Master's in Business Data Science and Analytics Curriculum

Master’s in Business Data Science and Analytics Curriculum

Building business intelligence in a data-driven world.

The M.S. in Business Data Science and Analytics program focuses on three core areas:

Training our students to be leaders in data management process and business analytics approaches
Providing our students with experiential project management opportunities using live data sets for analysis and application
Developing students’ expertise in key data management areas such as:
- Data mining
- Marketing technology, website analytics and social network analytics
- Applied statistical approaches
- How to interpret and communicate data analysis

The business analytics courses listed are delivered as a full-time, fixed curriculum, cohort-based program. S

The program will register students for all their data and business analytics courses. Please see the academic calendar for specific dates and deadlines on MSU scheduling.

Students must maintain a cumulative grade-point average of 3.0 or higher in all graduate courses. For more information, please refer to the handbook:

Master’s BDSA Student Handbook

Sample Resume Template

Current Curriculum Requirements may be updated/changed during the curriculum review process.

ITM 881 Network Analytics

3 Credits

This course explores the application of network analysis in business contexts. Focus is placed on establishing the basic methods and terminology associated with network analysis and text analytics and then progresses into broad-based applications. Applications of these techniques span a broad range of business contexts, including human resource management, CRM systems, supplier networks and online networks.

View full course description at the Office of the Registrar.
CSE 801B Data Mining
ITM 885 Machine Learning and Optimization in Analytics

3 Credits

Concepts and application in decision making, (stochastic) gradient descent, supervised learning (such as penalized regression, classification, survival analysis), deep neural networks for classification, optimization in Python and R, advanced topics in machine learning (such as generative models, adversarial learning, meta learning).

View full course description at the Office of the Registrar.
ITM 888 Capstone Business Analytics Corporate Project

1 Credit

Corporate practicum in the development and delivery of predictive data analysis for strategic decision making in organizations. Application of the principles and tools of analytics to real-world problems in research & development, marketing, supply chain, accounting, finance and human resources management. Development and presentation of analytical insights and recommendations.

View full course description at the Office of the Registrar.