Courses

Analytics depends on computing, business, statistics, and research—all areas in which Georgia Tech ranks in the top 10. Learners in the Online Master of Science in Analytics (OMS Analytics) program have access to 23 different courses to fulfill required and elective curriculum slots.

The OMS Analytics degree is tailored to the learner’s interests and goals through the selection of a specialized degree track. Analytical Tools and Computational Data Analytics are the tracks that are currently available, with a third track in Business Analytics will be available Fall 2018. Within each track, students can further customize specific electives to build specific expertise. Academic advisers are available to help learners make the best choices for their professional needs.

Summer 2018 Courses

  • CS 6400 - Database Systems Concepts and Design
    Study of fundamental concepts with regard to rational databases. Topics covered include database design, query processing, concurrency control, and recovery. Credit not given for both CS 6400 and CS 6754.

  • ISYE 6414 - Regression Analysis
    Simple and multiple linear regression, inferences and diagnostics, stepwise regression and model selection, advanced regression methods, basic design and analysis of experiments, factorial analysis.

  • ISYE 6501 - Introduction to Analytics Modeling (This is a foundational course.)
    An introduction to important and commonly used models in Analytics, as well as aspects of the modeling process.

  • ISYE 6644 - Simulation
    Covers modeling of discrete-event dynamic systems and introduces methods for using these models to solve engineering design and analysis problems.

  • MGT 6203 - Data Analytics in Business
    Teaches the scientific process of transforming data into insights for making better business decisions. It covers the methodologies, algorithms, and challenges related to analyzing business data.

  • MGT 8803/6754 - Business Fundamentals for Analytics (This is a foundational course.)
    An accelerated introduction to the basics of management and the language of business. The course provides a framework that will enhance a person's effectiveness in the business world.

Fall 2018 Courses

  • CS 6400 - Database Systems Concepts and Design
    Study of fundamental concepts with regard to rational databases. Topics covered include database design, query processing, concurrency control, and recovery. Credit not given for both CS 6400 and CS 6754.

  • CS 7641 - Machine Learning
    Machine learning techniques and applications. Topics include foundational issues; inductive, analytical, numerical, and theoretical approaches; and real-world applications.

  • CSE 6040 - Computing for Data Analysis (This is a foundational course.)
    Computational techniques needed for data analysis; programming, accessing databases, multidimensional arrays, basic numerical computing, and visualization; hands-on applications and case studies. Credit will not be awarded for both CSE 6040 and CX 4240.

  • CSE 6242 - Data and Visual Analytics
    The course introduces students to analysis and visualization of complex high dimensional data. Both theory and applications will be covered including several practical case studies.

  • CSE 6250 - Big Data Analytics in Healthcare
    The course introduces students to analysis and visualization of complex high dimensional data. Both theory and applications will be covered including several practical case studies.

  • ISYE 6402 - Time Series Analysis
    Basic forecasting and methods, ARIMA models, transfer functions.

  • ISYE 6414 - Regression Analysis
    Simple and multiple linear regression, inferences and diagnostics, stepwise regression and model selection, advanced regression methods, basic design and analysis of experiments, factorial analysis.

  • ISYE 6420 - Bayesian Statistics
    Rigorous introduction to the theory of Bayesian Statistical Inference. Bayesian estimation and testing. Conjugate priors. Noninformative priors. Bayesian computation. Bayesian networks and Bayesian signal processing. Various engineering applications.

  • ISYE 6501 - Introduction to Analytics Modeling (This is a foundational course.)
    An introduction to important and commonly used models in Analytics, as well as aspects of the modeling process.

  • ISYE 6644 - Simulation
    Covers modeling of discrete-event dynamic systems and introduces methods for using these models to solve engineering design and analysis problems.

  • ISYE 6669 - Deterministic Optimization
    An introduction to deterministic optimization methodologies including approaches from linear, discrete, and nonlinear optimization including algorithms, computations, and a variety of applications.

  • MGT 8803/6754 - Business Fundamentals for Analytics (This is a foundational course.)
    An accelerated introduction to the basics of management and the language of business. The course provides a framework that will enhance a person's effectiveness in the business world.

  • MGT 6203 - Data Analytics in Business
    Teaches the scientific process of transforming data into insights for making better business decisions. It covers the methodologies, algorithms, and challenges related to analyzing business data.

  • MGT 6311 - Digital Marketing
    Become familiar with the key concepts and techniques utilized in modern digital marketing. Understand the primary characteristics of various online channels including mobile marketing, email marketing, and social media marketing. Gain awareness of important concepts and best practices in the use of digital marketing tools (search engine optimization, pay-per-click advertising, etc.).

  • MGT 8813 - Financial Modeling

  • CSE/ISYE/MGT 6748 - Applied Analytics Practicum
    Practical analytics project experience applying ideas from the classroom to a significant project of interest to a business, government agency, or other organization.

Future Courses

  • CS 7450 - Information Visualization
    Study of computer visualization principles, techniques, and tools used for explaining and understanding symbolic, structured, and/or hierarchical information. Includes data and software visualization. Students cannot receive credit for both CS 7450 and CS 4460.

  • CSE 6140 - Computational Science and Engineering Algorithms
    This course will introduce students to designing high-performance and scalable algorithms for computational science and engineering applications. The course focuses on algorithms design, complexity analysis, experimentation, and optimization, for important science and engineering applications.

  • CSE 6240 - Web Search and Text Mining
    Basic and advanced methods for web information retrieval and text mining: indexing and crawling, IR models, link and click data, social search, text classification and clustering.

  • CSE/ISYE 6740 - Computational Data Analytics (Machine Learning)
    Theoretical/computational foundations of analyzing large/complex modern datasets, including the fundamental concepts of machine learning and data mining needed for both research and practice Cross-listed with CSE 6740.

  • ISYE 6404 - Nonparametric Data Analysis
    Nonparametric statistics and basic categorical data analysis.

  • ISYE 6413 - Design and Analysis of Experiments
    Analysis of variance, full and fractional factoral designs at two and three levels, orthogonal arrays, response surface methodology, robust parameter design for production/process improvement.

  • ISYE 6416 - Computational Statistics
    This class describes the available knowledge regarding statistical computing. Topics include random deviates generation, importance sampling, Monte Carlo Markov chain (MCMC), EM algorithms, bootstrapping, model selection criteria, (e.g. C-p, AIC, etc.) splines, wavelets, and Fourier transform.

  • ISYE 6650 - Probabilistic Models
    An introduction to basic stochastic processes such as Poisson and Markov processes and their applications in areas such as inventory, reliability, and queuing.

  • ISYE 7406 - Data Mining and Statistical Learning
    Topics include neural networks, support vector machines, classification trees, boosting, and discriminant analyses.

Tracks

edX MicroMasters

Three of Georgia Tech’s OMS Analytics foundational courses will form the Analytics: Essential Tools and Methods MicroMasters program on edx.org. Students will learn essential analytics fundamentals, including statistical and machine learning methods, data-intensive computing and visualization techniques, software tools and business applications. The MicroMasters program will be free to enroll and open to anyone. Learners will be able to earn a MicroMasters certificate from edX as a standalone credential for $1,500. For more information on this program, please visit edX.org.