Online Master of Science in Analytics - Curriculum

Applications for Fall 2024 Open Aug. 15, 2023


There's no better way to learn about a field that requires computing, business, statistics, and research, than through a university that ranks in the top 10 in all four categories. The Online Master of Science in Analytics (OMS Analytics) at Georgia Tech meets this criterion – and many other high standards.

Many students fulfill the degree requirements in one-and-a-half to two years; however, the program is flexible enough that you have up to six years to complete them.

The program also consists of 30 course offerings. All OMS Analytics students take 15 hours of mandatory (core) coursework on big data analytics in business, visual analytics, computing, statistics, and operations research essentials. Additionally, you get to focus on your individual interests through 15 hours of electives in a specialized track (area of concentration).

Six-Hour Practicum

The degree program culminates with a practicum course that enables you to apply previously learned concepts and classroom teachings to a project of significant interest to you. Like students in the on-campus version of the program, you can propose an authentic business need that uses data from your own employer, or you can gain real-world experience through one of our pre-selected projects.

Visual Snapshot

Our OMS Analytics curriculum grid breaks down the different types of courses and concentrations into digestible components. For more details about each track’s focus, scroll down to the section below.

Areas of Specialization

Every student in the degree program gets to declare an area of specialization within data science and analytics. Three focus areas are available: an Analytical Tools track, a Business Analytics track, and a Computational Data Analytics track. The summaries below describe each in more detail.

Analytical Tools Track

The Analytical Tools track focuses on the quantitative methodology: how to select, build, solve and analyze models using methodology, regression, forecasting, data mining, machine learning, optimization, stochastics, and simulation.

Business Analytics Track

The Business Analytics track explores the understanding, framing, and solution of 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 Track

The Computational Data Analytics track explores a deeper understanding of big data—including how to acquire, preprocess, store, manage, analyze, and visualize large datasets.


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