Data Science for Health Research

Overview

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