Skip to main content

University of Michigan School of Information

Menu

Curriculum

Students enrolled in the University of Michigan School of Information’s Master of Applied Data Science (MADS) program will take courses in all essential subjects of applied data science with an emphasis on an end-to-end approach. The MADS program resides at the intersection of computation, theory and application, ensuring that students are putting their data science learnings into practice and are being prepared for data science careers in any industry. 

The following list of course clusters and titles highlights the breadth and depth of the data science subject matter with which students can engage. Courses are grouped by relevant themes and demonstrate how the program works from one end of data science (problem formulation) to the other (presenting and putting results into action).

For sample descriptions of the courses offered this fall, please see our MADS courses page.

Python is the primary programming language used throughout this curriculum. Students will also apply their data science skills and knowledge in capstone projects along the way and at the end of the program. These tangible, hands-on projects will greatly benefit students in practicing end-to-end data science with real-world data and in real-world scenarios. The projects will also be collected in a portfolio students may share with others. 

Please note that these course titles are subject to change as we continue to build out and refine our curriculum.

Unless otherwise noted, each course is a 1-credit unit (roughly 4 weeks) in length. A total of 34 credit units is required to complete the degree. 

Formulating problems 

  • Introduction to Applied Data Science
  • Contextual Inquiry
  • Data Science Ethics

Collecting and processing data 

  • SQL & Databases
  • SQL Architectures & Technologies
  • Big Data: Efficient Data Processing
  • Big Data: Scalable Data Processing
  • Data Manipulation
  • Experiment Design and Analysis

Analyzing and modeling data

  • Math Methods for Data Science
  • Visual Exploration of Data
  • Data Mining I
  • Data Mining II
  • Supervised Learning
  • Unsupervised Learning
  • Deep Learning
  • Machine Learning Pipelines
  • Causal Inference
  • Natural Language Processing
  • Network Analysis

Presenting and integrating results into action

  • Information Visualization I
  • Presenting Uncertainty
  • Communicating Data Science Results 
  • Information Visualization II

Real world applications of data science

  • Search and Recommender Systems
  • Social Media Analytics
  • Learning Analytics
  • More to come

Culminating learning experiences

  • Project I: synthesis of computational techniques to collect and process big data
  • Project II: synthesis of analytics and machine learning techniques to analyze data and present results
  • Project III: capstone that applies end-to-end data science techniques to real world scenarios

Sign up for our interest list and receive:

  • Invitations to exclusive information sessions and events 
  • The opportunity to chat with current students 
  • More in-depth information about the MADS program 
  • Tips and advice for creating a successful application 
  • Reminders for important deadlines