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

Access provided by New York State Department of Labor

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
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

Details to know

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Taught in English

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  • Develop a deep understanding of key concepts
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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 Manipulation
Category: Histogram
Category: Tidyverse (R Package)
Category: Data Wrangling
Category: Ggplot2
Category: Scatter Plots
Category: Data Transformation
Category: Rmarkdown
Category: Exploratory Data Analysis
Category: Statistical Visualization
Category: Software Installation
Category: Data Visualization Software
Category: Data Import/Export
Category: Package and Software Management
Category: Scripting

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

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: Statistical Hypothesis Testing
Category: Biostatistics
Category: Predictive Modeling
Category: Descriptive Statistics
Category: Predictive Analytics
Category: Data Validation
Category: Statistical Modeling
Category: Statistical Methods
Category: Health Care
Category: Statistics
Category: Exploratory Data Analysis
Category: Applied Machine Learning
Category: Probability & Statistics
Category: Statistical Analysis
Category: R Programming

Instructors

Bhramar Mukherjee
University of Michigan
2 Courses1,112 learners

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