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Data Science for Health Research

Philip S. Boonstra and Bhramar Mukherjee

In Data Science for Health Research, 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.

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What's inside

Three courses

Arranging and Visualizing Data in R

(4 hours)

This course introduces the R statistical environment. Learners will navigate R and RStudio, read data, and prepare it for analysis. Concepts covered include sorting, grouping, summarizing, pivoting, and creating new variables. Learners will also visualize their data and set up a project workflow. Concepts are covered through lectures, guided coding practice, and independent practice.

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Linear Regression Modeling for Health Data

This course introduces learners to statistical modeling, including the t-test and linear regression. It covers fitting and interpreting regression models for continuous outcomes with multiple predictors.

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Logistic Regression and Prediction for Health Data

(3 hours)

This course introduces learners to analyzing binary outcomes. They will learn about tests for two-group comparisons, statistical inference, and prediction using logistic regression. By the end, learners will understand how binary outcomes arise, how to compare proportions between two groups in R, fit logistic regressions, make predictions, and assess their quality.

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Time

420 hours

Level

Intermediate

Via

Coursera

Institution

University of Michigan

Instructors

Philip S. Boonstra

Bhramar Mukherjee

Level

Intermediate

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