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University of Michigan

Master of Applied Data Science

University of Michigan via Coursera


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Learn from the #1 public research university in the U.S. and join the next generation of data scientists.

The University of Michigan School of Information’s online Master of Applied Data Science (MADS) degree is designed for aspiring data scientists to learn and apply skills through hands-on projects. You’ll learn how to use data to improve outcomes and achieve ambitious goals.

The MADS curriculum prepares you to be a leader in the field through mastery of core data science concepts like machine learning and natural language processing. By diving deep on key topics such as privacy, data ethics, and persuasive communication, you’ll be prepared to succeed within today’s organizations. You’ll also work with real data sets from top companies as you build a work portfolio that showcase your skills. Learn the systems and techniques that help organizations overcome data overload and make smart decisions.

Whether you’re looking to create real estate market forecasts or use data to study Russian literature, this online Master’s program teaches the skills you need for success in an ever-changing field.


The University of Michigan’s School of Information (#1 ranked in Information Systems, U.S. News & World Report 2018) has a long-established partnership with Coursera, and its faculty are experts at teaching online. More than one million learners have taken courses online from UMSI faculty since launching on Coursera.


The University of Michigan’s School of Information prepares students to be leaders in the field. Graduates from the on-campus Master of Science in Information program have a 98%+ job placement rate and go on to become data scientists at places like Google, Facebook, and Amazon.


There are only a few basic technical prerequisites (knowledge of statistics and Python) for the program. Students are not required to have a bachelor’s degree in a science or math discipline, or work experience in a technical field. Students who need a refresher on statistics and Python are encouraged to take the Statistics for Python and Python 3 Programming Specializations, which are also offered by the University of Michigan through Coursera.


While other data science Master’s programs focus on Computer Science theory, this online degree equips students for leadership with an end-to-end perspective on data science. Students are prepared to solve real-world problems through expertise on contextual inquiry, data visualization, and presentation.


University of Michigan professors are among the most respected and passionate in the field. Their expertise provides the highest quality of instruction to online degree learners. Students get direct access to faculty and graduate students through live office hours and email conversations.


The University of Michigan community provides a wide range of student services, including access to libraries and exclusive job listings. Students who complete online graduate degrees join the 540,000-strong Michigan alumni network, and are invited to participate in commencement ceremonies in Ann Arbor.



Courses cover:

  • Computational methods for big data
  • Exploring and communicating data
  • Visualizing data using various methods
  • Analytic techniques (machine learning, network analysis, natural language processing, experiments and causal inference)
  • Data science applications in context (search and recommender systems, social media analytics, learning analytics)
  • 3 portfolio-building major projects

Classes are flexible, and offered in one-credit, four-week course modules. The degree is designed to fit your life, even if you have a full-time job and family responsibilities.

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 intersects computation with theory and application, ensuring that students put their data science learnings into practice.

The following course clusters and titles highlights a breadth and depth of engaging data science subjects. Courses cover everything from problem formulation to putting results into action.

Python is the primary programming language used throughout this curriculum. Students will apply data science skills and knowledge in 3 capstone projects throughout the program.

Please note that course titles are subject to change as the curriculum is expanded and refined.

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

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

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

MADS students have the opportunity to start with these data science courses:

Introduction to Applied Data Science

This course explores expertise, perspectives, and ethical commitments data scientists apply to projects during four phases of data science: problem formulation, data acquisition, modeling and analysis, and presentation of results. Through this process, students will define a vision for how they want their data science careers to develop.

Data Manipulation

Data Manipulation presents manipulation and cleaning techniques using the popular Python Pandas data science library. By the end of this course, students will have the skills needed to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

Math Methods for Data Science

Math Methods will review and establish the foundational math concepts needed for a data scientist’s toolkit. Students will learn and apply concepts from linear algebra (such as matrices and vectors), basic optimization techniques (such as gradient descent), and statistics (such as Bayes’ rule).

Information Visualization I

Information Visualization I will focus is on the role of visualization in understanding one-dimensional and multidimensional data. It covers how perception, cognition, and good design can enhance visualizations. This course also introduces APIs for visualization construction.

Experiment Design and Analysis

Experiment Design and Analysis presents techniques for laboratory and field experiments. Students will discuss the logic of experimentation and the ways in which experimentation is used to investigate social and technological phenomena. Students will also learn ways to design experiments and analyze experimental data.

Visual Exploration of Data

Visual Exploration of Data enables students to identify aggregate patterns within data using the matplotlib library, and learn the challenges associated with exploring and representing data. Students will also improve their understanding of the applications of various statistical methods.

Data Mining I

Data Mining I introduces the basic concepts of data mining. This course covers how to represent real world information as basic data types (itemsets, matrices, and sequences) that facilitate downstream analytics tasks. Students will learn how to characterize each type of data through pattern extraction and similarity measures.


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