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.
Data Science for Health Research Specialization
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
Instructors: Bhramar Mukherjee
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Get in-depth knowledge of a subject
(10 reviews)
Beginner level
Recommended experience
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
(10 reviews)
Beginner level
Recommended experience
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
learn to organize and visualize health data using statistical analysis in programs like R
Overview
Skills you'll gain
Tools you'll learn
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
29 practice exercises
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from University of Michigan
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
R Programming, Data Wrangling, Ggplot2, Data Manipulation, Tidyverse (R Package), Scatter Plots, Histogram, Data Import/Export, R (Software), Exploratory Data Analysis, Statistical Visualization, Data Visualization, Rmarkdown, Scripting, Data Visualization Software, and Package and Software Management
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
Statistical Modeling, Regression Analysis, Statistical Hypothesis Testing, Statistical Inference, Predictive Modeling, Data Analysis, Correlation Analysis, Statistical Analysis, Probability & Statistics, and R Programming
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
Regression Analysis, Statistical Hypothesis Testing, Statistical Inference, Statistical Analysis, Biostatistics, Statistical Methods, Probability & Statistics, Statistical Modeling, Risk Analysis, R Programming, Predictive Analytics, Classification And Regression Tree (CART), Data Analysis, Statistics, and Descriptive Statistics
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Frequently asked questions
There are three courses in this Specialization. Course 1 is four weeks long, Course 2 and Course 3 are each three weeks long. In total, this specialization has 10 weeks of learning.
There are no formal requirements to take this specialization. It is expected that learners understand data important for public health, have a basic understanding of algebra and probability but they do not need to have any formal statistical training or credentials. Course 1 is primarily directed at those who have no previous experience working with R.
Each course can be completed as a standalone or as part of the three-course specialization. Course 1 is primarily directed at those who have no previous experience working with R. Although it is not required, we encourage you to take all three courses in this specialization.
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Financial aid available,