Data Science for Health Research, Short Course | Part time online | Coursera | United States
3 months
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Wrangle, Visualize and Analyze Health Data. Import, process data and fit basic statistical models to analyze health outcome data, all in the R statistical environment with this Data Science for Health Research course offered by Coursera in partnership with University of Michigan.

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In Data Science for Health Research course offered by Coursera in partnership with University of Michigan, learn to organize and visualize health data using statistical analysis in programs like R. Explore how to translate data, interpret statistical models, and predict outcomes to help make data-informed decisions within the public health field.  

Applied Learning Project

In course 1, learners will summarize data from a global survey on financial account ownership (Global Findex Database) and recreate a table and a figure from the 2017 Global Findex Database report. In courses 2 and 3, learners will analyze synthetic data relating common risk factors and cardiovascular disease in an Indian population.

The pattern of learning for this course will generally proceed through the following steps:

  • Learning about ideas through lecture-style videos.
  • Implementing those ideas together in a guided practice video, where the instructor demonstrates the use of specific functions in R, and learners can repeat the steps that the instructor demonstrates.
  • Repeating Steps 1 and 2 for most topics in a lesson.
  • Reinforcing on your own by following along a series of written steps called ‘independent guides’, which cover all topics in a lesson.
  • Practicing your understanding of these ideas through non-graded quizzes and non-graded discussion prompts.

Skills You Will Gain:

  • Become knowledgeable about and conversant in the R environment
  • Compare the prevalence of a binary outcome across two groups
  • Implement and interpret two-sample comparison of means
  • Fit and summarize linear regression with multiple predictors
  • Fit and apply logistic regression
  • Develop a workflow in R
  • Format and manipulate data within R into suitable formats
  • Develop an intuition for doing exploratory data analysis
  • Conceptualize statistical models

Programme Structure

Courses include:

  • Arranging and Visualizing Data in R
  • Linear Regression Modeling for Health Data
  • Logistic Regression and Prediction for Health Data

Key information


  • Part-time
    • 3 months
    • Flexible

Start dates & application deadlines

You can apply for and start this programme anytime.





Academic requirements

We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.

English requirements

We are not aware of any English requirements for this programme.

Other requirements

General requirements

Intermediate level

  • There are no formal requirements to take this specialization.  Course 1 is primarily for those who have no previous experience working with R.

Tuition Fee

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  • International

    Tuition Fee
    Based on the tuition of 0 USD for the full programme during 3 months.
  • National

    Tuition Fee
    Based on the tuition of 0 USD for the full programme during 3 months.

You can choose from hundreds of free courses, or get a degree or certificate at a breakthrough price. You can now select Coursera Plus, an annual subscription that provides unlimited access.


Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project.

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