Master of Applied Data Science 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.
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 1 credit unit (roughly 4 weeks) in length. A total of 34 credit units is required to complete the degree.
● 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
When the program launches in Fall 2019 MADS students will have the opportunity to take several critical data science courses. These brief descriptions provide a snapshot of the classes students may enroll in and the subject matter they will learn.
Introduction to Applied Data Science
This course explores what expertise, perspectives, and ethical commitments applied data scientists bring to projects. We will apply that lens to four phases of data science projects: problem formulation; data acquisition; modeling and analysis; and presentation of results. Through this process, students will formulate a statement of who they want to be as data scientists.
Data Manipulation presents manipulation and cleaning techniques using the popular Python Pandas data science library. By the end of this course, students will be able 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 math concepts that are foundational 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) in this course.
Information Visualization I
Information Visualization I will initiate information visualization — visual representations of data through interactive systems. Specific focus is on the role of visualization in understanding data, multidimensional data, and how perception, cognition, and good design enhance visualization. This course introduces APIs for visualization construction.
Experiment Design and Analysis
Experiment Design and Analysis presents experiment design for laboratory and field experiments. Students will discuss the logic of experimentation and the ways in which experimentation has been — and could be — used to investigate social and technological phenomena. Students will learn how to design experiments and analyze experimental data.
Visual Exploration of Data
Visual Exploration of Data teaches students how to look for (visually) aggregate patterns within data using the matplotlib library. Students will learn the challenges in visually exploring and representing analytic data with a focus on understanding how statistical measures can be applied.
Data Mining I
Data Mining I introduces basic concepts and tasks of data mining. This course focuses on how to formally 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.