University of Michigan
Data Science for Health Research Specialization
University of Michigan

Data Science for Health Research Specialization

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

Bhramar Mukherjee
Philip S. Boonstra

Instructors: Bhramar Mukherjee

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5.0

(9 reviews)

Intermediate level

Recommended experience

3 months
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
5.0

(9 reviews)

Intermediate level

Recommended experience

3 months
at 10 hours a week
Flexible schedule
Learn at your own pace

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Taught in English

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Specialization - 3 course series

What you'll learn

  • Become knowledgeable about and conversant in the R environment

  • Format and manipulate data within R into suitable formats

  • Develop an intuition for doing exploratory data analysis

  • Develop a workflow in R

Skills you'll gain

Category: R Programming
Category: Data Wrangling
Category: Data Manipulation
Category: Ggplot2
Category: Tidyverse (R Package)
Category: Histogram
Category: Scatter Plots
Category: Rmarkdown
Category: Scripting
Category: Exploratory Data Analysis
Category: Data Visualization Software
Category: Package and Software Management
Category: Software Installation
Category: Statistical Reporting
Category: Data Import/Export
Category: Statistical Visualization

What you'll learn

  • Become knowledgeable about the concept of statistical modeling and the basics of statistical inference

  • Recognize, fit, and interpret a simple linear regression model

  • Develop intuition to fit and interpret a multiple regression model

Skills you'll gain

Category: Regression Analysis
Category: Statistical Modeling
Category: Statistical Hypothesis Testing
Category: Statistical Analysis
Category: Statistical Inference
Category: Correlation Analysis
Category: Data Analysis
Category: Predictive Modeling
Category: Statistical Methods
Category: Probability & Statistics

What you'll learn

  • Understand how binary outcomes arise and know the difference between prevalence, risk ratios, and odds ratios

  • Use logistic regression to estimate and interpret the association between one or more predictors and a binary outcome

  • Understand the principles for using logistic regression to make predictions and assessing the quality of those predictions

Skills you'll gain

Category: Regression Analysis
Category: Statistical Inference
Category: Predictive Modeling
Category: Biostatistics
Category: Statistical Hypothesis Testing
Category: Classification And Regression Tree (CART)
Category: Statistical Methods
Category: Statistical Analysis
Category: R Programming
Category: Predictive Analytics
Category: Health Care
Category: Exploratory Data Analysis
Category: Probability & Statistics
Category: Statistical Modeling
Category: Descriptive Statistics
Category: Applied Machine Learning
Category: Statistics
Category: Statistical Machine Learning

Instructors

Bhramar Mukherjee
University of Michigan
2 Courses1,131 learners

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