The Master of Applied Data Science (MADS) curriculum teaches data science in action. 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 roles across industries.

Courses fall into themed clusters that mirror the data science pipeline — from formulating the right questions to communicating results and applying your skills in specific contexts like health care, learning and sports. 

Course structure

Most courses are delivered in a 1-month, 1-credit format. Students 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. 

Total credits: 38

Unless otherwise noted, each course is 1-credit (roughly 4 weeks in length). Python is the primary programming language used throughout this curriculum. Visit our course catalog to see course descriptions.

Fundamentals

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

Formulating problems 

• Introduction to Applied Data Science
• Contextual Inquiry
• Data Science Ethics
• Data Science for Social Good

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
• Math Methods for Data Science II
• 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
• Reinforcement Learning Algorithms
• Cloud Computing for Data Science
• Health Analytics
• Sports Analytics

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

Concentrations

MADS students have the option to pursue a concentration in a high-demand area by completing a themed set of electives. Choosing a concentration can help you tailor your learning to specific career goals and gain a competitive edge in the job market. 

To pursue a concentration, you simply take the relevant electives and submit an intent form at any point before you graduate. Successful completion of a concentration is noted on your unofficial transcript.

Artificial Intelligence (AI)

This concentration emphasizes advanced techniques in AI and machine learning. Electives explore emerging tools and topics that go beyond core coursework in the MADS program.

Choose four of the following electives:

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

Data Analytics

This concentration focuses on exploratory data analysis, analytics workflows and platforms, data science communication, and advanced visualization. Designed for students aiming to lead in analytics-heavy roles across industries, it significantly expands the scope of techniques covered in required courses.

Choose four of the following electives:

• Presenting Uncertainty
• Business SQL
• Information Visualization II
• Cloud Computing