Analytics and Modeling Specialization
Core and elective courses
Analytics and Modeling
Data science has a rich history at Northwestern.
When the data science program began in fall 2011, it was called the Master of Science in Predictive Analytics (MSPA) program. The program was heavily influenced by applied statistics with applications in business and economics. The Analytics and Modeling specialization builds on courses that were part of the MSPA program.
Supervised Learning Methods
Regression and classification
Unsupervised Learning Methods
Learn about the structure of data (dimension reduction) and what goes with what (cluster analysis)
Time Series Analysis and Forecasting
Detect trends from the past and use those trends to predict the future
Marketing Data Science
Product positioning, consumer preferences, segmentation and targeting
Financial Machine Learning
Analyze financial instruments and markets
Web and Network Data Science
Graphs (nodes/vertices and links/edges) and their many applications, beginning with social network analysis
Applied Probability and Simulation Modeling
Advanced modeling techniques
Research Design for Data Science
Data science relies on the scientific method
Data Visualization
Seeing trends and patterns in data
Sports Performance Analytics
Focus on individual athletes, sports science, and performance measurement
Sports Management Analytics
Focus on teams, game-day strategy, and the business of sports
Special Topics: SAS for Data Scientists
Focus on teams, game-day strategy, and the business of sports