Coursera

Logistic Regression and Prediction for Health Data (Coursera)

Offered by University of Michigan,
Logistic Regression and Prediction for Health Data (Coursera)

This course introduces learners to the analysis of binary/dichotomous outcomes. Learners will become familiar with fundamental tests for two-group comparisons and statistical inference plus prediction more broadly using logistic regression. They will understand the connection between prevalence, risk ratios, and odds ratios. By the end of this course, learners will be able to understand how binary outcomes arise, how to use R to compare proportions between two groups, how to fit logistic regressions in R, how to make predictions using logistic regression, and how to assess the quality of these predictions.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

All concepts taught in this course will be covered with multiple modalities: slide-based lectures, guided coding practice with the instructor, and independent but structured exercises.
This course is part of the Data Science for Health Research Specialization.

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

Syllabus

Simple Comparisons of Binary Outcomes
Module 1
This module introduces you to binary outcomes, including how they arise, how to calculate proportions, and how to compare proportions between two groups.

Introducing Logistic Regression
Module 2
In this module, you will be introduced to the ubiquitous logistic regression, one of the most common tools for measuring the association between one or more predictors and a binary outcome.

Assessing the Predictive Accuracy of Logistic Regression Models
Module 3
This module introduces you to tools for assessing the quality of a fitted logistic regression model.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Regression Models (Coursera) Coursera
Johns Hopkins University

Regression Models (Coursera)

Statistics & Data Analysis Data Science

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models.

Aug 24th 2026
4 Weeks
Statistics Regression Linear Regression
Details
Foundations of strategic business analytics (Coursera) Coursera
ESSEC Business School

Foundations of strategic business analytics (Coursera)

Management & Leadership Business

Who is this course for? This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role. You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering.

Aug 31st 2026
4 Weeks
Data Analysis Analytics Presentations
Details
Introduction to Machine Learning in Sports Analytics (Coursera) Coursera
University of Michigan

Introduction to Machine Learning in Sports Analytics (Coursera)

Statistics & Data Analysis Data Science

In this course students will explore supervised machine learning techniques using the python scikit learn (sklearn) toolkit and real-world athletic data to understand both machine learning algorithms and how to predict athletic outcomes. Building on the previous courses in the specialization, students will apply methods such as support vector machines (SVM), decision trees, random forest, linear and logistic regression, and ensembles of learners to examine data from professional sports leagues such as the NHL and MLB as well as wearable devices such as the Apple Watch and inertial measurement units (IMUs).

Aug 24th 2026
4 Weeks
Machine Learning Linear Regression Logistic Regression
Details
Introduction to Machine Learning (Coursera) Coursera
Duke University

Introduction to Machine Learning (Coursera)

Data Science

This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction.

Aug 24th 2026
5-12 Weeks
ML NLP Machine Learning
Details
Prediction and Control with Function Approximation (Coursera) Coursera
University of Alberta,Alberta Machine Intelligence Institute

Prediction and Control with Function Approximation (Coursera)

Data Science

In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward.

Aug 24th 2026
4 Weeks
Artificial Intelligence Machine Learning Prediction
Details
Statistical Learning (Coursera) Coursera
Illinois Tech

Statistical Learning (Coursera)

Statistics & Data Analysis Data Science

This course offers a deep dive into the world of statistical analysis, equipping learners with cutting-edge techniques to understand and interpret data effectively. We explore a range of methodologies, from regression and classification to advanced approaches like kernel methods and support vector machines, all designed to enhance your data analysis skills.

Sep 14th 2026
5-12 Weeks
Classification Linear Regression Logistic Regression
Details
Managing Data Analysis (Coursera) Coursera
Johns Hopkins University

Managing Data Analysis (Coursera)

Management & Leadership Statistics & Data Analysis

This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results.

Aug 24th 2026
1 Week
Strategy Communication Inference
Details
Raising Capital: Credit Tech, Coin Offerings, and Crowdfunding (Coursera) Coursera
University of Michigan

Raising Capital: Credit Tech, Coin Offerings, and Crowdfunding (Coursera)

Economics & Finance Business

Consider the benefits and challenges of disruptive capital-raising technology. Advances in technology have both systematized and democratized consumers’ and business’ access to capital. In this course, you will explore the ways in which technology has transformed access to consumer credit and access to seed capital for business projects.

Aug 24th 2026
4 Weeks
Capital Logistic Regression Cryptocurrency
Details
Machine Learning: Regression (Coursera) Coursera
University of Washington

Machine Learning: Regression (Coursera)

Statistics & Data Analysis Data Science

Case Study - Predicting Housing Prices. In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.

Aug 24th 2026
5-12 Weeks
Python Algorithms Machine Learning
Details