Thanks to Georgia Tech's strengths in each of the key areas of analytics and data science, there are more than 80 courses that MS Analytics students can take to fulfill required and elective slots in their curriculum. Students are encouraged to choose electives to develop specific expertise within an area of analytics/data science where they have career interests.
Courses available to the students either as core requirements or elective options include topics such as machine learning, forecasting, regression analysis, data mining, statistical learning, natural language, computational statistics, simulation, digital marketing, optimization, visualization, databases, web and text mining, algorithms, high-performance computing, graph analytics, business intelligence, pricing analytics, revenue management, business process analysis, financial analysis, decision support, privacy and security, and risk analytics (see below for the full list).
We provide academic and career advising for all MS Analytics students, to help you select the right set of coursework for your personal interests and career goals.
MSA ELECTIVE COURSES
Computing Electives
Basic techniques of design and analysis of efficient algorithms for standard computational problems. NP-Completeness. Credit not allowed for both CS 3510 and CS 3511.
The course will cover current developments including distributed, object-oriented, temporal-spatial, Web-based, mobile, and active database technologies, and data warehousing and mining applications.
Introduction to principles and techniques of infomation visualization, the presentation of primarily abstract data to help people understand, analyze and make sense of data. Students will not receive credit for both CS 4460 and CS 7450.
A broad spectrum of information security: threats, basic cryptography, software vulnerabilities, programming for malice, operating system protections, network security, privacy, data mining, computer crime.
This course will cover the concepts, techniques, algorithms, and systems of big data systems and data analytics, with strong emphasis on big data processing systems, fundamental models and optimizations for data analytics and machine learning, which are widely deployed in real world big data analytics and applications.
Study of fundamental concepts with regard to relational databases. Topics covered include database design, query processing, concurrency control, and recovery. Credit not given for both CS 6400 and CS 6754.
This graduate seminar focuses on text and network analysis of data with applications to domains such as political science, sociolinguistics, and public health.
Introduction to computer vision including fundamentals of image formation, camera imaging geometry, feature detection and matching, stereo, motion estimation and tracking, image classification and scene understanding. Credit not awarded for both CS 6476 and CS 4495 or CS 4476.
Design and analysis of algorithms on a graduate level, including dynamic programming, divide and conquer, FFT, graph and flow algorithms, RSA, linear programming, and NP-completedness.
Advanced techniques for designing and analyzing efficient algorithms for combinatorial, algebraic, and number-theoretic problems.
Basic concepts and methods of artificial intelligence including both symbolic/conceptual and numerical/probabilistic techniques.
Introductory course on design principles and applications of data visualization. This course teaches best practices for visualizing datasets from diverse domains intended to help people make sense of data.
Describes the characteristics of interaction between humans and computers and demonstrates techniques for the evaluation of user-centered systems. Crosslisted with PSYC 6750.
This course provides a basic arsenal of powerful mathematical tools for the analysis of learning algorithms, focusing on both statistical and computational aspects.
An exploration of how artificial intelligence is used in modern digital computer games. Credit will not be awarded for CS 7632 and CS 4731, CS 7632 and LCC 4731 or CS 7632 and LMC 4731.
Structured knowledge representation; knowledge-based methods of reasoning and learning; problem-solving, modeling and design.
This course will cover theory and practice of deep learning, including neural network and structured models, optimization algorithms, and applications to perception and Artificial Intelligence.
Topics include lexical analysis, parsing, interpretation of sentences, semantic representation, organization of knowledge, inference mechanisms. Newer approaches combining statistical language processing and information retrieval techniques. Credit not allowed for both CS 7650 and CS 4650.
Provides the mathematical background for two of the pillars of modern data science: linear algebra and applied probability.
Special topics of current interest. Treatment of new developments in various areas of computing.
Special topics of current interest. Treatment of new developments in various areas of computing.
Special topics of current interest. Treatment of new developments in various areas of computing.
Special topics of current interest. Treatment of new developments in various areas of computing
Computing principles, computer architecture, algorithms and data structure; software development, parallelism. No credit for graduate students or undergraduates in Computer Science or Computational Media.
Computational techniques needed for data analysis; programming, accessing databases, multidimensional arrays, basic numerical computing, and visualization; hands-on applications and case studies. Credit is will not be awarded for both CSE 6040 and CX 4240.
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.
This course will introduce students to the design, analysis, and implementation of high performance computational science and engineering applications.
Introduction to MIMD parallel computation, using textbook excerpts, resesarch papers, and projects on multiple parallel machines. Emphasizes practical issues in high-performance computing.
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.
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.
Introduction to numerical solutions of the classical problems of linear algebra including linear systems, least squares, singular value decomposition, and eigen value problems. Crosslisted with MATH 6643.
Iterative methods for linear and nonlinear systems of equations including Jacobi, G-S, SOR, CG, multigrid, Newton, quasi-Newton, updating, and gradient based methods. Crosslisted with MATH 6644.
Foundations and algorithms concerning the development of conceptual models for systems, and their realization in the form of computer software; discrete and continuous models. Crosslisted with ECE 6730.
Provides the mathematical background for two of the pillars of modern data science: linear algebra and applied probability.
Topics of current interest in Computational Science and Engineering.
Topics of current interest in Computational Science and Engineering.
Topics of current interest in Computational Science and Engineering.
Topics of current interest in Computational Science and Engineering.
Topics of current interest in Computational Science and Engineering.
Topics of current interest in Computational Science and Engineering.
Topics of current interest in Computational Science and Engineering.
Topics of current interest in Computational Science and Engineering.
Topics of current interest in Computational Science and Engineering.
Topics of current interest in Computational Science and Engineering.
Business Electives
Information security vulnerabilities and risks; legal, cost, privacy, and technology constraints; derivation of strategies; technical and procedural means of achieving desired ends. Credit not awarded for both CS 6725 and CS 4725/MGT 4725/6725/PUBP 4725/6725.
Students will learn to apply advanced analytical techniques in business settings to facilitate better decision-making, with a particular focus on financial and managerial reporting.
Digital innovation is disrupting Financial Intermediation like lending, payments, asset management, and insurance. This class gives a comprehensive understanding of the FinTech industry.
Introduction to securities markets and study of theory and practice of security analysis and portfolio management concepts as applied to equities and fixed-income securities. Portfolio management relies on probability theory, statistical analysis, and computational methods. Credit not allowed for MGT 6078 and MGT 6080.
This course provides an introduction to financial theories and tools an entrepreneur needs to start, build, and harvest a successful venture. Cases and lecture will cover business evaluation and valuation, including the venture capital and the real option approach, forecasting models, quantitative and qualitative risk measurement methodologies, financing, venture capital funds, compensation structure and exit strategies. Credit not allowed for both MGT 6086 and MGT 4072.
In this course, student teams work under the guidance of faculty practitioner on current real-world business challenges to apply business analytics skills & methods.
Course focusing on the quantitative and qualitative aspects of marketing research, including gathering, analyzing, and reporting information used to solve specific marketing problems in several areas including international marketing research.
An applied overview of modernized digital marketing tools and strategy, emphasizing the diverse ways that contemporary marketers use information technology through digital channels such as social media and the Internet to achieve strategic business objectives.
This course applies a data science approach using network and text analytics to model and analyze how individuals and various kinds of markets behave.
Focus in on increasing profit by measuring price responses and controlling capacity. Topics include forecasting, price optimization, and revenue management. Credit not allowed for both MGT 6400 and MGT 6362.
Focus on the design and implementation of succesful supply chain strategies. Develop and recommend the right mix of operational levers through quantitative analysis for supply chain efficiency and effectiveness. Topics include: supply chain design, supply chain coordination, capacity management, retailing, and supplier management. Credit not allowed for both MGT 6401 and MGT 6362.
Provides exposure to the concepts, frameworks and techniques for managing projects. Coverage includes both general project management frameworks along with agile methodologies commonly used to manage IT projects. Cases, problems, activities, software tools, extra readings, and guest speakers focus specifically on IT project management practice.
This course helps business graduate students learn how to prepare and visualize data appropriately, to explore patterns and relations, and to convey these findings effectively.
Information security vulnerabilities and risks; legal, cost, privacy, and technology constraints; derivation of strategies; technical and procedural means of achieving desired ends. Credit will not be awarded for both MGT 6725 and MGT 4725 or CS 4725 or CS 6725 or PUBP 4725 or PUBP 6725.
This course takes a multi-disciplinary approach to privacy, a topic of great interest in the technology, policy, ethics, law, and business realms. Information collection, transmission, and utilization in analog and digital formats raise specific issues about information classification and organization; information storage and processing; and information transmission, transfer, and signaling. In addition, privacy tech policy affects the way one builds a network and related systems planning and design; human interfacing and use analysis; database development; and related aspects of hardware, software, economics, social factors, and capacity. Credit will not be awareded for both MGT 6726 and CS 4726 or CS 6726 or MGT 4726.
Topics of current interest.
Statistical Electives
Machine learning techniques and applications. Topics include foundational issues; inductive, analytical, numerical, and theoretical approaches; and real-world applications. Credit not awarded for both CS 7641 and CS 4641/CSE 6740/ISYE 6340.
Theoretical/computational foundations of analyzing large/complex modern datasets, including the fundamental concepts of machine learning and data mining needed for both resesarch and practice. Crosslisted with ISYE 6740. Credit not awarded for both CSE 6740 and CS 4641/7641/ISYE 6740.
Basic forecasting methods, ARIMA models, transfer functions.
Nonparametric statistics and basic categorical data analysis.
Fractional factorial designs, response surface methods.
Rigorous introduction to theory of statistical inference. Estimation and testing. Construction and assessment of estimators and tests. Fundamentals of decision theory, minimax, and Bayes Paradigms.
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.
Simple and multiple linear regression, inferences and diagnostics, stepwise regression and model selection, advanced regression methods, basic design and analysis of experiments, factorial analysis.
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.
An introduction to fundamental ideas and techniques in Biostatistics, with an emphasis on conceptual understanding and on the analysis of real data sets.
Theoretical/computational foundations of analyzing large/complex modern datasets, including the fundamental concepts of machine learning and data mining needed for both resesarch and practice. Crosslisted with CSE 6740. Credit not awarded for both ISYE 6740 and CS 4641/7641/CSE 6740.
Fundamentals of statistical inference are presented and developed for models used in the modern analysis of financial data. Techniques are motivated by examples and developed in the context of applications. Cross-listed with MATH 6783.
Topics include hazard functions, life distributions, censoring, life tables, nonparametric and parametric estimation and inference, accelerated life testing, structure functions, reliability and maintenance systems, replacement theory.
The course focuses on sensor-based condition monitoring techniques, modeling of degradation processes, fault diagnostics and prognostics of failures in engineering systems using stochastic and statistical methods.
Random and mixed models, nested and blocked designs. Intended for Ph.D. students and those seeking the M.S. in Statistics.
Nonlinear models, logistic regression, loglinear models. Intended for Ph.D. students and those seeking the M.S. in Statistics.
Multivariate ANOVA, principal components, factor analysis etc. Intended for Ph.D. students and those seeking the M.S. in Statistics.
Topics include neural networks, support vector machines, classification trees, boosting and discriminant analyses. Intended for Ph.D. students and those seeking the M.S. in Statistics.
Special topics in Industrial and Systems Engineering.
Special topics in Industrial and Systems Engineering.
Operations Research Electives
The course provides an introduction to theory and practice of graphical models in machine learning. It covers three main aspects; representation, probabilistic inference, and learning.
Topics include preferences and utilities, social choice, equilibrium concepts, noncooperative and cooperative game theory, price mechanisms, auction mechanisms, voting theory, and incentive compatibility.
Includes topics in sequencing and scheduling with emphasis on deterministic machine scheduling problems with some stochastic results examined. Complexity of various problems will be analyzed.
Deterministic models of supply chains including location and material flow. Optimization techniques including linear programming, network flows, integer programming, and heuristics.
Probabilistic models of supply chains, including the effects of variability; models of wholesale and retail demand; forecasting and simulation.
Covers modeling of discrete-event dynamic systems and introduces methods for using these models to solve engineering design and analysis problems.
An introduction to basic stochastic processes such as Poisson and Markov processes and their applications in areas such as inventory, reliability, and queueing.
Fundamentals of nonlinear optimization. Topics include optimality conditions; convex programming and duality; unconstrained and constrained methods. Polynomial algorithms and interior point methods. Dual methods. This course is for students seriously considering a PhD.
An introduction to deterministic optimization methodologies including approaches from linear, discrete, and nonlinear optimization including algorithms and computations. Applications will be introduced as appropriate.
Strategies and techniques for converting optimization theory into effective computational procedures. Emphasis is on applications in linear, integer, and nonlinear programming; networks and graphs.
Theory, algorithms, and applications of computer simulation. Topics include generalized semi-Markov processes; input-output analysis; random number, variate, and sample path generation; variance reduction techniques; and optimization via simulation. This course is intended for Ph.D. students.
Convex programming; linear, conic quadratic and semidefinite programming; cheap optimization methods for extremely large-scale convex problems.
Typical coverage includes: Matching theory, network optimization, traversals in graphs, integrality of polyhedra, matroids, covers, cliques, and stable sets
General integer modeling concepts; valid inequalities and facets; duality; general algorithms such as branch-and-bound and branch-and-cut; special purpose algorithms; applications.
The course provides an introduction to theory and practice of graphical models in machine learning. It covers three main aspects; representation, probabilistic inference, and learning.
Special topics in Industrial and Systems Engineering.
Special Topics in the field of Operations Research.
Special Topics in the field of Operations Research.
Special Topics in the field of Operations Research.
Special Topics in the field of Operations Research.
Application Electives
This course introduces students to spatial analysis using geographic information systems. Fundamentals of software design and geographic data are covered.
The course provides students with advanced spatial analysis techniques including network analysis, three-dimensional surface modeling, and GIS application development.
This course focuses on the application of geographic information systems (GIS) to environmental problems. It highlights the types and sources of data appropriate to those applications.
This advanced GIS course addresses the collection, management, analysis, and interpretation of spatial social, economic, housing, and demographic information. Credit not allowed for both CP 6570 and CP 6551.
This course teaches fundamental programming skills for geoprocessing and data presentation in a geographic information system environment. The primary languages used are Python and Javascript.
Topics of current interest in geographic information systems.
A broad review of the US health system and the application of informatics to the clinical practice of medicine, digital imaging, public health and bioinformatics.
Topics include analysis of flows, bottlenecks and queuing, types of operations, manufacturing inventories, aggregreate production planning, lot sizes and lead times, and pull production systems.
Topics include design and analysis of materials handling systems, warehouse layout, order picking strategies, warehousing inventories, warehouse management systems, integration of production and distribution systems. Credit will not be awarded for both ISYE 6202 and ISYE 6383.
Topics include supply chain characterization, site location, mode selection, distribution planning, vehicle routing, demand management, replenishment management, geographic information systems, and real-time control issues. Credit will not be awarded for both ISYE 6203 and ISYE 6383.
The focus is on the health and public applications of Operations Research. Students will complete a group project with a non-profit organization and discuss papers.
Production scheduling; inventory systems; warehousing, including stocking strategies, order-picking, sortation, automation; distribution.
Advanced models in operations planning, scheduling and control of supply chain, production and service systems. Intended for Ph.D. students.
Advanced modeling and analysis of freight transportation and logistics systems. Intended for Ph.D. students.
Special topics in Industrial and Systems Engineering.
Special Topics in the field of Operations Research.