Dr. Natalia Summerville

MSA Curriculum

Curriculum HighlightsThe Master of Science in Analytics (MSA) is a novel curriculum aimed squarely at producing graduates with the multi-faceted skills needed to draw insights from complex data sets, and to be able to communicate those insights effectively. The brainchild of a former MIT professor, it is the product of a 3-year collaboration by an interdisciplinary group including mathematicians, computer scientists, statisticians, economists, geographers, operations researchers, and faculty with expertise in various fields of business and management.

The MSA is a single, fully-integrated course of study—not a menu of core and elective courses—taught exclusively to students in the program. It is highly interactive. Students work together in teams and receive personalized coaching to improve their productivity. It is an intensive 10-month learning experience designed to immerse students into the acquisition of practical knowledge and application of methods and techniques. The curriculum is carefully calibrated and continuously updated to meet the evolving challenges facing data scientists. The Institute houses classrooms, team rooms, study spaces, and other amenities under one roof, as well as the faculty and staff who are available to interact with students throughout the day.

MSA students hone their skills working on challenging problems with actual data shared from sponsoring organizations. The Practicum spans eight months and culminates with an executive-level report and presentation to the sponsor. Students work with leading industry-standard programming tools. Since the program’s inception, MSA students have engaged in a total 134 projects with over 100 sponsors spanning virtually every industry segment and include some of the world’s leading organizations and best known brands.

With a decade of experience and hundreds of graduates, the curriculum has a proven track record in producing superior student outcomes.

Master of Science in Analytics


AA500 – Analytics Tools and Techniques

  • Orientation and Introduction
  • Data Querying and Reporting
  • Data Access and Management
  • Data Cleaning
  • Statistical Programming Tools
  • Data Mining Overview
  • Geospatial Data Analytics
  • Relational Databases and Data Warehouses
  • Statistical Analysis of Databases
  • Data Visualization
  • Presentation Skills
  • Teamwork Skills
  • Problem Solving Skills

AA501 – Analytics Foundations

  • Linear Algebra Overview
  • Exploratory Data Analysis
  • Linear Regression
  • Multiple Linear Regression
  • Regression Diagnostics
  • Logistic Regression
  • Analysis of Tables
  • Statistics Assessments
  • Written and Computer Application


AA502 – Analytics Methods and Applications I

  • Linear Algebra
  • Data Mining
  • Machine Learning
  • Text Mining
  • Logistic Regression
  • Simulation and Finance
  • Optimization
  • Time Series and Forecasting
  • Advanced Programming
  • Customer Analytics

AA504 – Analytics Practicum I

  • Data Privacy and Security
  • Legal Issues
  • Data Visualization
  • Geospatial Data
  • Project Management
  • Teamwork and Conflict Resolution
  • Leadership/Followership
  • Consulting Skills
  • Problem-Solving
  • Communication Skills
  • Technical Writing


AA503 – Analytics Methods and Applications II

  • Advanced Modeling
  • Survival Analysis
  • Big Data
  • Design of Experiments
  • Risk Analytics
  • Financial Analytics
  • Web Analytics
  • Advanced Exploratory and Outlier Analysis
  • Advanced Data Mining
  • Special Topics

AA505 – Analytics Practicum II

  • Data Privacy and Security
  • Data Ethics
  • Project Management
  • Data Visualization
  • Teamwork Skills
  • Visual Communication of Data
  • Presentation Skills
  • Technical Writing