The Master of Science in Analytics at Georgia Tech
The Master of Science in Analytics is an interdisciplinary degree program that leverages the strengths of Georgia Tech in statistics, operations research, computing, and business by combining the world-class expertise of the Scheller College of Business, the College of Computing, and the College of Engineering. By blending the strengths of these nationally ranked programs, graduates will learn to integrate skills in a unique and interdisciplinary way that yields deep insights into analytics problems.
Georgia Tech offers two options for students seeking a Master’s in Analytics. The on-campus Master of Science in Analytics program can be completed in one year, and includes “perqs” such as dedicated job placement assistance; on-campus analytics job fairs, and a conference travel budget. The Online Master of Science in Analytics program provides the option to complete the degree remotely in one to two years with the same access to the expertise of Georgia Tech faculty. For more information on the online program, please progress to the OMS in Analytics website.
Why an Interdisciplinary Master’s in Analytics?
Analytics is an important, fast-growing field that has quickly become a key facet of business strategy. There is an increasing need for analytics-savvy employees who can think uniquely across disciplines to transform data into relevant insights for making better business decisions.
Georgia Tech's interdisciplinary approach to analytics gives students the opportunity to learn directly from top international authorities on business intelligence, developers of cutting-edge analytics techniques in statistics and operations research, and world leaders in big data and high-performance computing. Students will use advanced resources across campus such as Georgia Tech's state-of-the-art high-performance computing infrastructure for massive-scale data analytics, work in cross-disciplinary teams to solve real analytics problems for a range of companies and organizations, and more. It all adds up to a unique ability to generate deeper insights into analytics problems.
With the Georgia Tech Master’s in Analytics degree, graduates will enter the workplace with the computing, business, statistics, and operations research skills needed to immediately identify, analyze, and solve analytics problems for better business intelligence and decision support.
Networking and Career Placement
One of the central objectives of the program will be to produce and place graduates ready to make both immediate and long-term impacts in business, industry, and government. In addition to making contacts with leading analytics organizations during the course of the program, students will be funded to attend a major analytics conference, gain valuable exposure at Georgia Tech's Big Data Industry Forum, and be supported in their job search by a dedicated professional.
The curriculum will also facilitate internal connections. To establish a strong professional network within each cohort, students will take several courses together, developing interdisciplinary working relationships and forging connections that can be relied upon throughout their career.
Analytical Tools Track
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.
Business Analytics Track
The Business Analytics track provides students with a deeper understanding of the practice of using analytics in business and industry: how to understand, frame, and solve problems in marketing, operations, finance, management of information technology, human resources, and accounting in order to develop and execute analytics projects within businesses.
Computational Data Analytics
The Computational Data Analytics track provides students with 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.