility to your expertise and signal to employers that you have met a recognized standard of knowledge and competence in research analysis.
• Curriculum and Continuing Education: Examine the curriculum to ensure it covers current and emerging tools, technologies, and methodologies in research analysis. Opt for certifications that offer continuing education opportunities, so you can keep your skills sharp and stay abreast of new developments in the field.
• Networking and Professional Community: Consider certifications that provide access to a professional community or network. Being part of a community of research analysts can facilitate knowledge sharing, provide support, and open up opportunities for collaboration and career advancement.

Preparing for Your Research Analyst Certification

Certification faqs for research analysts, is getting a research analyst certification worth it, do you need a certification to get a job as a research analyst, can research analyst certifications help pivoters make the transition into data & analytics from another career path.

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The Beginner's Guide to Statistical Analysis | 5 Steps & Examples

Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organizations.

To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.

After collecting data from your sample, you can organize and summarize the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings.

This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.

Table of contents

Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarize your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results, other interesting articles.

To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.

Writing statistical hypotheses

The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.

A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.

While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.

• Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
• Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
• Null hypothesis: Parental income and GPA have no relationship with each other in college students.
• Alternative hypothesis: Parental income and GPA are positively correlated in college students.

Planning your research design

A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.

First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.

• In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
• In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
• In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.

Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.

• In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
• In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
• In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
• Experimental
• Correlational

First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.

In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.

Measuring variables

When planning a research design, you should operationalize your variables and decide exactly how you will measure them.

For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:

• Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
• Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).

Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.

Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.

In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.

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In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.

Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.

Sampling for statistical analysis

There are two main approaches to selecting a sample.

• Probability sampling: every member of the population has a chance of being selected for the study through random selection.
• Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.

In theory, for highly generalizable findings, you should use a probability sampling method. Random selection reduces several types of research bias , like sampling bias , and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.

But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to at risk for biases like self-selection bias , they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.

If you want to use parametric tests for non-probability samples, you have to make the case that:

• your sample is representative of the population you’re generalizing your findings to.
• your sample lacks systematic bias.

Keep in mind that external validity means that you can only generalize your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialized, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.

If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalized in your discussion section .

Create an appropriate sampling procedure

Based on the resources available for your research, decide on how you’ll recruit participants.

• Will you have resources to advertise your study widely, including outside of your university setting?
• Will you have the means to recruit a diverse sample that represents a broad population?
• Do you have time to contact and follow up with members of hard-to-reach groups?

Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.

Calculate sufficient sample size

Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.

There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.

To use these calculators, you have to understand and input these key components:

• Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
• Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
• Expected effect size : a standardized indication of how large the expected result of your study will be, usually based on other similar studies.
• Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarize them.

Inspect your data

There are various ways to inspect your data, including the following:

• Organizing data from each variable in frequency distribution tables .
• Displaying data from a key variable in a bar chart to view the distribution of responses.
• Visualizing the relationship between two variables using a scatter plot .

By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.

A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.

In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.

Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.

Calculate measures of central tendency

Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:

• Mode : the most popular response or value in the data set.
• Median : the value in the exact middle of the data set when ordered from low to high.
• Mean : the sum of all values divided by the number of values.

However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.

Calculate measures of variability

Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:

• Range : the highest value minus the lowest value of the data set.
• Interquartile range : the range of the middle half of the data set.
• Standard deviation : the average distance between each value in your data set and the mean.
• Variance : the square of the standard deviation.

Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.

Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.

From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.

It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.

A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.

Researchers often use two main methods (simultaneously) to make inferences in statistics.

• Estimation: calculating population parameters based on sample statistics.
• Hypothesis testing: a formal process for testing research predictions about the population using samples.

You can make two types of estimates of population parameters from sample statistics:

• A point estimate : a value that represents your best guess of the exact parameter.
• An interval estimate : a range of values that represent your best guess of where the parameter lies.

If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.

You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).

There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.

A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.

Hypothesis testing

Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.

Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:

• A test statistic tells you how much your data differs from the null hypothesis of the test.
• A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.

Statistical tests come in three main varieties:

• Comparison tests assess group differences in outcomes.
• Regression tests assess cause-and-effect relationships between variables.
• Correlation tests assess relationships between variables without assuming causation.

Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.

Parametric tests

Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.

A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).

• A simple linear regression includes one predictor variable and one outcome variable.
• A multiple linear regression includes two or more predictor variables and one outcome variable.

Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.

• A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
• A z test is for exactly 1 or 2 groups when the sample is large.
• An ANOVA is for 3 or more groups.

The z and t tests have subtypes based on the number and types of samples and the hypotheses:

• If you have only one sample that you want to compare to a population mean, use a one-sample test .
• If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
• If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
• If you expect a difference between groups in a specific direction, use a one-tailed test .
• If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .

The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.

However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.

You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:

• a t value (test statistic) of 3.00
• a p value of 0.0028

Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.

A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:

• a t value of 3.08
• a p value of 0.001

The final step of statistical analysis is interpreting your results.

Statistical significance

In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.

Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.

This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.

Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.

Effect size

A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.

In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .

With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.

Decision errors

Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.

You can aim to minimize the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.

Frequentist versus Bayesian statistics

Traditionally, frequentist statistics emphasizes null hypothesis significance testing and always starts with the assumption of a true null hypothesis.

However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.

Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval

Methodology

• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hostile attribution bias
• Affect heuristic

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Online full-time programs are offered as either Daytime, or a combination of Evenings and Saturdays. Check your program Dates and Times to see what the program commitment will be.

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In the current knowledge economy and information age, and as organizations recognize the growing need for research on which key policy and strategic decisions are based, the demand for social, market and marketing research has increased substantially. The Research Analyst graduate certificate program focuses on the theoretical, practical and ethical underpinnings of research while equipping you with the statistical, technical and professional skills necessary to do applied research in a variety of different settings throughout the public and private sectors.

In this program, you will focus on all of the major aspects of the research process including research design, information retrieval and evaluation, data collection, analysis and interpretation, and the preparation and presentation of research findings.

Special features of the program include:

• creating research deliverables
• critical analysis of research studies
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• data visualization
• new research technologies
• philosophical frameworks for research
• placement and career preparation
• professional and ethical concerns when designing research studies
• report, proposal and grant writing
• research project management

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In-Person - An in-person course is delivered fully on campus.

Online Asynchronous (A) - An online asynchronous course has no fixed class schedule and allows students to engage with the course at different times according to their needs. Faculty provide modules, which are completed independently by the students according to established deadlines.

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The chart below outlines the delivery options available for each course in this program, by campus. For some academic terms, there may be more than one delivery option available. You’ll be able to select your preferred options when building your course schedule during open enrolment. Preferences for course delivery will be considered on a first come, first served basis. Some Humber programs are also delivered fully online, where all courses are delivered online.

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Work-integrated learning.

Work-integrated learning opportunities prepare you for your future career. You will apply what you’ve learned in class and in real-world environments through a wide range of academic, community and industry partnerships. These work-integrated learning opportunities may include field experiences, professional practicums and co-operative education.

Field Experience

A field experience offers students an opportunity to engage in intensive experiences related to their field of study or career goals to build their skills, knowledge and abilities. Field experiences may be paid or unpaid.

Professional Practicum

Programs requiring a professional practicum offer practice-based experience or work hours for a professional license or certification. Students work under the direct supervision of an experienced professional. Placements are unpaid.

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Students in co-op programs gain experience through paid work terms in their field of study that become progressively more complex as their skill level increases.

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Students in co-op programs gain experience through paid work terms in their field of study that become progressively more complex as their skill level increases. The co-op portion of this program is optional.

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The Humber Advantage

What is a research analyst.

Is our program right for you? You should be detail-oriented, enjoy doing research, and have good communications skills. You care about accuracy and you enjoy thinking critically and analytically and then explaining what you have learned to others.

Student Testimonials

I enrolled in the Humber Research Analysts Post-Grad Program looking for a career change and in less than a year I was working in the research field. The program helped me by connecting me with industry professionals and providing me with the necessary skills needed to land a job in market research. The course gave me the chance to put my knowledge and skills into practice with an internship placement which ultimately lead me to a job. It's amazing how in a year so much has changed and I've been able to kick-start my career in this industry! –  N.P.

This program really works! With research courses comparable to those at a master's degree level, strong industry connections, and outstanding guest speakers, the program justified - and exceeded by far - all my expectations.  – V.T.

Scholarships

Research Analyst Entrance Scholarship

A Scholarship is presented to a student who has demonstrated academic proficiency (minimum of 80 per cent average) and involvement in extracurricular activities that clearly indicate a commitment to their community as selected by the scholarship committee. In addition, two reference (education, employment and/or community) letters are to be included in the submission package, these reference letters must explain the recipient's commitment to their studies, their academic and/or career goals and their focus on giving back to others. These aspects have also been addressed by the recipient through the submission of a letter addressed to the selection committee (1-2 pages in length), where they also indicate how this scholarship will enable them to succeed in their studies.

Logit Group Scholarship

Presented to students who identify as Black/African or Indigenous and are entering the Research Analyst Program. Students must demonstrate financial need, an interest in marketing research and have made contributions to their community. Preference will be given to student who are a Canadian citizen or permanent resident.

Excellence in Social Research

This annual award is presented to a graduate of the Research Analyst Graduate Certificate Program who has demonstrated a strong commitment to social issues as well as exceptional skills in social research while maintaining an 80% average of higher.

Academic Excellence Award in Research

This annual award is presented to the graduate of the Research Analyst Graduate Certificate Program who has achieved the highest overall average.

Research Analyst Award of Merit

Presented to a group of students who produced an exemplary research project in partial fulfillment of the requirements of RAPP 5011: Research Project Management. The research project authors will have demonstrated a mastery of research design, qualitative and/or quantitative data acquisition and analysis, as well as presentation skills.

Industry Partners

Your Career

Graduates of this program may find employment in the social, market or marketing research sectors as research analysts, data analysts, research co-ordinators and managers, research grant writers, and research field workers.

Program Availability

Domestic students can now apply to this program at Humber’s new International Graduate School (IGS) .

International students can now apply to this program at Humber’s new International Graduate School (IGS) .

Humber is a publicly-funded institution and does not have a public-private partnership. International students graduating from Humber or Humber’s International Graduate School (IGS) are eligible to apply for a Post-Graduation Work Permit .

International Students in Canada who apply for September 2024 start could be eligible for an automatic scholarship*. Apply now

Please note the new International Admissions Process and Provincial Attestation Letters. Read the update

International Students Out of Canada can Apply through Humber International

There's Still Time to Apply for this September

How to Apply  

Program Delivery Types

Block-based: Students select a pre-set weekly schedule of courses that best meets their needs. Block-Based schedules may include in-person, hybrid and online courses.

Course-based: Students create their own schedule of courses from among in-person, hybrid and online options.

Condensed Week - Courses requiring students to come to campus are scheduled over 2-3 days per week. Online courses are scheduled on other days.

Online - Courses are scheduled only online and may be delivered asynchronously, where students study independently or synchronously, where students attend the online class on a specified time and day.

Twilight - In-person, online synchronous and hybrid courses are generally scheduled after 3:00pm.

Twilight-Online: Online synchronous courses are generally scheduled after 3:00 pm.

Student Success

Watch the video to see how Humber's Research Analyst program helped Lisa find the job she loves.

The RAP Network, the central hub for Humber’s Research Analyst Program (RAP). Tune into our channels!

Building The Foundation: Humber College Helps Students Find Their Way Into Post-Secondary Education

The need for pathway programs has been highlighted by the COVID-19 pandemic and its impact on workforce opportunities.

Top 10 Reasons to Choose a Humber College Pathways Program

Associate Dean of Pathways and graduate, Cameron Farrar, share their top 10 reasons to choose one of the Arts and Science Pathways programs.

No news at this time.

Every attempt is made to ensure that information contained on this website is current and accurate. Humber reserves the right to correct any error or omission, modify or cancel any course, program, fee, timetable or campus location at any time without prior notice or liability to users or any other Person.

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GET YOUR $125 APPLICATION FEE BACK *

Apply on us this september.

Apply through OCAS between June 14 - July 19, 2024 and be registered full-time on Tuesday, September 17, 2024 and receive a $125 credit equal to the domestic application fee.

To qualify, get started by filling out the APPLY ON US form.

*This promotion is open to domestic applications for the September 2024 program intakes only. Offer not valid for existing applicants or applications.

International Students with a valid Study Permit or Letter of Introduction (LOI)* can receive a Waiver Code to cover the $100 September 2024 application fee.

Bringing You Our A-Game

A Humber education is second to none, benefit from:

• Career-focused Programs
• Work-integrated Learning Experiences
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How to Apply

Domestic students.

Applications to Humber are made through ontariocolleges.ca . Be sure to submit your application by July 15, 2024 to receive your Application Fee Credit. Applications for September will remain open as long as the program availability status shows Open.

To check program availability refer to the Campus/Availability listing on Humber’s program pages, search by availability , or ontariocolleges.ca .

To see where you are in the admissions process, visit the Admissions Road Map .

International Students

If you’re an international student, you can apply directly to Humber via our International Centre .

Admissions Questions

General enquiries.

Call 416-675-3111 or email [email protected] . If you have already applied, be sure to check your application status on myhumber.ca .

Domestic Applicants Enquiries

Domestic applicants can book a one-on-one advising appointment with an admissions representative.

International Applicants Enquiries

Contact the International Centre for information about full-time programs (including the International Graduate School), how to apply and to follow up on your submitted application.

Program-Specific Questions

Speak to the Program Co-ordinator about the course curriculum, projects and career options.

Mary Takacs, program co-ordinator 416.675.6622 ext. 73483 [email protected]

Campus Information

Book a campus tour to take a closer look at what it's like to be a student at Humber.

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Sign-up now for more info on Humber, including programs, special events and more!

How To Become An Apprentice

Becoming an apprentice.

Find an employer willing to sponsor you as an apprentice.

Contact the Ministry of Labour, Immigration, Training and Skills Development to register as an apprentice.

Work with your employer approximately one year before attending Humber.

View Instructions  

Ontario Youth Apprenticeship Program (OYAP)

If you’re in high school – grade 11 or 12 – you can earn co-op education credits through work placements in some skilled trades.

Visit OYAP  

Applications to Humber are made through ontariocolleges.ca . Be sure to submit your application by the equal consideration deadline of February 1. You may apply after February 1, however, post-February 1 applications will be considered on a first-come, first-served basis depending on the availability of the space in the program.

Need Advice?

Program advising appointments.

Get help narrowing down your program options or book a one-on-one pre-enrolment advising appointment with one of our Recruitment Officers.

Transfer & Pathway Advising

Book a virtual appointment with a Student Mobility Advisor learn more about getting Transfer Credit(s) for previous post-secondary experience, Prior Learning Assessment and Recognition (PLAR), and Pathways options.

Admission Requirements

Admission selection is based on the academic criteria indicated. Meeting minimum eligibility requirements does not guarantee admission.

Admission selection is based on the following three requirements:

To be eligible for admission, you must possess the following:

• A bachelor’s degree or equivalent

Mature Applicants

Diplomas and certificates.

An applicant is considered a mature applicant if they have not completed secondary school or other postsecondary school, and will be 19 or older as of the first day of classes. Humber will invite you for testing to demonstrate that you meet all listed course requirements.

An applicant is considered a mature applicant if they have not completed secondary school or attended postsecondary studies, and will be 21 or older as of the first day of classes. Mature applicants for degree programs will be required to meet course requirements at the U/M level or equivalent.

College Transfer Applicants

An applicant is considered a college transfer applicant if they have completed some or all of a college-level credential. Humber may use a combination of secondary school and/or college courses and grades to determine program eligibility.

An applicant is considered a college transfer applicant if they have completed some or all of a college-level credential. Humber may use a combination of secondary school and/or college courses and grades to determine program eligibility. Applicants must have an overall minimum grade point average (GPA) of 65 per cent in the program. Applicants are required to disclose and provide academic transcripts for all course work completed at the postsecondary level.

University Transfer Applicants

An applicant is considered a university transfer applicant if they have completed some or all of a university-level credential. Humber may use a combination of secondary school and/or university courses and grades to determine program eligibility.

An applicant is considered a university transfer applicant if they have completed some or all of a university-level credential. Humber may use a combination of secondary school and/or university courses and grades to determine program eligibility. Applicants are required to disclose and provide academic transcripts for all course work completed at the postsecondary level.

English Language Proficiency

All applicants whose first language is not English must meet Humber’s English Language Proficiency Policy .

International Credit Evaluation

Canadian citizens or permanent residents with international education are required to provide a credential evaluation. Note, for international High school education course by course evaluations, ICAS must be used. For international post-secondary education, a WES evaluation must be provided. In situations where you expect to apply for transfer credit, it is recommended that a course by course WES evaluation is completed.

International Academic Equivalency

Admission equivalencies for Humber depend on your country of study. Please enter your location or choose detect my location to see the requirements for your country below.

Applying with an International Baccalaureate (IB)

Post-Admission Requirements

Once you have been accepted, and have confirmed your offer, you may need to complete a further set of requirements related to your program (Post-Admission Requirements).

Equipment & Device Requirements

Semester 1, 2

Fees & Financial Aid

The 2024/2025 fee for three semesters is:

• domestic: $5,832.58
• international: $19,169.12

Fees are subject to change.

Fees by Semester

Domestic Fees by Semester

International Fees by Semester

*Plus Mandatory Health Insurance fee once per academic year: Fall start - $420 Winter start - $280 Summer start - $140

Financial Aid, Scholarships and Bursaries

Understand the costs associated with coming to Humber and explore resources available from first year to your final year on Student Fees and Financial Resources .

Humber Scholarships

Find out more about scholarships and bursaries that you may be eligible for, visit Student Scholarships . International students can visit International Student Scholarships .

Humber Bursaries

Bursaries are available for Certificate, Diploma and Degree programs primarily based on financial need, visit Humber Bursaries.

External Awards, Bursaries & Scholarships

Find out more information about external scholarships and bursaries, visit External Awards.

Indigenous Student Awards, Bursaries & Scholarships

Humber offers a variety of bursaries and scholarships for Indigenous students, visit Indigenous Student Awards.

Explore Opportunities through Humber Pathways

Humber Pathways include:

• Opportunities to build on your college education and complete your diploma or degree at Humber.
• Degree and graduate study opportunities at other institutions in Ontario, Canada and abroad.

Pathway Options

Below are a few examples of pathways into this program. For more information on a pathway, click the Details button. To see all of your possible pathway options, click the View All Pathways link underneath the table.

Additional information will be made available to students from their program before the beginning of the Winter term. Courses with in-person requirements will likely also have online components. The delivery mode of some courses is still being determined. Humber may need to change plans for in-person learning, subject to government and public health directives and/or additional health and safety considerations.

You can find a complete list of programs with downloads including program and course details at Current Student Resources  

Students in programs marked as online/in-person will have a combination of those two types of delivery. Additional information will be made available to students from their program in the first week of June. Courses with in-person requirements will likely also have online components. The delivery mode of some courses is still being determined. Humber may need to change plans for in-person learning, subject to government and public health directives and/or additional health and safety considerations.

Learning Outcomes:

Upon successful completion of the program, a graduate will:

Interpret primary and secondary data applying principles of currency, relevance, authority, accuracy and purpose.

Design and execute a marketing or social research project to provide recommendations and actionable insights.

Manage research projects with due consideration to budget, schedule, quality, scope, ethics, and data governance.

Prepare a variety of effective social and marketing research documents and deliverables using knowledge translation and mobilization practices.

Communicate results of research using effective and engaging written, oral, visual and multi-media modes, that are appropriate for diverse audience needs.

Respond to the evolving sensibilities/concepts of equity, diversity and cultural sensitivity in conducting research.

Mitigate risk by the detection of bias in research design and processes.

Collect data using varied robust, effective and appropriate quantitative and qualitative research methods.

Organize and analyze primary and secondary data using industry standard software tools.

Conduct research projects in accordance with legal, ethical and professional standards.

Develop a career ready profile through goal-setting, on-going professional learning and industry exposure.

Evaluate research evidence and findings for the identification of assumptions, soundness of logic and the veracity of claims.

Select and synthesize academic, trade, business, institutional, governmental, or non-governmental resources for appropriate research purposes.

Strategic Marketing and Research Analysis

Select start date and campus

• = Canadian Offering
• = International Offering
• AD = Accelerated Delivery
• Program description

Applying as a Canadian applicant

Domestic students should apply using a Conestoga College Program Application Form .

Campus tours

Campus tours are one of the best ways to experience Conestoga. During this time, we are offering online guided tours to show you all Conestoga has to offer.

Virtual tours

If you can't make an on-campus tour or attend one of our events, the virtual tour is a great way to visit us.

Courses - May 2025

Course details.

Professional Communications and Reporting   COMM8550

Getting into the job market is a tough nut to crack, and sustaining a career long-term requires a strong foundation of professional workplace communication skills. In this course, students will develop workplace communication skills for a professional research environment. Students will learn how to engage and build relationships with different stakeholders including clients, vendors, managers, and team members. They will learn how to communicate complex information to a variety of audiences and how to deliver convincing presentations. Topics covered include effective stakeholder communication and management, cultural competency, emotional intelligence, negotiation, conflict resolution, and appropriate use of electronic communications (e.g., text messaging, emails, and social media). Students will also learn how to create strategic visualizations, webinars and dashboards that can be used in core research projects at the workplace.

• Pre-Requisites:
• CoRequisites:

Information Technology for Marketers COMP8090

In this course, you will gain hands-on experience using software essential for marketers, including Microsoft Excel and SPSS. You will learn how to create spreadsheet documents and analyze data in a marketing environment. You will learn and apply advanced features of spreadsheet applications to develop comprehensive solutions to business marketing problems; and utilize the advanced database functions and business intelligence tools of a spreadsheet to make comprehensive and informed business marketing decisions. A variety of software features will be explored including spreadsheet designs; basic to advanced formulas and functions; charting and database features; PivotTables and Pivot Charts; dashboarding and monitoring tools, and a variety of formatting tools.

Conestoga 101 CON0101

Marketing Fundamentals MKT8500

In this course students will be introduced to traditional marketing concepts, analyses, and activities that comprise marketing management. These skills will help students to solve foundational marketing problems. Students will also be introduced to some of the basic formulas and arithmetic which marketers use to measure, evaluate, and plan different marketing activities. Topics include marketing strategy, segmentation, product management, pricing, promotion, distribution, market share analysis and competitive analysis.

Marketing Research: Strategy, Stakeholders, and Ethics   MKT8510

In this course students will explore the foundation of marketing research principles, such as how marketing research is used to inform marketing decisions, and how it supports the marketing function. Students will delve into the most important choices made in research design, such as the role of primary and secondary research, and when to use qualitative versus quantitative approaches. Students will learn about The Code of Conduct and industry association practices, and explore the impact of Canada's privacy legislation, the Personal Information Protection and Electronic Documents Act (PIPEDA). Topics include the traditional research designs used in marketing research, such as observation, hypothesis testing, experimental design and modeling. Once the key theoretical concepts are reviewed, they are applied in design-oriented case studies.

Quantitative Marketing Research Design, Execution, and Analysis  MKT8520

In this course, students will gain a thorough knowledge of the principles and tools of statistical reasoning and analysis. Students learn a variety of approaches to analyze quantitative data related to marketing research. Topics include sampling and weighting, probability, central tendency, variation, estimation, hypothesis testing, correlation analysis, statistical reasoning and interpretation and nonparametric statistics.

Qualitative Marketing Research Design, Execution, and Analysis  MKT8530

In this course, students will investigate current theories and applications of non-empirical or qualitative marketing research techniques. Topics include the latest marketing research techniques being used in the world of in-person and online qualitative market research, focus groups, mini-groups, in-depth interviews, projective techniques, bulletin boards, and live chat. Students will apply this learning with hands-on experience by analyzing recent case studies using qualitative research in areas such as advertising, communications, product development, policy development and customer satisfaction.

Marketing Psychology & Consumer Behaviour MKT8540

Great marketers know how their customers think and act. They pay extra attention to consumer behaviour: the way people buy and use products and services. Understanding consumer behaviour can help you crawl inside the mind of a customer and be more effective at marketing, design, packaging, product development, and every other initiative that impacts your buyer.

Career Management CDEV8132

Descriptive Design  MKT8550

In marketing research, it often proves to be the case that collected data is only as good as the questions that are asked. Students will develop research questionnaires in the context of consumer behaviour and public opinion. Focus is on the structure and design of questionnaires, including the purpose, use and construction of questions, such as open-ended, multiple choice, ranking, paired comparison, summated scales, product rating, attitude scales and demographic questions. Students will create different types of questions using practical exercises and discussions. Students will learn and leverage key survey software such as Qualtrics.

Strategic Marketing Case Analysis MKT8560

Walk the walk, talk the talk. By the time you get to your final semester of this program, you will have been exposed to the breadth and depth of marketing. In Strategic Marketing Case Analysis you’ll have the chance to pull it all together just like a real marketer, to make decisions, to debate your point of view, to critically assess and learn from real marketing events. You will use case studies to practice decision making, using a variety of marketing analytical tools. Through written reports, presentations and highly interactive class discussions, you will defend your analysis and recommendations. Once you’ve completed this capstone marketing course, you will be ready for the real world of marketing.

Marketing Research Technology and Analytics MKT8570

Great marketing decisions are typically based on the sophisticated analysis of timely in-depth consumer, competitor and environmental information. Students in this course will get hands-on experience with the tools used by the most advanced marketing consultants and large successful marketers. Topics include data mining, data segmentation, SPSS, predictive analytics, key marketing models, UX research, and applied learning through discussions, cases and projects.

Global Marketing MKT8580

Marketing in the global and intercultural context is an important principle for market researchers as the breadth of product penetration expands. In this course students will develop an appreciation for the principles of doing research on an international and intercultural level. The challenges of conducting research in major markets, such as the United States, Europe and Asia are also discussed. Students will review the European Society for Opinion and Marketing Research (ESOMAR) standards. The learning activities include case studies to focus on the international aspects of marketing research.

Marketing Research Capstone Project MKT8590

The Marketing Research Consultancy Capstone Project is designed to provide you with practical and comprehensive experience in managing real-life research consulting projects. Building on the foundation laid in the Strategic Marketing Research Analysis program, this course focuses on completing simulated client research projects. The entire research process is covered, from project inception and creation of objectives to the final presentation of the written report.

Overall, the course aims to bridge the gap between theory and practical application, preparing you for real-world challenges in the field of marketing research consulting.

• Pre-Requisites: MKT8520 AND MKT8530

Emerging Techniques in Market Research RSCH8290

Marketing Research and Analysis are dynamic fields, with new approaches rapidly evolving and changing the research landscape. In this course students will explore emerging techniques, ranging from Insight Communities to Text Analytics to Web Analytics, as well as current topics relevant to future careers. In addition, students will investigate practical applications of marketing research that may be of interest to potential employers and clients. Students will evaluate research methods and recommend appropriate techniques using real-life case studies. Software/Topics explored includes Tableau, Artificial Intelligence / Virtual Assistant Technology, Social Listening, Micro-Data, and ChatBot Research.

Program outcomes

• Apply marketing research techniques, analyze the results, and recommend actions to solve business challenges and answer questions for various audiences and stakeholders including internal, and client-facing.
• Analyze and interpret data as it relates to various aspects of a business organization's readiness to change
• Analyze, organize, and manipulate data to support problem solving, business decision-making, and opportunity identification
• Devising strategic insights and recommendations based on research evidence and assess potential threats within a larger organizational decision process.
• Design, construct, and communicate an applied marketing research project that applies both theoretical, and conceptual, using research tools and techniques to ensure client's business needs are achieved.
• Assess and apply business intelligence and Big Data tools appropriate to the business decisions, business problems, data movement, and system workloads
• Develop statistical and predictive models that use operational and marketing data to identify patterns and provide insights to business stakeholders
• Prepare and communicate complex materials verbally, in writing, and digitally for a variety of audiences, purposes, and in various levels of detail.
• Conduct consumer behaviour research, with a high degree of accuracy, and reliability, to evaluate outcomes and inform major business decisions.
• Deliver data-oriented projects using data science, business analysis, and project management principles, tools, and techniques to ensure client's business needs are achieved

Here’s how you know

• U.S. Department of Health and Human Services
• National Institutes of Health

Meditation and Mindfulness: Effectiveness and Safety

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Meditation has a history that goes back thousands of years, and many meditative techniques began in Eastern traditions. The term “meditation” refers to a variety of practices that focus on mind and body integration and are used to calm the mind and enhance overall well-being. Some types of meditation involve maintaining mental focus on a particular sensation, such as breathing, a sound, a visual image, or a mantra, which is a repeated word or phrase. Other forms of meditation include the practice of mindfulness, which involves maintaining attention or awareness on the present moment without making judgments.

Programs that teach meditation or mindfulness may combine the practices with other activities. For example, mindfulness-based stress reduction is a program that teaches mindful meditation, but it also includes discussion sessions and other strategies to help people apply what they have learned to stressful experiences. Mindfulness-based cognitive therapy integrates mindfulness practices with aspects of cognitive behavioral therapy.

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Meditation and mindfulness practices usually are considered to have few risks. However, few studies have examined these practices for potentially harmful effects, so it isn’t possible to make definite statements about safety. 

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A 2020 review examined 83 studies (a total of 6,703 participants) and found that 55 of those studies reported negative experiences related to meditation practices. The researchers concluded that about 8 percent of participants had a negative effect from practicing meditation, which is similar to the percentage reported for psychological therapies. The most commonly reported negative effects were anxiety and depression. In an analysis limited to 3 studies (521 participants) of mindfulness-based stress reduction programs, investigators found that the mindfulness practices were not more harmful than receiving no treatment.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} How popular are meditation and mindfulness?

According to the National Health Interview Survey, an annual nationally representative survey, the percentage of U.S. adults who practiced meditation more than doubled between 2002 and 2022, from 7.5 to 17.3 percent. Of seven complementary health approaches for which data were collected in the 2022 survey, meditation was the most popular, beating out yoga (used by 15.8 percent of adults), chiropractic care (11.0 percent), massage therapy (10.9 percent), guided imagery/progressive muscle relaxation (6.4 percent), acupuncture (2.2 percent), and naturopathy (1.3 percent).

For children aged 4 to 17 years, data are available for 2017; in that year, 5.4 percent of U.S. children used meditation. 

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In a 2012 U.S. survey, 1.9 percent of 34,525 adults reported that they had practiced mindfulness meditation in the past 12 months. Among those responders who practiced mindfulness meditation exclusively, 73 percent reported that they meditated for their general wellness and to prevent diseases, and most of them (approximately 92 percent) reported that they meditated to relax or reduce stress. In more than half of the responses, a desire for better sleep was a reason for practicing mindfulness meditation.

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Meditation and mindfulness practices may have a variety of health benefits and may help people improve the quality of their lives. Recent studies have investigated if meditation or mindfulness helps people manage anxiety, stress, depression, pain, or symptoms related to withdrawal from nicotine, alcohol, or opioids. 

Other studies have looked at the effects of meditation or mindfulness on weight control or sleep quality. 

However, much of the research on these topics has been preliminary or not scientifically rigorous. Because the studies examined many different types of meditation and mindfulness practices, and the effects of those practices are hard to measure, results from the studies have been difficult to analyze and may have been interpreted too optimistically.

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• A 2018 NCCIH-supported analysis of 142 groups of participants with diagnosed psychiatric disorders such as anxiety or depression examined mindfulness meditation approaches compared with no treatment and with established evidence-based treatments such as cognitive behavioral therapy and antidepressant medications. The analysis included more than 12,000 participants, and the researchers found that for treating anxiety and depression, mindfulness-based approaches were better than no treatment at all, and they worked as well as the evidence-based therapies.
• A 2021 analysis of 23 studies (1,815 participants) examined mindfulness-based practices used as treatment for adults with diagnosed anxiety disorders. The studies included in the analysis compared the mindfulness-based interventions (alone or in combination with usual treatments) with other treatments such cognitive behavioral therapy, psychoeducation, and relaxation. The analysis showed mixed results for the short-term effectiveness of the different mindfulness-based approaches. Overall, they were more effective than the usual treatments at reducing the severity of anxiety and depression symptoms, but only some types of mindfulness approaches were as effective as cognitive behavioral therapy. However, these results should be interpreted with caution because the risk of bias for all of the studies was unclear. Also, the few studies that followed up with participants for periods longer than 2 months found no long-term effects of the mindfulness-based practices.
• A 2019 analysis of 23 studies that included a total of 1,373 college and university students looked at the effects of yoga, mindfulness, and meditation practices on symptoms of stress, anxiety, and depression. Although the results showed that all the practices had some effect, most of the studies included in the review were of poor quality and had a high risk of bias.

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Few high-quality studies have examined the effects of meditation and mindfulness on blood pressure. According to a 2017 statement from the American Heart Association, the practice of meditation may have a possible benefit, but its specific effects on blood pressure have not been determined.

• A 2020 review of 14 studies (including more than 1,100 participants) examined the effects of mindfulness practices on the blood pressure of people who had health conditions such as hypertension, diabetes, or cancer. The analysis showed that for people with these health conditions, practicing mindfulness-based stress reduction was associated with a significant reduction in blood pressure.

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Studies examining the effects of mindfulness or meditation on acute and chronic pain have produced mixed results.

• A 2020 report by the Agency for Healthcare Research and Quality concluded that mindfulness-based stress reduction was associated with short-term (less than 6 months) improvement in low-back pain but not fibromyalgia pain.
• A 2020 NCCIH-supported analysis of five studies of adults using opioids for acute or chronic pain (with a total of 514 participants) found that meditation practices were strongly associated with pain reduction.
• Acute pain, such as pain from surgery, traumatic injuries, or childbirth, occurs suddenly and lasts only a short time. A 2020 analysis of 19 studies examined the effects of mindfulness-based therapies for acute pain and found no evidence of reduced pain severity. However, the same analysis found some evidence that the therapies could improve a person’s tolerance for pain.
• A 2017 analysis of 30 studies (2,561 participants) found that mindfulness meditation was more effective at decreasing chronic pain than several other forms of treatment. However, the studies examined were of low quality.
• A 2019 comparison of treatments for chronic pain did an overall analysis of 11 studies (697 participants) that evaluated cognitive behavioral therapy, which is the usual psychological intervention for chronic pain; 4 studies (280 participants) that evaluated mindfulness-based stress reduction; and 1 study (341 participants) of both therapies. The comparison found that both approaches were more effective at reducing pain intensity than no treatment, but there was no evidence of any important difference between the two approaches.
• A 2019 review found that mindfulness-based approaches did not reduce the frequency, length, or pain intensity of headaches. However, the authors of this review noted that their results are likely imprecise because only five studies (a total of 185 participants) were included in the analysis, and any conclusions made from the analysis should be considered preliminary.

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Mindfulness meditation practices may help reduce insomnia and improve sleep quality.

• A 2019 analysis of 18 studies (1,654 total participants) found that mindfulness meditation practices improved sleep quality more than education-based treatments. However, the effects of mindfulness meditation approaches on sleep quality were no different than those of evidence-based treatments such as cognitive behavioral therapy and exercise.

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Several clinical trials have investigated if mindfulness-based approaches such as mindfulness-based relapse prevention (MBRP) might help people recover from substance use disorders. These approaches have been used to help people increase their awareness of the thoughts and feelings that trigger cravings and learn ways to reduce their automatic reactions to those cravings.

• A 2018 review of 37 studies (3,531 total participants) evaluated the effectiveness of several mindfulness-based approaches to substance use disorder treatment and found that they significantly decreased participants’ craving levels. The mindfulness-based practices were slightly better than other therapies at promoting abstinence from substance use.
• A 2017 analysis specifically focused on MBRP examined 9 studies (901 total participants) of this approach. The analysis concluded that MBRP was not more effective at preventing substance use relapses than other treatments such as health education and cognitive behavioral therapy. However, MBRP did slightly reduce cravings and symptoms of withdrawal associated with alcohol use disorders.

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Studies have suggested that meditation and mindfulness may help reduce symptoms of post-traumatic stress disorder (PTSD).

• A 2018 review supported by NCCIH examined the effects of meditation (in 2 studies, 179 total participants) and other mindfulness-based practices (in 6 studies, 332 total participants) on symptoms of PTSD. Study participants included veterans, nurses, and people who experienced interpersonal violence. Six of the eight studies reported that participants had a reduction of PTSD symptoms after receiving some form of mindfulness-based treatment.
• A 2018 clinical trial funded by the U.S. Department of Defense compared the effectiveness of meditation, health education, and prolonged exposure therapy, a widely accepted treatment for PTSD recommended by the American Psychological Association. Prolonged exposure therapy helps people reduce their PTSD symptoms by teaching them to gradually remember traumatic memories, feelings, and situations. The study included 203 veterans with PTSD as a result of their active military service. The results of the study showed that meditation was as effective as prolonged exposure therapy at reducing PTSD symptoms and depression, and it was more effective than PTSD health education. The veterans who used meditation also showed improvement in mood and overall quality of life.

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Mindfulness-based approaches may improve the mental health of people with cancer.

• A 2019 analysis of 29 studies (3,274 total participants) of mindfulness-based practices showed that use of mindfulness practices among people with cancer significantly reduced psychological distress, fatigue, sleep disturbance, pain, and symptoms of anxiety and depression. However, most of the participants were women with breast cancer, so the effects may not be similar for other populations or other types of cancer.

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Studies have suggested possible benefits of meditation and mindfulness programs for losing weight and managing eating behaviors.

• A 2017 review of 15 studies (560 total participants) looked at the effects of mindfulness-based practices on the mental and physical health of adults with obesity or who were overweight. The review found that these practices were very effective methods for managing eating behaviors but less effective at helping people lose weight. Mindfulness-based approaches also helped participants manage symptoms of anxiety and depression.
• A 2018 analysis of 19 studies (1,160 total participants) found that mindfulness programs helped people lose weight and manage eating-related behaviors such as binge, emotional, and restrained eating. The results of the analysis showed that treatment programs, such as mindfulness-based stress reduction and mindfulness-based cognitive therapy, that combine formal meditation and mindfulness practices with informal mindfulness exercises were especially effective methods for losing weight and managing eating.

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Several studies have been done on using meditation and mindfulness practices to improve symptoms of attention-deficit hyperactivity disorder (ADHD). However, the studies have not been of high quality and the results have been mixed, so evidence that meditation or mindfulness approaches will help people manage symptoms of ADHD is not conclusive.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} How do meditation and mindfulness work?

Some research suggests that meditation and mindfulness practices may affect the functioning or structure of the brain. Studies have used various methods of measuring brain activity to look for measurable differences in the brains of people engaged in mindfulness-based practices. Other studies have theorized that training in meditation and mindfulness practices can change brain activity. However, the results of these studies are difficult to interpret, and the practical implications are not clear.

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NCCIH supports a variety of meditation and mindfulness studies, including:

• An evaluation of how the brain responds to the use of mindfulness meditation as part of a combined treatment for migraine pain.
• A study of the effectiveness of mindfulness therapy and medication (buprenorphine) as a treatment for opioid use disorder.
• A study of a mindfulness training program designed to help law enforcement officers improve their mental health by managing stress and increasing resilience.

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• Don’t use meditation or mindfulness to replace conventional care or as a reason to postpone seeing a health care provider about a medical problem.
• Ask about the training and experience of the instructor of the meditation or mindfulness practice you are considering.
• Take charge of your health—talk with your health care providers about any complementary health approaches you use. Together, you can make shared, well-informed decisions

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Nccih clearinghouse.

The NCCIH Clearinghouse provides information on NCCIH and complementary and integrative health approaches, including publications and searches of Federal databases of scientific and medical literature. The Clearinghouse does not provide medical advice, treatment recommendations, or referrals to practitioners.

Toll-free in the U.S.: 1-888-644-6226

Telecommunications relay service (TRS): 7-1-1

Website: https://www.nccih.nih.gov

Email: [email protected] (link sends email)

Know the Science

NCCIH and the National Institutes of Health (NIH) provide tools to help you understand the basics and terminology of scientific research so you can make well-informed decisions about your health. Know the Science features a variety of materials, including interactive modules, quizzes, and videos, as well as links to informative content from Federal resources designed to help consumers make sense of health information.

Explaining How Research Works (NIH)

Know the Science: How To Make Sense of a Scientific Journal Article

Understanding Clinical Studies (NIH)

A service of the National Library of Medicine, PubMed® contains publication information and (in most cases) brief summaries of articles from scientific and medical journals. For guidance from NCCIH on using PubMed, see How To Find Information About Complementary Health Approaches on PubMed .

Website: https://pubmed.ncbi.nlm.nih.gov/

NIH Clinical Research Trials and You

The National Institutes of Health (NIH) has created a website, NIH Clinical Research Trials and You, to help people learn about clinical trials, why they matter, and how to participate. The site includes questions and answers about clinical trials, guidance on how to find clinical trials through ClinicalTrials.gov and other resources, and stories about the personal experiences of clinical trial participants. Clinical trials are necessary to find better ways to prevent, diagnose, and treat diseases.

Website: https://www.nih.gov/health-information/nih-clinical-research-trials-you

Research Portfolio Online Reporting Tools Expenditures & Results (RePORTER)

RePORTER is a database of information on federally funded scientific and medical research projects being conducted at research institutions.

Website: https://reporter.nih.gov

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• Anheyer D, Leach MJ, Klose P, et al.  Mindfulness-based stress reduction for treating chronic headache: a systematic review and meta-analysis . Cephalalgia . 2019;39(4):544-555.
• Black LI, Barnes PM, Clarke TC, Stussman BA, Nahin RL.  Use of yoga, meditation, and chiropractors among U.S. children aged 4–17 years . NCHS Data Brief, no 324. Hyattsville, MD: National Center for Health Statistics. 2018.
• Breedvelt JJF, Amanvermez Y, Harrer M, et al.  The effects of meditation, yoga, and mindfulness on depression, anxiety, and stress in tertiary education students: a meta-analysis . Frontiers in Psychiatry . 2019;10:193. 
• Burke A, Lam CN, Stussman B, et al.  Prevalence and patterns of use of mantra, mindfulness and spiritual meditation among adults in the United States . BMC Complementary and Alternative Medicine. 2017;17(1):316.
• Carrière K, Khoury B, Günak MM, et al.  Mindfulness‐based interventions for weight loss: a systematic review and meta‐analysis . Obesity Reviews . 2018;19(2):164-177. 
• Cavicchioli M, Movalli M, Maffei C.  The clinical efficacy of mindfulness-based treatments for alcohol and drugs use disorders: a meta-analytic review of randomized and nonrandomized controlled trials . European Addiction Research . 2018;24(3):137-162.
• Cillessen L, Johannsen M, Speckens AEM, et al.  Mindfulness‐based interventions for psychological and physical health outcomes in cancer patients and survivors: a systematic review and meta‐analysis of randomized controlled trials . Psychooncology . 2019;28(12):2257-2269.
• Creswell JD.  Mindfulness interventions . Annual Review of Psychology. 2017;68:491-516.
• Davidson RJ, Kaszniak AW.  Conceptual and methodological issues in research on mindfulness and meditation . American Psychologist. 2015;70(7):581-592.
• Farias M, Maraldi E, Wallenkampf KC, et al.  Adverse events in meditation practices and meditation-based therapies: a systematic review . Acta Psychiatrica Scandinavica. 2020;142(5):374-393. 
• Garland EL, Brintz CE, Hanley AW, et al.  Mind-body therapies for opioid-treated pain: a systematic review and meta-analysis . JAMA Internal Medicine . 2020;180(1):91-105.
• Goldberg SB, Tucker RP, Greene PA, et al. Mindfulness-based interventions for psychiatric disorders: a systematic review and meta-analysis . Clinical Psychology Review . 2018;59:52-60.
• Grant S, Colaiaco B, Motala A, et al.  Mindfulness-based relapse prevention for substance use disorders: a systematic review and meta-analysis . Journal of Addiction Medicine . 2017;11(5):386-396. 
• Haller H, Breilmann P, Schröter M et al.  A systematic review and meta‑analysis of acceptance and mindfulness‑based interventions for DSM‑5 anxiety disorders . Scientific Reports . 2021;11(1):20385.
• Hilton L, Hempel S, Ewing BA, et al.  Mindfulness meditation for chronic pain: systematic review and meta-analysis . Annals of Behavioral Medicine. 2017;51(2):199-213.
• Hirshberg MJ, Goldberg SB, Rosenkranz M, et al.  Prevalence of harm in mindfulness-based stress reduction . Psychological Medicine. August 18, 2020. [Epub ahead of print]. 
• Intarakamhang U, Macaskill A, Prasittichok P.  Mindfulness interventions reduce blood pressure in patients with non-communicable diseases: a systematic review and meta-analysis . Heliyon. 2020;6(4):e03834.
• Khoo E-L, Small R, Cheng W, et al.  Comparative evaluation of group-based mindfulness-based stress reduction and cognitive behavioural therapy for the treatment and management of chronic pain: a systematic review and network meta-analysis . Evidence-Based Mental Health.  2019;22(1):26-35.
• Levine GN, Lange RA, Bairey-Merz CN, et al.  Meditation and cardiovascular risk reduction: a scientific statement from the American Heart Association . Journal of the American Heart Association. 2017;6(10):e002218.
• Nidich S, Mills PJ, Rainforth M, et al.  Non-trauma-focused meditation versus exposure therapy in veterans with post-traumatic stress disorder: a randomised controlled trial . Lancet Psychiatry . 2018;5(12):975-986.
• Niles BL, Mori DL, Polizzi C, et al.  A systematic review of randomized trials of mind-body interventions for PTSD . Journal of Clinical Psychology . 2018;74(9):1485-1508.
• Rogers JM, Ferrari M, Mosely K, et al.  Mindfulness-based interventions for adults who are overweight or obese: a meta-analysis of physical and psychological health outcomes . Obesity Reviews . 2017;18(1):51-67. 
• Rosenkranz MA, Dunne JD, Davidson RJ.  The next generation of mindfulness-based intervention research: what have we learned and where are we headed? Current Opinion in Psychology. 2019;28:179-183.
• Rusch HL, Rosario M, Levison LM, et al.  The effect of mindfulness meditation on sleep quality: a systematic review and meta-analysis of randomized controlled trials . Annals of the New York Academy of Sciences . 2019;1445(1):5-16. 
• Schell LK, Monsef I, Wöckel A, et al. Mindfulness-based stress reduction for women diagnosed with breast cancer. Cochrane Database of Systematic Reviews. 2019;3(3):CD011518. Accessed at cochranelibrary.com on June 3, 2022.
• Semple RJ, Droutman V, Reid BA.  Mindfulness goes to school: things learned (so far) from research and real-world experiences . Psychology in the Schools. 2017;54(1):29-52.
• Shires A, Sharpe L, Davies JN, et al.  The efficacy of mindfulness-based interventions in acute pain: a systematic review and meta-analysis . Pain . 2020;161(8):1698-1707. 
• Van Dam NT, van Vugt MK, Vago DR, et al.  Mind the hype: a critical evaluation and prescriptive agenda for research on mindfulness and meditation . Perspectives on Psychological Science. 2018;13(1):36-61. 

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• American Academy of Pediatrics Section on Integrative Medicine. Mind-body therapies in children and youth. Pediatrics . 2016;138(3):e20161896.
• Coronado-Montoya S, Levis AW, Kwakkenbos L, et al. Reporting of positive results in randomized controlled trials of mindfulness-based mental health interventions. PLoS One . 2016;11(4):e0153220.
• Dakwar E, Levin FR. The emerging role of meditation in addressing psychiatric illness, with a focus on substance use disorders. Harvard Review of Psychiatry . 2009;17(4):254-267.
• Goyal M, Singh S, Sibinga EMS, et al. Meditation programs for psychological stress and well-being: a systematic review and meta-analysis. JAMA Internal Medicine. 2014;174(3):357-368.
• Institute of Medicine (US) Committee on Advancing Pain Research, Care, and Education. Relieving Pain in America: A Blueprint for Transforming Prevention, Care, Education, and Research . Washington, DC: National Academies Press; 2011. 
• Kabat-Zinn J, Massion AO, Kristeller J, et al. Effectiveness of a meditation-based stress reduction program in the treatment of anxiety disorders. American Journal of Psychiatry. 1992;149(7):936-943.
• Ludwig DS, Kabat-Zinn J. Mindfulness in medicine. JAMA. 2008;300(11):1350-1352.
• McKeering P, Hwang Y-S. A systematic review of mindfulness-based school interventions with early adolescents. Mindfulness . 2019;10:593-610.
• Muratori P, Conversano C, Levantini V, et al. Exploring the efficacy of a mindfulness program for boys with attention-deficit hyperactivity disorder and oppositional defiant disorder. Journal of Attention Disorders . 2021;25(11):1544-1553.
• Nahin RL, Rhee A, Stussman B. Use of complementary health approaches overall and for pain management by US adults. JAMA. 2024;331(7):613-615.
• Poissant H, Mendrek A, Talbot N, et al. Behavioral and cognitive impacts of mindfulness-based interventions on adults with attention-deficit hyperactivity disorder: a systematic review. Behavioural Neurology . 2019;2019:5682050.
• Skelly AC, Chou R, Dettori JR, et al. Noninvasive Nonpharmacological Treatment for Chronic Pain: A Systematic Review Update. Comparative Effectiveness Review no. 227. Rockville, MD: Agency for Healthcare Research and Quality; 2020. AHRQ publication no. 20-EHC009.
• Stieger JR, Engel S, Jiang H, et al. Mindfulness improves brain–computer interface performance by increasing control over neural activity in the alpha band. Cerebral Cortex . 2021;31(1):426-438.
• Teasdale JD, Segal ZV, Williams JMG, et al. Prevention of relapse/recurrence in major depression by mindfulness-based cognitive therapy. Journal of Consulting and Clinical Psychology . 2000;68(4):615-623.
• Weng HY, Lewis-Peacock JA, Hecht FM, et al. Focus on the breath: brain decoding reveals internal states of attention during meditation. Frontiers in Human Neuroscience . 2020;14:336.
• Yoshida K, Takeda K, Kasai T, et al. Focused attention meditation training modifies neural activity and attention: longitudinal EEG data in non-meditators. Social Cognitive and Affective Neuroscience . 2020;15(2):215-223.
• Yuan JP, Connolly CG, Henje E, et al. Gray matter changes in adolescents participating in a meditation training. Frontiers in Human Neuroscience . 2020;14:319.
• Zhang J, Díaz-Román A, Cortese S. Meditation-based therapies for attention-deficit/hyperactivity disorder in children, adolescents and adults: a systematic review and meta-analysis.  Evidence-Based Mental Health . 2018;21(3):87-94.

Acknowledgments

Thanks to Elizabeth Ginexi, Ph.D., Erin Burke Quinlan, Ph.D., and David Shurtleff, Ph.D., NCCIH, for their review of this 2022 publication.

This publication is not copyrighted and is in the public domain. Duplication is encouraged.

NCCIH has provided this material for your information. It is not intended to substitute for the medical expertise and advice of your health care provider(s). We encourage you to discuss any decisions about treatment or care with your health care provider. The mention of any product, service, or therapy is not an endorsement by NCCIH.

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