Master of Science in Analytics, Students earn a degree in data science, a data science masters

Master of Science in Analytics

Curriculum details for MSc Analytics

Students have the flexibility to pursue the Master of Science in Analytics degree on a part- or full-time schedule. Part-time students enroll in one or two courses each quarter and take their courses in the evenings or on Saturdays. Full-time students take three courses per quarter. Some of their courses may be offered during the day. All courses are taught at the NBC Tower in downtown Chicago.

Students earn the Master of Science in Analytics by successfully completing up to fourteen courses and a final capstone project. The Master of Science in Analytics curriculum consists of the following:

  • One Statistics Bootcamp
  • Two Foundational Courses
  • Nine Core Courses
  • Two Electives
  • Capstone project

How long will it take to earn a Master of Science in Analytics degree?

Typically, full-time students can complete the Master of Science in Analytics program in one and a half years. Students may apply to start in either the Spring or Autumn quarters.

What courses are offered in the Master of Science in Analytics program?

Our program builds a basis in analytics theory that will be applied in advanced analytics classes that span several analytics disciplines and specialities.

Foundation Courses

Foundation courses provide the basis for our rigorous analytics degree that will support the theoretical, strategic, and practical analytics studies in more advanced courses. Students with sufficient preparation may be eligible to bypass some foundation courses. Statistics Bootcamp begins one month prior to a student’s start quarter.

Introduction to Statistical Concepts: Statistics Bootcamp Linear Algebra and Matrix Analysis
Programming for Analytics  

Core Courses

Core courses allow students to build their theoretical analytics knowledge and practice applying this theory to examine business problems. The required Leadership Skills core course teaches how to make the ties between data analysis learnings and business objectives. Each of the nine core courses is required to earn the Master of Science in Analytics.

Statistical Analysis Leadership Skills: Teams, Strategies, and Communications
Linear and Nonlinear Models for Business Applications Data Mining Principles
Time Series Analysis and Forecasting Machine Learning and Predictive Analytics
Data Engineering Platforms for Analytics  Big Data Platforms Research Design for Business Applications
Research Design for Business Applications  

Electives

Explore advanced analytics strategies and applications. Students are required to complete two electives. Our program continually adds electives to evolve with the analytics landscape. Alumni are able to take classes, when available, at reduced tuition.

Financial Analytics Credit and Insurance Risk Analytics
Marketing Analytics Digital Marketing Analytics in Theory and Practice
Health Analytics Optimization and Simulation Methods for Analytics
Real Time Analytics Data Visualization Techniques
Real Time Intelligent Systems Bayesian Methods 
Reinforcement Learning and Advanced Optimization Advanced Machine Learning and Artificial Intelligence
Natural Language Processing and Cognitive Computing  

Linear Algebra and Matrix Analysis is available as an elective to students who were not required to take it as a Foundation Course.

Capstone Project

The required capstone project is completed over three quarters. The capstone courses start with the core course, Research Design for Business Applications. Part-time students generally start their capstone project three to four quarters before their projected graduation. Full-time students start their capstone project in their third quarter.

Capstone Project Implementation Capstone Project Writing
More about Capstone Projects ›  

Non-Credit Workshops and Short Courses

Short courses and workshops are offered to support student success in the relevant concurrent courses and electives.

Ethics in Big Data Analytics Hadoop Workshop
Linux Workshop Python Workshop
R Workshop Deep Learning and Image Recognition
Python for Analytics