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Computational Data Analytics Track

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 credit-hours 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 hours 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 hours 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 8803 Big Data Analytics in Business (3 hours)
  • CSE 6242 Data and Visual Analytics (3 hours)
  • One operations research course (3 hours)
  • Two statistics courses (6 hours)
  • 5 Elective/core courses (15 hours; 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 hours 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/ISyE 6740 Computational Data Analysis
  • At least three computing courses beyond the introductory core, including CSE 6242 Data and Visual Analytics (can also include CSE/ISyE Computational Data Analysis)
  • MGT 6203 Data Analytics in Business
  • Two statistics courses (can include CSE/ISyE 6740 Computational Data Analysis)
  • One operations research course
  • Three additional electives (including introductory courses in CSE, MGT, and ISyE if not waived)
  • CSE/ISyE/MGT 6748 Applied Analytics Practicum (can include approved applied analytics internship)
Sample programs of study for Computational Data Analytics track
  Fall semester Spring semester Summer semester
Example program 1 for Computational Data Analytics Track
  • MGT 8803 Introduction to Business for Analytics
  • ISyE 8803 Introduction to Analytics Models
  • ISyE 6414 Regression Analysis
  • ISyE 6333 Operations Research I
  • CSE 6141 Massive Graph Analytics
  • CSE/ISyE 6740 Computational Data Analytics
  • MGT 6203 Data Analytics in Business
  • CSE 6242 Data and Visual Analytics
  • CSE/ECE 6730 Modeling and Simulation
  • CSE 6240 Web Search and Text Mining
  • CSE/ISyE/MGT 6748 Applied Analytics Practicum
Example program 2 for Computational Data Analytics Track
  • MGT 8803 Introduction to Business for Analytics
  • ISyE 8803 Introduction to Analytics Models
  • CSE/ISyE 6740 Computational Data Analytics
  • ISyE 6664 Simulation
  • CSE 6230 High Performance Parallel Computing
  • ISyE 7406 Data Mining and Statistical Learning
  • MGT 6203 Data Analytics in Business
  • CSE 6242 Data and Visual Analytics
  • CSE 6240 Web Search and Text Mining
  • MGT 6400 Pricing Analytics and Revenue Management
  • CSE/ISyE/MGT 6748 Applied Analytics Practicum

Additional Resources - All Tracks

In addition to learning from world leaders and cutting-edge researchers in analytics, students in the MS Analytics program will have access to a variety of specialized resources, including:

  • Academic and professional advising
  • Job placement support
  • Funding to attend a major analytics conference
  • Exposure at Georgia Tech's annual Business Analytics and Big Data Industry Forum
  • Georgia Tech's state-of-the-art high-performance computing infrastructure for massive-scale analytics
  • Free cloud computing resources
  • Free and discounted analytics, engineering, and productivity software
  • Free and discounted certification training
  • Creativity training
  • Written and oral communication training
  • Leadership and teamwork training
  • Ethics training
  • Access to the MS Analytics Seminar Series

Capstone Experience: Applied Analytics Practicum - All Tracks

At the conclusion of the program, in the summer semester, each student will complete a 6-credit-hour applied analytics practicum. For the practicum course, students have two choices.  Many students may want to pursue an applied analytics internship as their practicum, working on a significant analytics project at the a company or organization site.  Other students may prefer to work in cross-disciplinary teams on a significant analytics project that companies and organizations bring to campus; those teams will consist of MS Analytics students from each track, to bring each of their specializations to bear in an integrated solution. In this way, the interdisciplinary learning will be emphasized in practice as well as in the classroom.