Overview - All Tracks
The MS Analytics curriculum is structured to be completed in a single year (fall, spring, and summer), with a total of 36 credits required for each student. Trained by world-class faculty, students will learn identification and framing of problems; acquisition, management, and utilization of large and fast-moving streams of data; creation, analysis, solution, and interpretation of mathematical models using appropriate methodology; and the integration of these interdisciplinary skills to enable graduates to successfully develop and execute analytics projects.
The interdisciplinary core includes 15 credits of coursework across business, computing, statistics, and operations research. On top of this integrated breadth of study covering the core areas of analytics, each student has 15 credits of electives to satisfy one of the specialized tracks to give them depth in an analytics area of specialization: Analytical Tools, Business Analytics, and Computational Data Analytics. Each student's elective choices can be personalized to support their individual career goals. The final piece of the curriculum is an applied analytics practicum, in which students will work with companies and organizations on real analytics problems.
To see the specific list of topics covered in the interdisciplinary core and electives, see the Topics Covered page.
Base Curriculum - All Tracks
- MGT 6203 Data Analytics in Business (3 credits)
- CSE 6242 Data and Visual Analytics (3 credits)
- One operations research course (3 credits)
- Two statistics courses (6 credits)
- 5 Elective/core courses (15 credits; includes 3 introductory core courses in computing, business, and statistics/OR areas, each of which may be waived depending on individual student backgrounds; most students are expected to need 2, leaving 3 remaining elective course slots)
- 6 credits of applied analytics team practicum or internship
- Each student’s course choices must satisfy the requirements of at least one of the defined tracks (Analytical Tools, Business Analytics, Computational Data Analytics).
To see a list of all courses offered, see the Course Listing page.
Computational Data Analytics Track
The computational data analytics track allows students to build on the interdisciplinary core curriculum to gain a deeper understanding of the practice of dealing with so-called “big data”: how to acquire, preprocess, store, manage, analyze, and visualize data arriving at high volume, velocity, and variety.
The specific requirements of the computational data analytics track include:
CSE 6040 Computing for Data Analysis (can be replaced by an elective if you have sufficient background)
ISyE 6501 Introduction to Analytics Modeling (can be replaced by an elective if you have sufficient background)
MGT 8803 Introduction to Business for Analytics (can be replaced by an elective if you have sufficient background)
- MGT 6203 Data Analytics in Business
- At least three (3) computing courses beyond the introductory core, including CSE 6242 Data and Visual Analytics (can also include CSE/ISyE Computational Data Analysis (Machine Learning), which must be taken as a computing elective or as a statistics elective)
- Two statistics courses (can include CSE/ISyE 6740 Computational Data Analysis (Machine Learning)) and one operations research course
- CSE/ISyE/MGT 6748 Applied Analytics Practicum (can include approved applied analytics internship)