MS Analytics

General Information

The MS Analytics curriculum is structured to be completed in 1.5 years (fall, spring, summer, and fall), with a total of 36 credits required for each student. Students may take four courses in Fall, four courses in Spring, practicum or 2 courses in Summer, and 2 courses or practicum in the Fall.

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Curriculum

The interdisciplinary core includes 15 credits of coursework (equivalent to five three-credit courses) 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. From Fall 2021, Analytical Tools Track will be provided in GT-Shenzhen. The other two tracks may be adopted to GT-Shenzhen in the future.

Base Curriculum – For the Analytical Tools Track only

• CSE 6040 Computing for Data Analytics (3 credits)
• CSE 6242 Data and Visual Analytics (3 credits)
• ISYE 6501 Introduction to Analytics Modeling (3 credits)
• MGT 8803 Introduction to Business for Analytics (3 credits)
• MGT 6203 Data Analytics in Business (3 credits)
• Five additional courses (15 credits) in machine learning, statistics, and operations research
• ISYE 6748 Applied Analytics Practicum (6 credits, including an internship or team project). Students need to finish eight courses including CSE 6242 and MGT 6203 before taking this course.
• The two CSE courses and the two MGT courses are likely to be offered online.

A subset of these courses are offered in the Shenzhen campus. Some of these courses may have an online version, whose availability in the Shenzhen campus will be reviewed later.

Analytical Tools Track

As mentioned earlier, the Shenzhen campus will start with offering this track in Fall 2021. Other tracks will be considered in the future.

The Analytical Tools track provides students with a greater knowledge and understanding of the quantitative methodology of descriptive, predictive, and prescriptive analytics: how to select, build, solve, and analyze models using methodology such as parametric and non-parametric statistics, regression, forecasting, data mining, machine learning, optimization, stochastics, and simulation.