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University of Michigan School of Information

Master of Applied Data Science curriculum

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The Master of Applied Data Science (MADS) curriculum teaches data science in action, including the AI and machine learning techniques transforming every industry. You’ll build a foundation in data collection, computation and analytics — then apply those skills through real-world projects that challenge you to communicate insights and present solutions. You’ll graduate with a portfolio that showcases your readiness to lead in data science and AI roles across industries.

Course structure

The MADS degree consists of 38 credits, including 34 credits of required coursework and 4 credits of elective coursework.

Courses are delivered in a 1-month, 1-credit format, unless otherwise noted in the list below. You will typically take 1-3 courses per month. This modular structure gives you momentum and flexibility as you progress through the program, whether you’re studying full time or balancing the program with work. 

Python is the primary programming language used throughout this curriculum. Visit our course catalog to see course descriptions.

Required courses

Fundamentals

This course is designed to ensure all students have mastery of the fundamental programming and debugging skills used throughout the program. Students who enter the program with advanced placement are exempt from this requirement.

  • Data-Oriented Python Programming and Debugging — 4 credits, 16 weeks

Formulating problems 

  • Being a Data Scientist
  • Qualitative Inquiry for Data Scientists
  • Data Science Ethics

Collecting and processing data 

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

Analyzing and modeling data

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

Presenting and integrating results into action

  • Information Visualization I
  • Communicating Data Science Results

Data science applications (choose one) 

  • Data Science for Social Good
  • Health Analytics
  • Learning Analytics
  • Search and Recommender Systems
  • Sports Analytics

Project-based courses 

Milestone and capstone courses are designed to help you integrate and apply what you’ve learned throughout the MADS program. Taken at the one-third, two-thirds and final stages of the degree, these courses give you the chance to collaborate in teams, work with faculty mentors and, in some cases, explore projects connected to your own interests.

  • Milestone I (2 credits, 8 weeks): synthesis of computational techniques to collect and process big data
  • Milestone II (2 credits, 8 weeks): synthesis of analytics and machine learning techniques to analyze data and present results
  • Capstone (3 credits, 12 weeks): culminating project that applies end-to-end data science techniques — including AI and ML methods — to real-world scenarios

Electives

Choose four of the following electives: 

  • Presenting Uncertainty
  • Business SQL
  • Generative AI in Business and Society
  • Database Architectures and Technologies
  • Information Visualization II
  • Reinforcement Learning Algorithms
  • Deep Learning II
  • Applications of Generative AI
  • Cloud Computing for Data Science
  • Learning Analytics
  • Health Analytics
  • Search and Recommender Systems
  • Sports Analytics
  • Data Science for Social Good
  • Independent Study (instructor permission required)

Concentrations

You have the option to focus your elective coursework in Artificial Intelligence or Data Analytics to earn a concentration in one of these areas, which will be noted on your unofficial transcript. Choosing a concentration can help you tailor your learning to specific career goals and gain a competitive edge in the job market. 

Artificial Intelligence (AI)

Develop advanced expertise in AI and machine learning, including generative AI, reinforcement learning and advanced deep learning. You’ll work with the tools and techniques currently shaping industry practice. 

Choose four of the following electives:

  • Generative AI in Business and Society
  • Reinforcement Learning Algorithms
  • Deep Learning II
  • Applications of Generative AI
  • Health Analytics

Data Analytics

Build the in-depth skills required to lead in analytics roles across industries, including exploratory data analysis, analytics workflows and platforms, data science communication and advanced visualization.

Choose the following four electives:

  • Presenting Uncertainty
  • Business SQL
  • Information Visualization II
  • Cloud Computing for Data Science