Apply to MADS by the final deadline on May 15th - Try a Course Today
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.
Data Science Ethics
Collecting and Processing Data
Experiment Design and Analysis
Analyzing and Modeling Data
Presenting and Integrating Results into Action
Information Visualization II
Real world applications of data science
More to come
Culminating Learning Experiences
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 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.
The courses in the program represent 1-3 credits each. Courses take 1-2 months to complete. Courses are offered in the fall, winter and spring/summer semesters, and are taught by core University of Michigan School of Information faculty.
This flexible online curriculum will be offered in one credit, four-week course modules, so students can take as little as one credit per month and as many as three credits per month. The program is well suited for students seeking to study on a flexible schedule. For example, full-time students may complete the program in one year (12 months)*.
Part-time students may complete the program in two years (24 months) or three years (36 months).
The three capstone courses award 2-3 credits, and may require more than one month to complete. Starting with the Fall 2020 cohort, students will be able to take a full-time load, which is equivalent to 16 credits. 34 total credits are needed to graduate.
Earn your degree in 12-36 months, completely online, on your schedule. Lectures and quizzes are available on-demand, and professors and teaching assistants will be accessible through online office hours and discussion boards. Access classes from your chosen mobile device. Download lectures to work offline without earning your data plan.
Students can access course materials from anywhere with the mobile app, which is used by more than 80 percent of degree students on Coursera. The app is available on iOS and Android.
Using the mobile app, you can:
Applications for the Fall 2020 cohort are now open.
The first priority application deadline is March 15th.
The second priority application deadline is April 1st.
The final application deadline is May 15th.
Coursera does not grant credit, and does not represent that any institution other than the degree granting institution will recognize the credit or credential awarded by the institution; the decision to grant, accept, or transfer credit is subject to the sole and absolute discretion of an educational institution.
We encourage you to investigate whether this degree meets your academic and/or professional needs before applying.