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Real-world case studies to jumpstart your career

Professional Certificate in
Data Science

What you will learn

  • Fundamental R programming skills
  • Statistical concepts such as probability, inference, and modeling and how to apply them in practice
  • Gain experience with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr
  • Become familiar with essential tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio
  • Implement machine learning algorithms
  • In-depth knowledge of fundamental data science concepts through motivating real-world case studies

The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. The HarvardX Data Science program prepares you with the necessary knowledge base and useful skills to tackle real-world data analysis challenges. The program covers concepts such as probability, inference, regression, and machine learning and helps you develop an essential skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with Unix/Linux, version control with git and GitHub, and reproducible document preparation with RStudio.

In each course, we use motivating case studies, ask specific questions, and learn by answering these through data analysis. Case studies include: Trends in World Health and Economics, US Crime Rates, The Financial Crisis of 2007-2008, Election Forecasting, Building a Baseball Team (inspired by Moneyball), and Movie Recommendation Systems.

Throughout the program, we will be using the R software environment. You will learn R, statistical concepts, and data analysis techniques simultaneously. We believe that you can better retain R knowledge when you learn how to solve a specific problem.

Expert instruction
9 skill-building courses
Progress at your own speed
1 year 5 months
2 - 3 hours per week
Discounted price: $1,332.90
Pre-discounted price: $1,481USD
For the full program experience

Courses in this program

  1. HarvardX's Data Science Professional Certificate

  2. 1–2 hours per week, for 8 weeks

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

  3. 1–2 hours per week, for 8 weeks

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

  4. 1–2 hours per week, for 8 weeks

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

  5. 1–2 hours per week, for 8 weeks

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

  6. 1–2 hours per week, for 8 weeks

    Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.

  7. 1–2 hours per week, for 8 weeks

    Learn to process and convert raw data into formats needed for analysis.

  8. 1–2 hours per week, for 8 weeks

    Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.

  9. 2–4 hours per week, for 8 weeks

    Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

  10. 15–20 hours per week, for 2 weeks

    Show what you've learned from the Professional Certificate Program in Data Science.

  11. This program was supported in part by NIH grant R25GM114818.

    HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.

    HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our research statement to learn more.

    Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact and/or report your experience through the edX contact form.

    • 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)

Meet your instructor
from Harvard University (HarvardX)

Rafael Irizarry
Professor of Biostatistics
Harvard University

Experts from HarvardX committed to teaching online learning

Enrolling Now

Discounted price: $1,332.90
Pre-discounted price: $1,481USD
9 courses in 1 year 5 months
Pursue the Program


  • We recommend that you take the courses in the order in which they appear on this site:
  • You are welcome to start with one course at a time until you finish them all.
  • Contact edX.
  • There are no prerequisites for the first course, but the later courses assume knowledge from the prior courses in the series.
  • In general, new course runs of all 9 courses launch in March and October.
  • No, you can complete the courses across multiple course runs.
  • No, course progress does not carry over across course runs.


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