# Online Master of Science in Analytics - Curriculum

##### FALL 2025 STANDARD APPLICATION DEADLINE

Feb 1, 2025##### Fall 2025 Final Application Deadline

Mar 15, 2025##### SPRING 2025 Program Start

Jan 6, 2025## Curriculum

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 requirements for this online data analytics master’s degree in one-and-a-half to two years; however, the program is flexible enough that you have up to six years to complete it.

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 Online Master of Science in Analytics 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. You can propose an authentic business need that uses data from your own employer or third party (external project), or you can gain real-world experience through one of our GT-sponsored projects (internal project).

The practicum is 10-15 weeks depending on the semester and the process starts several months in advance.

**Fall**(15 weeks): Process begins in**mid-May**with the*Practicum Interest Survey*sent to the student**Spring**(15 weeks): Process begins in**mid-September**with the*Practicum Interest Survey*sent to the student**Summer**(10 weeks): Process begins in**mid-February**with the*Practicum Interest Survey*sent to the student

**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 pursues 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

Students with sufficient background in any of these areas may apply to replace one or more of these courses with an elective.

**Introduction for Computing for Data Analytics (CSE 6040)**

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.**Introduction to Analytics Modeling (ISYE 6501) **

This course gives a basic introduction to a wide variety of analytics models and techniques, including the basic ideas behind the models, experience using software to solve/analyze them, and case studies dealing with combining models to find a complete solution. Modeling approaches covered include classification, clustering, change detection, time series modeling, regression models, design of experiments, probability distributions, probability-based models and simulation, PCA, and optimization. Cross-cutting topics like data preparation, model validation, and variable selection are also covered.**Business Fundamentals for Analytics (MGT 8803/6754) **

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.

**Data and Visual Analytics (CSE 6242) **

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.**Data Analytics in Business (MGT 6203) **

Teaches the scientific process of transforming data into insights for making better business decisions. It covers methodologies, algorithms, and challenges related to analyzing business data. Completion of MGT 6203 is required before starting the practicum. Suggested prerequisite: ISYE 6501

Select two courses from the list.

**Bayesian Statistics (ISYE 6420)**

This course covers the fundamentals of Bayesian statistics, including both the underlying models and methods of Bayesian computation, and how they are applied. Modeling topics include conditional probability and Bayes’ formula, Bayesian inference, credible sets, conjugate and noninformative priors, hypothesis testing, Bayesian regression, empirical Bayes models, and hierarchical Bayesian models. Computational topics include Monte Carlo methods, MCMC, Metropolis-Hasting algorithms, Gibbs sampling, variational Bayes, and other methods for posterior approximation. Various applications of Bayesian statistics will be discussed. Prerequisite: Calculusbased Introductory Statistics Course

**Computational Data Analysis (ISYE 6740) **

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. The course is designed to answer the most fundamental questions about machine learning: What are the most important methods to know about, and why? How can we answer the question 'is this method better than that one' using asymptotic theory? How can we answer the question 'is this method better than that one' for a specific dataset of interest? What can we say about the errors our method will make on future data? What's the 'right' objective function? What does it mean to be statistically rigorous? This course is designed to give graduate students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The course covers topics from machine learning, classical statistics, and data mining. Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be Analytical Tools an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate. Some experience with coding are expected.**Regression Analysis (ISYE 6414)**

This course covers the fundamentals of Bayesian statistics, including both the underlying models and methods of Bayesian computation, and how they are applied. Modeling topics include conditional probability and Bayes’ formula, Bayesian inference, credible sets, conjugate and noninformative priors, hypothesis testing, Bayesian regression, empirical Bayes models, and hierarchical Bayesian models. Computational topics include Monte Carlo methods, MCMC, Metropolis-Hasting algorithms, Gibbs sampling, variational Bayes, and other methods for posterior approximation. Various applications of Bayesian statistics will be discussed. Prerequisite: Calculus-based Introductory Statistics Course

**Time Series Analysis (ISYE 6402)**

Basic forecasting and methods, ARIMA models, transfer functions.

**Data Mining and Statistical Learning (ISYE 7406)**

An introduction to some commonly used data mining and statistical learning algorithms such as K-nearest neighbor (KNN) algorithm, linear methods for regression and classification, tree-based methods, ensemble methods, support vector machine, neural networks, and Kmeans clustering algorithm. This course focuses on the understanding of methodology, motivation, and assumptions of different algorithms as well as implementation of these algorithms with data examples using the R statistical software.

**High-dimensional Data Analytics (ISYE 8803)**

This course focuses on analysis of high-dimensional structured data including profiles, images, and other types of functional data using statistical machine learning. A variety of topics such as functional data analysis, image processing, multilinear algebra and tensor analysis, and regularization in highdimensional regression and its applications including low rank and sparse learning is covered. Optimization methods commonly used in statistical modeling and machine learning and their computational aspects are also discussed - convex conic optimization, which is a significant generalization of linear optimization. The fourth and final module is on integer optimization, which augments the previously covered optimization models with the flexibility of integer decision variables. The course blends optimization theory and computation with various applications to modern data analytics.

Select one course from the list.

**Simulation (ISYE 6644)**

Basic forecasting and methods, ARIMA models, transfer functions.

**Deterministic Optimization (ISYE 6669)**

The course will teach basic concepts, models, and algorithms in linear optimization, integer optimization, and convex optimization. The first module of the course is a general overview of key concepts in linear algebra, calculus, and optimization. The second module of the course is on linear optimization, covering modeling techniques, basic polyhedral theory, simplex method, and duality theory.

Select two courses from the list.

**Time Series Analysis (ISYE 6402)**

Basic forecasting and methods, ARIMA models, transfer functions.

**Regression Analysis (ISYE 6414)**

Simple and multiple linear regression, inferences and diagnostics, stepwise regression and model selection, advanced regression methods, basic design and analysis of experiments, factorial analysis.

**Bayesian Statistics (ISYE 6420)**

This course covers the fundamentals of Bayesian statistics, including both the underlying models and methods of Bayesian computation, and how they are applied. Modeling topics include conditional probability and Bayes’ formula, Bayesian inference, credible sets, conjugate and noninformative priors, hypothesis testing, Bayesian regression, empirical Bayes models, and hierarchical Bayesian models. Computational topics include Monte Carlo methods, MCMC, Metropolis-Hasting algorithms, Gibbs sampling, variational Bayes, and other methods for posterior approximation. Various applications of Bayesian statistics will be discussed. Prerequisite: Calculus-based Introductory Statistics Course

**Simulation (ISYE 6644)**

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

**Deterministic Optimization (ISYE 6669)**

The course will teach basic concepts, models, and algorithms in linear optimization, integer optimization, and convex optimization. The first module of the course is a general overview of key concepts in linear algebra, calculus, and optimization. The second module of the course is on linear optimization, covering modeling techniques, basic polyhedral theory, simplex method, and duality theory.

**Computational Data Analysis (ISYE 6740)**

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. The course is designed to answer the most fundamental questions about machine learning: What are the most important methods to know about, and why? How can we answer the question 'is this method better than that one' using asymptotic theory? How can we answer the question 'is this method better than that one' for a specific dataset of interest? What can we say about the errors our method will make on future data? What's the 'right' objective function? What does it mean to be statistically rigorous? This course is designed to give graduate students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The course covers topics from machine learning, classical statistics, and data mining. Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be Analytical Tools an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate. Some experience with coding are expected.

**Data Mining and Statistical Learning (ISYE 7406)**

An introduction to some commonly used data mining and statistical learning algorithms such as K-nearest neighbor (KNN) algorithm, linear methods for regression and classification, tree-based methods, ensemble methods, support vector machine, neural networks, and Kmeans clustering algorithm. This course focuses on the understanding of methodology, motivation, and assumptions of different algorithms as well as implementation of these algorithms with data examples using the R statistical software.

**Topics on High-dimensional Data Analytics (ISYE 8803)**

This course focuses on analysis of high-dimensional structured data including profiles, images, and other types of functional data using statistical machine learning. A variety of topics such as functional data analysis, image processing, multilinear algebra and tensor analysis, and regularization in highdimensional regression and its applications including low rank and sparse learning is covered. Optimization methods commonly used in statistical modeling and machine learning and their computational aspects are also discussed - convex conic optimization, which is a significant generalization of linear optimization. The fourth and final module is on integer optimization, which augments the previously covered optimization models with the flexibility of integer decision variables. The course blends optimization theory and computation with various applications to modern data analytics.

The number of elective hours required depends on how many Introductory Core courses (if any) are replaced with electives.

Students who take two of these additional electives from the Track Electives list of either the Business Analytics track or the Computational Data Analytics track will satisfy the requirements for two tracks. To satisfy the Computational Data Analytics Track, students must have taken ISYE 6740 as one of their Statistics, Track, or Additional Electives.

**Big Data Analytics in Healthcare (CSE 6250)** 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.

**Database Systems Concepts and Design (CS 6400)** 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.

**Artificial Intelligence (CS 6601)** This course is a survey of the field of Artificial Intelligence and will often be taken as the first graduate course in the area. It is designed to be challenging and involves significant independent work, readings, and assignments. The course covers most of the required textbook Artificial Intelligence A Modern Approach 3rd edition, which is a keystone of Georgia Tech’s Intelligent Systems PhD qualifier exam.

**Human Computer Interaction (CS 6750) **This course is an introductory course on human-computer interaction. It does not presuppose any earlier knowledge of human-computer interaction, computer science, or psychology. The class covers three broad categories of topics within human-computer interaction: (a) the principles and characteristics of the interaction between humans and computers; (b) the techniques for designing and evaluating user-centered data.

** Network Science (CS 7280)** It is often the case that complex systems, both living and man-made, can be represented as static or dynamic networks of many interacting components. These components are typically much simpler in terms of behavior or function than the overall system, implying that the additional complexity of the latter is an emergent network property. Network science is a relatively new discipline that investigates the topology and dynamics of such complex networks, aiming to better understand the behavior, function and properties of the underlying systems. The applications of network science cover physical, informational, biological, cognitive, and social systems. In this course, we will study algorithmic, computational, and statistical methods of network science, as well as various applications in social, communication and biological networks. A significant component of the course will focus on the overlap between machine learning and network science, covering methods for network inference.

**Knowledge-based AI (CS 7637)** The twin goals of knowledge-based artificial intelligence (AI) are to build AI agents capable of human-level intelligence and gain insights into human cognition. The learning goals of the Knowledge-Based AI course are to develop an understanding of (1) the basic architectures, representations and techniques for building knowledge-based AI agents, and (2) issues and methods of knowledge-based AI. The main learning strategies are learning-by-example and learning-by-doing. Thus, the course puts a strong emphasis on homework assignments and programming projects. The course will cover three kinds of topics: core topics such as knowledge representation, planning, constraint satisfaction, case-based reasoning, knowledge revision, incremental concept learning, and explanation based learning; common tasks such as classification, diagnosis, and design; and advanced topics such as analogical reasoning, visual reasoning, and meta-reasoning.

** Reinforcement Learning (CS 7642)** Reinforcement Learning is a subarea of Machine Learning, that area of Artificial Intelligence that is concerned with computational artifacts that modify and improve their performance through experience. This course focuses on automated computational decision making through a combination of classic papers and more recent work. It examines efficient algorithms, where they exist, for single-agent and multiagent planning as well as approaches to learning near optimal decisions from experience. Topics include Markov decision processes; stochastic and repeated games; partially observable Markov decision processes; reinforcement learning; and interactive reinforcement learning. The class is particularly interested in issues of generalization, exploration, and representation.

**Deep Learning (CS 7643)** Deep learning is a sub-field of machine learning that focuses on learning complex, hierarchical feature representations from raw data. The dominant method for achieving this, artificial neural networks, has revolutionized the processing of data (e.g. images, videos, text, and audio) as well as decision-making tasks (e.g. game-playing). Its success has enabled a tremendous amount of practical commercial applications and has had significant impact on society. In this course, students will learn the fundamental principles, underlying mathematics, and implementation details of deep learning. This includes the concepts and methods used to optimize these highly parameterized models (gradient descent and backpropagation, and more generally computation graphs), the modules that make them up (linear, convolution, and pooling layers, activation functions, etc.), and common neural network architectures (convolutional neural networks, recurrent neural networks, etc.). Applications ranging from computer vision to natural language processing, and decision-making (reinforcement learning) will be demonstrated. Through in-depth programming assignments, students will learn how to implement these fundamental building blocks as well as how to put them together using a popular deep learning library, PyTorch. In the final project, students will apply what they have learned to real-world scenarios by exploring these concepts with a problem that they are passionate about.

** Machine Learning for Trading (CS 7646)** This course introduces students to the real-world challenges of implementing machine learning based trading strategies including the algorithmic steps from information gathering to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, Q-Learning, KNN, and regression trees and how to apply them to actual stock trading situations. Modeling,

**Simulation, & Military Gaming (CSE 6742)** Computer modeling and simulation offers a unique perspective on events because of the ability to hold some variables constant and change others and run a scenario repeatedly searching for underlying themes. Computer simulation has been used as an analytical tool in the natural sciences, business, commerce, government, and politics. This course focuses on the creation and application of computer simulations to model strategic international events concerning warfare. The course is project-based, requiring computing and international affairs students to work together in multidisciplinary teams to analyze specific questions utilizing computer-based modeling and simulation tools (largely, but not exclusively “Net Logo”).

**Applied Natural Language Processing (CSE 8803)** The primary objective of this course is to introduce you to broad classes of techniques and tools for analyzing text data using Natural Language Processing (NLP) algorithms and techniques. The class will emphasize applying pre-processing, processing, and post-processing NLP techniques to analyze and develop NLP models using conventional and deep learning machine learning methods.

**Time Series Analysis (ISYE 6402)** Basic forecasting and methods, ARIMA models, transfer functions.

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

**Bayesian Statistics (ISYE 6420)** This course covers the fundamentals of Bayesian statistics, including both the underlying models and methods of Bayesian computation, and how they are applied. Modeling topics include conditional probability and Bayes’ formula, Bayesian inference, credible sets, conjugate and noninformative priors, hypothesis testing, Bayesian regression, empirical Bayes models, and hierarchical Bayesian models. Computational topics include Monte Carlo methods, MCMC, Metropolis-Hasting algorithms, Gibb’s sampling, variational Bayes, and other methods for posterior approximation. Various applications of Bayesian statistics will be discussed. Prerequisite: Calculus based Introductory Statistics Course.

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

**Deterministic Optimization (ISYE 6669)** The course will teach basic concepts, models, and algorithms in linear optimization, integer optimization, and convex optimization. The first module of the course is a general overview of key concepts in linear algebra, calculus, and optimization. The second module of the course is on linear optimization, covering modeling techniques, basic polyhedral theory, simplex method, and duality theory.

**Computational Data Analysis (ISYE 6740)** Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. The course is designed to answer the most fundamental questions about machine learning: What are the most important methods to know about, and why? How can we answer the question 'is this method better than that one' using asymptotic theory? How can we answer the question 'is this method better than that one' for a specific dataset of interest? What can we say about the errors our method will make on future data? What's the 'right' objective function? What does it mean to be statistically rigorous? This course is designed to give graduate students a thorough grounding in the methods, theory, mathematics, and algorithms needed to do research and applications in machine learning. The course covers topics from machine learning, classical statistics, and data mining. Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be Analytical Tools an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate. Some experience with coding are expected.

** Data Mining and Statistical Learning (ISYE 7406)** An introduction to some commonly used data mining and statistical learning algorithms such as KNearest neighbor (KNN) algorithm, linear methods for regression and classification, tree-based methods, ensemble methods, support vector machine, neural networks, and Kmeans clustering algorithm. This course focuses on the understanding of methodology, motivation, and assumptions of different algorithms as well as implementation of these algorithms with data examples using the R statistical software.

**Topics on High-dimensional Data Analytics (ISYE 8803)** This course focuses on analysis of high dimensional structured data including profiles, images, and other types of functional data using statistical machine learning. A variety of topics such as functional data analysis, image processing, multilinear algebra and tensor analysis, and regularization in high-dimensional regression and its applications including low rank and sparse learning is covered. Optimization methods commonly used in statistical modeling and machine learning and their computational aspects are also discussed - convex conic optimization, which is a significant generalization of linear optimization. The fourth and final module is on integer optimization, which augments the previously covered optimization models with the flexibility of integer decision variables. The course blends optimization theory and computation with various applications to modern data analytics.

** Digital Marketing (MGT 6311)** 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.).

**Financial Modeling (MGT 8813)** Students will create spreadsheets using pivot tables, Excel functions, solver, goal seek, and VBA. The course will also include topics such as time value of money, stock and bond valuation, firm valuation, financial statements, cost of capital, option pricing models, and portfolio optimization. This course is intended to prepare students to build financial models.

** Data Analysis for Continuous Improvement (MGT 8823)** Because it is one thing to know how to analyze data and another thing to use it to solve problems, the purpose of this course is to show how mastery of data analysis can be applied to the real world. In doing so, we will explore the development of key performance indicators (KPIs) and how to use KPIs to drive improvement in an organization. We will also discuss the four methods of continuous improvement and how data analysis can be leveraged in each method. Because the course weaves examples from both industry and everyday life, what is learned can be directly applied to both your personal and professional lives. In addition to the practical knowledge gained, students will also be shown tools that are outside of the typical analytics course and provided with the knowledge of how to use these tools on their own and in conjunction with the data analysis. Because one must know not only how to analyze data but also how to determine from where the actual data should come, the tools chosen will assist the student in coming up with what to analyze in the first place.

**Privacy for Professionals (MGT 6727)** Because it is one thing to know how to analyze data and another thing to use it to solve problems, the purpose of this course is to show how mastery of data analysis can be applied to the real world. In doing so, we will explore the development of key performance indicators (KPIs) and how to use KPIs to drive improvement in an organization. We will also discuss the four methods of continuous improvement and how data analysis can be leveraged in each method. Because the course weaves examples from both industry and everyday life, what is learned can be directly applied to both your personal and professional lives. In addition to the practical knowledge gained, students will also be shown tools that are outside of the typical analytics course and provided with the knowledge of how to use these tools on their own and in conjunction with the data analysis. Because one must know not only how to analyze data but also how to determine from where the actual data should come, the tools chosen will assist the student in coming up with what to analyze in the first place.

** Information Security Policies and Strategies (PUBP 6725) **This course introduces students to the policy and management aspects of cybersecurity. It is based on the idea that cybersecurity policy can be sorted into three “layers” representing different levels of social organization: individual organization, the national level, and the transnational level. The course is divided into four modules: the first exposes students to basic concepts and definitions regarding policy, governance, and threats; the second deals with cybersecurity policy at the organizational level; the third deals with cybersecurity public policy at the national level; the fourth deals with cyber conflict, policy, and diplomacy at the transnational level. This course situates cybersecurity in the overall Internet ecosystem.

**Applied Analytics Practicum (ISYE 6748)**

A practical analytics experience that enables you to apply previously learned concepts and classroom teachings to a project of significant interest, either within your current organization or within a Georgia Tech-sponsored company (partnering companies pre-determine their projects). The objective of the practicum is to properly define and scope the analytics project, apply appropriate methodologies, create value, manage the project, and provide results in writing. The practicum project can be a project at a self-secured internship or work you do for your current employer. If either apply, you still must gain approval to register for (and fulfill the requirements of) the Applied Analytics Practicum course within the semester you are doing the project work. This course is by permit. The prerequisites for registration are completion of at least eight courses, including Data and Visual Analytics (CSE 6242) and Data Analytics in Business (MGT 6203) prior to (not concurrent with) the practicum. Offered: Fall, Spring, Summer

### Business Analytics Track

Students with sufficient background in any of these areas may apply to replace one or more of these courses with an elective.

**Introduction for Computing for Data Analytics (CSE 6040)**

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.**Introduction to Analytics Modeling (ISYE 6501)**

This course gives a basic introduction to a wide variety of analytics models and techniques, including the basic ideas behind the models, experience using software to solve/analyze them, and case studies dealing with combining models to find a complete solution. Modeling approaches covered include classification, clustering, change detection, time series modeling, regression models, design of experiments, probability distributions, probability-based models and simulation, PCA, and optimization. Cross-cutting topics like data preparation, model validation, and variable selection are also covered. **Business Fundamentals for Analytics (MGT 8803/6754)**

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.

**Data and Visual Analytics (CSE 6242)**

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.**Data Analytics in Business (MGT 6203) **

Teaches the scientific process of transforming data into insights for making better business decisions. It covers methodologies, algorithms, and challenges related to analyzing business data. Completion of MGT 6203 is required before starting the practicum. Suggested prerequisite: ISYE 6501

Select two courses from the list.

**Time Series Analysis (ISYE 6402)**

Basic forecasting and methods, ARIMA models, transfer functions.

**Regression Analysis (ISYE 6414)**

Simple and multiple linear regression, inferences and diagnostics, stepwise regression and model selection, advanced regression methods, basic design and analysis of experiments, factorial analysis.

**Bayesian Statistics (ISYE 6420)**

This course covers the fundamentals of Bayesian statistics, including both the underlying models and methods of Bayesian computation, and how they are applied. Modeling topics include conditional probability and Bayes’ formula, Bayesian inference, credible sets, conjugate and noninformative priors, hypothesis testing, Bayesian regression, empirical Bayes models, and hierarchical Bayesian models. Computational topics include Monte Carlo methods, MCMC, Metropolis-Hasting algorithms, Gibbs sampling, variational Bayes, and other methods for posterior approximation. Various applications of Bayesian statistics will be discussed. Prerequisite: Calculus-based Introductory Statistics Course

**Computational Data Analysis (ISYE 6740)**

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. The course is designed to answer the most fundamental questions about machine learning: What are the most important methods to know about, and why? How can we answer the question 'is this method better than that one' using asymptotic theory? How can we answer the question 'is this method better than that one' for a specific dataset of interest? What can we say about the errors our method will make on future data? What's the 'right' objective function? What does it mean to be statistically rigorous? This course is designed to give graduate students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The course covers topics from machine learning, classical statistics, and data mining. Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be Analytical Tools an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate. Some experience with coding are expected.

**Data Mining and Statistical Learning (ISYE 7406)**

An introduction to some commonly used data mining and statistical learning algorithms such as K-nearest neighbor (KNN) algorithm, linear methods for regression and classification, tree-based methods, ensemble methods, support vector machine, neural networks, and Kmeans clustering algorithm. This course focuses on the understanding of methodology, motivation, and assumptions of different algorithms as well as implementation of these algorithms with data examples using the R statistical software.

**Topics on High-dimensional Data Analytics (ISYE 8803)**

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. The course is designed to answer the most fundamental questions about machine learning: What are the most important methods to know about, and why? How can we answer the question 'is this method better than that one' using asymptotic theory? How can we answer the question 'is this method better than that one' for a specific dataset of interest? What can we say about the errors our method will make on future data? What's the 'right' objective function? What does it mean to be statistically rigorous? This course is designed to give graduate students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The course covers topics from machine learning, classical statistics, and data mining. Students entering the class with a preexisting working knowledge of probability, statistics and algorithms will be Analytical Tools an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate. Some experience with coding are expected.

Select one course from the list.

**Simulation (ISYE 6644)**

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

**Deterministic Optimization (ISYE 6669)**

The course will teach basic concepts, models, and algorithms in linear optimization, integer optimization, and convex optimization. The first module of the course is a general overview of key concepts in linear algebra, calculus, and optimization. The second module of the course is on linear optimization, covering modeling techniques, basic polyhedral theory, simplex method, and duality theory.

The number of elective hours required depends on how many Introductory Core courses (if any) are replaced with electives.

Students who take two of these additional electives from the Track Electives list of either the Business Analytics track or the Computational Data Analytics track will satisfy the requirements for two tracks. To satisfy the Computational Data Analytics Track, students must have taken ISYE 6740 as one of their Statistics, Track, or Additional Electives.

**Data Analysis for Continuous Improvement (MGT 8823)**

Because it is one thing to know how to analyze data and another thing to actually use it to solve problems, the purpose of this course is to show how mastery of data analysis can be applied to the real world. In doing so, we will explore the development of key performance indicators (KPIs) and how to use KPIs to drive improvement in an organization. We will also discuss the four methods of continuous improvement and how data analysis can be leveraged in each method. Because the course weaves examples from both industry and everyday life, what is learned can be directly applied to both your personal and professional lives. In addition to the practical knowledge gained, students will also be shown tools that are outside of the typical analytics course and provided with the knowledge of how to use these tools on their own and in conjunction with the data analysis. Because one must know not only how to analyze data but also how to determine from where the actual data should come, the tools chosen will assist the student in coming up with what to analyze in the first place.**Digital Marketing (MGT 6311)**

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.).**Financial Modeling (MGT 8813)**

Students will create spreadsheets using pivot tables, Excel functions, solver, goal seek, and VBA. The course will also include topics such as time value of money, stock and bond valuation, firm valuation, financial statements, cost of capital, option pricing models, and portfolio optimization. This course is intended to prepare students to build financial models.

**Privacy for Professionals (MGT 6727)**

This course takes a multi-disciplinary approach to the study of privacy––a current topic of great international interest for those in technology, policy, law, and/or business. It prepares students to work professionally in the privacy field, with an emphasis on U.S.-based law and practice. Course topics include introduction to privacy, federal and state regulators and enforcement of privacy law, principles of information management, online privacy, the California Consumer Privacy Act, information security and data breach notification laws, European Union privacy laws, medical privacy, financial privacy, education privacy, workplace privacy, privacy issues in civil litigation and government investigations, and emerging issues. The professor draws on his extensive experience in business, government, technology, and law to address current privacy debates.

**Information Security Policies and Strategies (PUBP 6725)**

This course introduces students to the policy and management aspects of cybersecurity. It is based on the idea that cybersecurity policy can be sorted into three “layers” representing different levels of social organization: individual organization, the national level, and the transnational level. The course is divided into four modules: the first exposes students to basic concepts and definitions regarding policy, governance, and threats; the second deals with cybersecurity policy at the organizational level; the third deals with cybersecurity public policy at the national level; the fourth deals with cyber conflict, policy, and diplomacy at the transnational level. This course situates cybersecurity in the overall Internet ecosystem.

The number of elective hours required depends on how many Introductory Core courses (if any) are replaced with electives.

Students who take two of these additional electives from the Track Electives list of either the Analytical Tools track or the Computational Data Analytics track will satisfy the requirements for two tracks. To satisfy the Computational Data Analytics Track, students must have taken ISYE 6740 as one of their Statistics, Track, or Additional Electives.

**Database Systems Concepts and Design (CS 6400) **

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.

**Artificial Intelligence (CS 6601)**

This course is a survey of the field of Artificial Intelligence and will often be taken as the first graduate course in the area. It is designed to be challenging and involves significant independent work, readings, and assignments. The course covers most of the required textbook Artificial Intelligence A Modern Approach 3rd edition, which is a keystone of Georgia Tech’s Intelligent Systems PhD qualifier exam.

**Knowledge-based AI (CS 7637) **

The twin goals of knowledge-based artificial intelligence (AI) are to build AI agents capable of human-level intelligence and gain insights into human cognition. The learning goals of the Knowledge-Based AI course are to develop an understanding of (1) the basic architectures, representations and techniques for building knowledge-based AI agents, and (2) issues and methods of knowledge-based AI. The main learning strategies are learning-by-example and learning-by-doing. Thus, the course puts a strong emphasis on homework assignments and programming projects. The course will cover three kinds of topics: core topics such as knowledge representation, planning, constraint satisfaction, case-based reasoning, knowledge revision, incremental concept learning, and explanationbased learning; common tasks such as classification, diagnosis, and design; and advanced topics such as analogical reasoning, visual reasoning, and meta-reasoning.

**Reinforcement Learning (CS 7642) **

Reinforcement Learning is a subarea of Machine Learning, that area of Artificial Intelligence that is concerned with computational artifacts that modify and improve their performance through experience. This course focuses on automated computational decision making through a combination of classic papers and more recent work. It examines efficient algorithms, where they exist, for single-agent and multiagent planning as well as approaches to learning nearoptimal decisions from experience. Topics include Markov decision processes; stochastic and repeated games; partially observable Markov decision processes; reinforcement learning; and interactive reinforcement learning. The class is particularly interested in issues of generalization, exploration, and representation.

**Deep Learning (CS 7643) **

Deep learning is a sub-field of machine learning that focuses on learning complex, hierarchical feature representations from raw data. The dominant method for achieving this, artificial neural networks, has revolutionized the processing of data (e.g. images, videos, text, and audio) as well as decision-making tasks (e.g. game-playing). Its success has enabled a tremendous amount of practical commercial applications and has had significant impact on society. In this course, students will learn the fundamental principles, underlying mathematics, and implementation details of deep learning. This includes the concepts and methods used to optimize these highly parameterized models (gradient descent and backpropagation, and more generally computation graphs), the modules that make them up (linear, convolution, and pooling layers, activation functions, etc.), and common neural network architectures (convolutional neural networks, recurrent neural networks, etc.). Applications ranging from computer vision to natural language processing, and decision-making (reinforcement learning) will be demonstrated. Through in-depth programming assignments, students will learn how to implement these fundamental building blocks as well as how to put them together using a popular deep learning library, PyTorch. In the final project, students will apply what they have learned to real-world scenarios by exploring these concepts with a problem that they are passionate about.

**Machine Learning for Trading (CS 7646)**

This course introduces students to the real-world challenges of implementing machine learning based trading strategies including the algorithmic steps from information gathering to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, Q-Learning, KNN, and regression trees and how to apply them to actual stock trading situations.

**Big Data Analytics in Healthcare (CSE 6250)**

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.**Time Series Analysis (ISYE 6402)**

Basic forecasting and methods, ARIMA models, transfer functions.

**Regression Analysis (ISYE 6414)**

Simple and multiple linear regression, inferences and diagnostics, stepwise regression and model selection, advanced regression methods, basic design and analysis of experiments, factorial analysis.

**Bayesian Statistics (ISYE 6420)**

This course covers the fundamentals of Bayesian statistics, including both the underlying models and methods of Bayesian computation, and how they are applied. Modeling topics include conditional probability and Bayes’ formula, Bayesian inference, credible sets, conjugate and noninformative priors, hypothesis testing, Bayesian regression, empirical Bayes models, and hierarchical Bayesian models. Computational topics include Monte Carlo methods, MCMC, Metropolis-Hasting algorithms, Gibbs sampling, variational Bayes, and other methods for posterior approximation. Various applications of Bayesian statistics will be discussed. Prerequisite: Calculus-based Introductory Statistics Course

**Simulation (ISYE 6644)**

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

**Deterministic Optimization (ISYE 6669)**

The course will teach basic concepts, models, and algorithms in linear optimization, integer optimization, and convex optimization. The first module of the course is a general overview of key concepts in linear algebra, calculus, and optimization. The second module of the course is on linear optimization, covering modeling techniques, basic polyhedral theory, simplex method, and duality theory.

**Computational Data Analysis (ISYE 6740)**

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. The course is designed to answer the most fundamental questions about machine learning: What are the most important methods to know about, and why? How can we answer the question 'is this method better than that one' using asymptotic theory? How can we answer the question 'is this method better than that one' for a specific dataset of interest? What can we say about the errors our method will make on future data? What's the 'right' objective function? What does it mean to be statistically rigorous? This course is designed to give graduate students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The course covers topics from machine learning, classical statistics, and data mining. Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be Analytical Tools an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate. Some experience with coding are expected.

**Data Mining and Statistical Learning (ISYE 7406)**

An introduction to some commonly used data mining and statistical learning algorithms such as K-nearest neighbor (KNN) algorithm, linear methods for regression and classification, tree-based methods, ensemble methods, support vector machine, neural networks, and Kmeans clustering algorithm. This course focuses on the understanding of methodology, motivation, and assumptions of different algorithms as well as implementation of these algorithms with data examples using the R statistical software.

**Topics on High-dimensional Data Analytics (ISYE 8803)**

This course focuses on analysis of highdimensional structured data including profiles, images, and other types of functional data using statistical machine learning. A variety of topics such as functional data analysis, image processing, multilinear algebra and tensor analysis, and regularization in high-dimensional regression and its applications including low rank and sparse learning is covered. Optimization methods commonly used in statistical modeling and machine learning and their computational aspects are also discussed - convex conic optimization, which is a significant generalization of linear optimization. The fourth and final module is on integer optimization, which augments the previously covered optimization models with the flexibility of integer decision variables. The course blends optimization theory and computation with various applications to modern data analytics.

**Digital Marketing (MGT 6311)**

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.).

**Financial Modeling (MGT 8813)**

Students will create spreadsheets using pivot tables, Excel functions, solver, goal seek, and VBA. The course will also include topics such as time value of money, stock and bond valuation, firm valuation, financial statements, cost of capital, option pricing models, and portfolio optimization. This course is intended to prepare students to build financial models.

**Data Analysis for Continuous Improvement (MGT 8823)**

Because it is one thing to know how to analyze data and another thing to actually use it to solve problems, the purpose of this course is to show how mastery of data analysis can be applied to the real world. In doing so, we will explore the development of key performance indicators (KPIs) and how to use KPIs to drive improvement in an organization. We will also discuss the four methods of continuous improvement and how data analysis can be leveraged in each method. Because the course weaves examples from both industry and everyday life, what is learned can be directly applied to both your personal and professional lives. In addition to the practical knowledge gained, students will also be shown tools that are outside of the typical analytics course and provided with the knowledge of how to use these tools on their own and in conjunction with the data analysis. Because one must know not only how to analyze data but also how to determine from where the actual data should come, the tools chosen will assist the student in coming up with what to analyze in the first place.

**Privacy for Professionals (MGT 8833)**

Because it is one thing to know how to analyze data and another thing to actually use it to solve problems, the purpose of this course is to show how mastery of data analysis can be applied to the real world. In doing so, we will explore the development of key performance indicators (KPIs) and how to use KPIs to drive improvement in an organization. We will also discuss the four methods of continuous improvement and how data analysis can be leveraged in each method. Because the course weaves examples from both industry and everyday life, what is learned can be directly applied to both your personal and professional lives. In addition to the practical knowledge gained, students will also be shown tools that are outside of the typical analytics course and provided with the knowledge of how to use these tools on their own and in conjunction with the data analysis. Because one must know not only how to analyze data but also how to determine from where the actual data should come, the tools chosen will assist the student in coming up with what to analyze in the first place.

**Applied Analytics Practicum (MGT 6748) **

A practical analytics experience that enables you to apply previously learned concepts and classroom teachings to a project of significant interest, either within your current organization or within a Georgia Tech-sponsored company (partnering companies pre-determine their projects). The objective of the practicum is to properly define and scope the analytics project, apply appropriate methodologies, create value, manage the project, and provide results in writing. The practicum project can be a project at a self-secured internship or work you do for your current employer. If either apply, you still must gain approval to register for (and fulfill the requirements of) the Applied Analytics Practicum course within the semester you are doing the project work. This course is by permit. The prerequisites for registration are completion of at least eight courses, including Data and Visual Analytics (CSE 6242) and Data Analytics in Business (MGT 6203) prior to (not concurrent with) the practicum. Offered: Fall, Spring, Summer

### Computational Data Analytics Track

Students with sufficient background in any of these areas may apply to replace one or more of these courses with an elective.

**Introduction for Computing for Data Analytics (CSE 6040)**

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.**Introduction to Analytics Modeling (ISYE 6501)**

This course gives a basic introduction to a wide variety of analytics models and techniques, including the basic ideas behind the models, experience using software to solve/analyze them, and case studies dealing with combining models to find a complete solution. Modeling approaches covered include classification, clustering, change detection, time series modeling, regression models, design of experiments, probability distributions, probability-based models and simulation, PCA, and optimization. Cross-cutting topics like data preparation, model validation, and variable selection are also covered. **Business Fundamentals for Analytics (MGT 8803/6754)**

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.

**Data and Visual Analytics (CSE 6242)**

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.**Data Analytics in Business (MGT 6203)**

Teaches the scientific process of transforming data into insights for making better business decisions. It covers methodologies, algorithms, and challenges related to analyzing business data. Completion of MGT 6203 is required before starting the practicum. Suggested prerequisite: ISYE 6501

Select at least two courses from the list. All C-track students must take ISYE 6740 as either a Statistics elective or a C-track elective.

**Time Series Analysis (ISYE 6402)**

Basic forecasting and methods, ARIMA models, transfer functions.

**Regression Analysis (ISYE 6414)**

Simple and multiple linear regression, inferences and diagnostics, stepwise regression and model selection

**Bayesian Statistics (ISYE 6420)**

This course covers the fundamentals of Bayesian statistics, including both the underlying models and methods of Bayesian computation, and how they are applied. Modeling topics include conditional probability and Bayes’ formula, Bayesian inference, credible sets, conjugate and noninformative priors, hypothesis testing, Bayesian regression, empirical Bayes models, and hierarchical Bayesian models. Computational topics include Monte Carlo methods, MCMC, Metropolis-Hasting algorithms, Gibbs sampling, variational Bayes, and other methods for posterior approximation. Various applications of Bayesian statistics will be discussed. Prerequisite: Calculus-based Introductory Statistics Course

**Computational Data Analysis (ISYE 6740) ****required for Computational Data Analytics Track*

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. The course is designed to answer the most fundamental questions about machine learning: What are the most important methods to know about, and why? How can we answer the question 'is this method better than that one' using asymptotic theory? How can we answer the question 'is this method better than that one' for a specific dataset of interest? What can we say about the errors our method will make on future data? What's the 'right' objective function? What does it mean to be statistically rigorous? This course is designed to give graduate students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The course covers topics from machine learning, classical statistics, and data mining. Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be Analytical Tools an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate. Some experience with coding are expected.

**Data Mining and Statistical Learning (ISYE 7406)**

Topics include neural networks, support vector machines, classification trees, boosting, and discriminant analyses.

**Topics on High-dimensional Data Analytics (ISYE 8803)**

This course focuses on analysis of highdimensional structured data including profiles, images, and other types of functional data using statistical machine learning. A variety of topics such as functional data analysis, image processing, multilinear algebra and tensor analysis, and regularization in high-dimensional regression and its applications including low rank and sparse learning is covered. Optimization methods commonly used in statistical modeling and machine learning and their computational aspects are also discussed - convex conic optimization, which is a significant generalization of linear optimization. The fourth and final module is on integer optimization, which augments the previously covered optimization models with the flexibility of integer decision variables. The course blends optimization theory and computation with various applications to modern data analytics.

The number of elective hours required depends on how many Introductory Core courses (if any) are replaced with electives.

Students who take two of these additional electives from the Track Electives list of either the Business Analytics track or the Computational Data Analytics track will satisfy the requirements for two tracks. To satisfy the Computational Data Analytics Track, students must have taken ISYE 6740 as one of their Statistics, Track, or Additional Electives.

**Simulation (ISYE 6644)**

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

**Deterministic Optimization (ISYE 6669)**

The course will teach basic concepts, models, and algorithms in linear optimization, integer optimization, and convex optimization. The first module of the course is a general overview of key concepts in linear algebra, calculus, and optimization. The second module of the course is on linear optimization, covering modeling techniques, basic polyhedral theory, simplex method, and duality theory.

Select at least two courses from the list. All C-track students must take ISYE 6740 as either a Statistics elective or a C-track elective.

**Computational Data Analysis (ISYE 6740) ****required for Computational Data Analytics Track*

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. The course is designed to answer the most fundamental questions about machine learning: What are the most important methods to know about, and why? How can we answer the question 'is this method better than that one' using asymptotic theory? How can we answer the question 'is this method better than that one' for a specific dataset of interest? What can we say about the errors our method will make on future data? What's the 'right' objective function? What does it mean to be statistically rigorous? This course is designed to give graduate students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The course covers topics from machine learning, classical statistics, and data mining. Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be Analytical Tools an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate. Some experience with coding are expected.

**Database Systems Concepts and Design (CS 6400)**

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.

**Artificial Intelligence (CS 6601)**

This course is a survey of the field of Artificial Intelligence and will often be taken as the first graduate course in the area. It is designed to be challenging and involves significant independent work, readings, and assignments. The course covers most of the required textbook Artificial Intelligence A Modern Approach 3rd edition, which is a keystone of Georgia Tech’s Intelligent Systems PhD qualifier exam.**Human Computer Interaction (CS 6750) **

This course is an introductory course on human-computer interaction. It does not presuppose any earlier knowledge of human-computer interaction, computer science, or psychology. The class covers three broad categories of topics within human-computer interaction: (a) the principles and characteristics of the interaction between humans and computers; (b) the techniques for designing and evaluating user-centered systems; and (c) current areas of cuttingedge research and development in human-computer interaction.

**Network Science (CS 7280)**

It is often the case that complex systems, both living and man-made, can be represented as static or dynamic networks of many interacting components. These components are typically much simpler in terms of behavior or function than the overall system, implying that the additional complexity of the latter is an emergent network property. Network science is a relatively new discipline that investigates the topology and dynamics of such complex networks, aiming to better understand the behavior, function and properties of the underlying systems. The applications of network science cover physical, informational, biological, cognitive, and social systems. In this course, we will study algorithmic, computational, and statistical methods of network science, as well as various applications in social, communication and biological networks. A significant component of the course will focus on the overlap between machine learning and network science, covering methods for network inference, generative network models, graph embeddings using deep neural networks, and other state of the art topics.

**Knowledge-based AI (CS 7637) **

The twin goals of knowledge-based artificial intelligence (AI) are to build AI agents capable of human-level intelligence and gain insights into human cognition. The learning goals of the Knowledge-Based AI course are to develop an understanding of (1) the basic architectures, representations and techniques for building knowledge-based AI agents, and (2) issues and methods of knowledge-based AI. The main learning strategies are learning-by-example and learning-by-doing. Thus, the course puts a strong emphasis on homework assignments and programming projects. The course will cover three kinds of topics: core topics such as knowledge representation, planning, constraint satisfaction, case-based reasoning, knowledge revision, incremental concept learning, and explanationbased learning; common tasks such as classification, diagnosis, and design; and advanced topics such as analogical reasoning, visual reasoning, and meta-reasoning.

**Reinforcement Learning (CS 7642) **

Reinforcement Learning is a subarea of Machine Learning, that area of Artificial Intelligence that is concerned with computational artifacts that modify and improve their performance through experience. This course focuses on automated computational decision making through a combination of classic papers and more recent work. It examines efficient algorithms, where they exist, for single-agent and multiagent planning as well as approaches to learning nearoptimal decisions from experience. Topics include Markov decision processes; stochastic and repeated games; partially observable Markov decision processes; reinforcement learning; and interactive reinforcement learning. The class is particularly interested in issues of generalization, exploration, and representation

**Deep Learning (CS 7643) **

Deep learning is a sub-field of machine learning that focuses on learning complex, hierarchical feature representations from raw data. The dominant method for achieving this, artificial neural networks, has revolutionized the processing of data (e.g. images, videos, text, and audio) as well as decision-making tasks (e.g. game-playing). Its success has enabled a tremendous amount of practical commercial applications and has had significant impact on society. In this course, students will learn the fundamental principles, underlying mathematics, and implementation details of deep learning. This includes the concepts and methods used to optimize these highly parameterized models (gradient descent and backpropagation, and more generally computation graphs), the modules that make them up (linear, convolution, and pooling layers, activation functions, etc.), and common neural network architectures (convolutional neural networks, recurrent neural networks, etc.). Applications ranging from computer vision to natural language processing, and decision-making (reinforcement learning) will be demonstrated. Through in-depth programming assignments, students will learn how to implement these fundamental building blocks as well as how to put them together using a popular deep learning library, PyTorch. In the final project, students will apply what they have learned to real-world scenarios by exploring these concepts with a problem that they are passionate about.** ****Machine Learning for Trading (CS 7646)**

This course introduces students to the real-world challenges of implementing machine learning based trading strategies including the algorithmic steps from information gathering to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, Q-Learning, KNN, and regression trees and how to apply them to actual stock trading situations.**Big Data Analytics in Healthcare (CSE 6250)**

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.

**Modeling Simulation & Military Gaming (CSE 6742)**

Computer modeling and simulation offers a unique perspective on events because of the ability to hold some variables constant and change others, and run a scenario repeatedly searching for underlying themes. Computer simulation has been used as an analytical tool in the natural sciences, business, commerce, government and politics. This course focuses on the creation and application of computer simulations to model strategic international events concerning warfare. The course is project-based, requiring computing and international affairs students to work together in multidisciplinary teams to analyze specific questions utilizing computer-based modeling and simulation tools (largely, but not exclusively “NetLogo”).

**Applied Natural Language Processing (CSE 8803)**

The primary objective of this course is to introduce you to broad classes of techniques and tools for analyzing text data using Natural Language Processing (NLP) algorithms and techniques. The class will emphasize applying pre-processing, processing, and post-processing NLP techniques to analyze and develop NLP models using conventional and deep learning machine learning methods.

The number of elective hours required depends on how many Introductory Core courses (if any) are replaced with electives.

Students who take two of these additional electives from the Track Electives list of either the Business Analytics track or the Analytical Tools track will satisfy the requirements for two tracks. To satisfy the Computational Data Analytics Track, students must have taken or ISYE 6740 as one of their Statistics, Track, or Additional Electives.

**Big Data Analytics in Healthcare** (CSE 6250) 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.

**Database Systems Concepts and Design** (CS 6400) 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.

**Artificial Intelligence (CS 6601**) This course is a survey of the field of Artificial Intelligence and will often be taken as the first graduate course in the area. It is designed to be challenging and involves significant independent work, readings, and assignments. The course covers most of the required textbook Artificial Intelligence A Modern Approach 3rd edition, which is a keystone of Georgia Tech’s Intelligent Systems PhD qualifier exam.

** Human Computer Interaction (CS 6750)** This course is an introductory course on human-computer interaction. It does not presuppose any earlier knowledge of human-computer interaction, computer science, or psychology. The class covers three broad categories of topics within human-computer interaction: (a) the principles and characteristics of the interaction between humans and computers; (b) the techniques for designing and evaluating user-centered data.

** Network Science (CS 7280)** It is often the case that complex systems, both living and man-made, can be represented as static or dynamic networks of many interacting components. These components are typically much simpler in terms of behavior or function than the overall system, implying that the additional complexity of the latter is an emergent network property. Network science is a relatively new discipline that investigates the topology and dynamics of such complex networks, aiming to better understand the behavior, function and properties of the underlying systems. The applications of network science cover physical, informational, biological, cognitive, and social systems. In this course, we will study algorithmic, computational, and statistical methods of network science, as well as various applications in social, communication and biological networks. A significant component of the course will focus on the overlap between machine learning and network science, covering methods for network inference.

**Knowledge-based AI (CS 7637)** The twin goals of knowledge-based artificial intelligence (AI) are to build AI agents capable of human-level intelligence and gain insights into human cognition. The learning goals of the Knowledge-Based AI course are to develop an understanding of (1) the basic architectures, representations and techniques for building knowledge-based AI agents, and (2) issues and methods of knowledge-based AI. The main learning strategies are learning-by-example and learning-by-doing. Thus, the course puts a strong emphasis on homework assignments and programming projects. The course will cover three kinds of topics: core topics such as knowledge representation, planning, constraint satisfaction, case-based reasoning, knowledge revision, incremental concept learning, and explanation based learning; common tasks such as classification, diagnosis, and design; and advanced topics such as analogical reasoning, visual reasoning, and meta-reasoning.

** Reinforcement Learning (CS 7642**) Reinforcement Learning is a subarea of Machine Learning, that area of Artificial Intelligence that is concerned with computational artifacts that modify and improve their performance through experience. This course focuses on automated computational decision making through a combination of classic papers and more recent work. It examines efficient algorithms, where they exist, for single-agent and multiagent planning as well as approaches to learning near optimal decisions from experience. Topics include Markov decision processes; stochastic and repeated games; partially observable Markov decision processes; reinforcement learning; and interactive reinforcement learning. The class is particularly interested in issues of generalization, exploration, and representation.

**Deep Learning (CS 7643)** Deep learning is a sub-field of machine learning that focuses on learning complex, hierarchical feature representations from raw data. The dominant method for achieving this, artificial neural networks, has revolutionized the processing of data (e.g. images, videos, text, and audio) as well as decision-making tasks (e.g. game-playing). Its success has enabled a tremendous amount of practical commercial applications and has had significant impact on society. In this course, students will learn the fundamental principles, underlying mathematics, and implementation details of deep learning. This includes the concepts and methods used to optimize these highly parameterized models (gradient descent and backpropagation, and more generally computation graphs), the modules that make them up (linear, convolution, and pooling layers, activation functions, etc.), and common neural network architectures (convolutional neural networks, recurrent neural networks, etc.). Applications ranging from computer vision to natural language processing, and decision-making (reinforcement learning) will be demonstrated. Through in-depth programming assignments, students will learn how to implement these fundamental building blocks as well as how to put them together using a popular deep learning library, PyTorch. In the final project, students will apply what they have learned to real-world scenarios by exploring these concepts with a problem that they are passionate about.

** Machine Learning for Trading (CS 7646)** This course introduces students to the real-world challenges of implementing machine learning based trading strategies including the algorithmic steps from information gathering to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, Q-Learning, KNN, and regression trees and how to apply them to actual stock trading situations. Modeling,

**Simulation, & Military Gaming (CSE 6742)** Computer modeling and simulation offers a unique perspective on events because of the ability to hold some variables constant and change others and run a scenario repeatedly searching for underlying themes. Computer simulation has been used as an analytical tool in the natural sciences, business, commerce, government, and politics. This course focuses on the creation and application of computer simulations to model strategic international events concerning warfare. The course is project-based, requiring computing and international affairs students to work together in multidisciplinary teams to analyze specific questions utilizing computer-based modeling and simulation tools (largely, but not exclusively “Net Logo”).

**Applied Natural Language Processing (CSE 8803)** The primary objective of this course is to introduce you to broad classes of techniques and tools for analyzing text data using Natural Language Processing (NLP) algorithms and techniques. The class will emphasize applying pre-processing, processing, and post-processing NLP techniques to analyze and develop NLP models using conventional and deep learning machine learning methods.

**Time Series Analysis (ISYE 6402)** Basic forecasting and methods, ARIMA models, transfer functions.

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

**Bayesian Statistics (ISYE 6420)** This course covers the fundamentals of Bayesian statistics, including both the underlying models and methods of Bayesian computation, and how they are applied. Modeling topics include conditional probability and Bayes’ formula, Bayesian inference, credible sets, conjugate and noninformative priors, hypothesis testing, Bayesian regression, empirical Bayes models, and hierarchical Bayesian models. Computational topics include Monte Carlo methods, MCMC, Metropolis-Hasting algorithms, Gibb’s sampling, variational Bayes, and other methods for posterior approximation. Various applications of Bayesian statistics will be discussed. Prerequisite: Calculus based Introductory Statistics Course.

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

**Deterministic Optimization (ISYE 6669)** The course will teach basic concepts, models, and algorithms in linear optimization, integer optimization, and convex optimization. The first module of the course is a general overview of key concepts in linear algebra, calculus, and optimization. The second module of the course is on linear optimization, covering modeling techniques, basic polyhedral theory, simplex method, and duality theory.

**Computational Data Analysis (ISYE 6740)** Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. The course is designed to answer the most fundamental questions about machine learning: What are the most important methods to know about, and why? How can we answer the question 'is this method better than that one' using asymptotic theory? How can we answer the question 'is this method better than that one' for a specific dataset of interest? What can we say about the errors our method will make on future data? What's the 'right' objective function? What does it mean to be statistically rigorous? This course is designed to give graduate students a thorough grounding in the methods, theory, mathematics, and algorithms needed to do research and applications in machine learning. The course covers topics from machine learning, classical statistics, and data mining. Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be Analytical Tools an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate. Some experience with coding are expected.

** Data Mining and Statistical Learning (ISYE 7406)** An introduction to some commonly used data mining and statistical learning algorithms such as KNearest neighbor (KNN) algorithm, linear methods for regression and classification, tree-based methods, ensemble methods, support vector machine, neural networks, and Kmeans clustering algorithm. This course focuses on the understanding of methodology, motivation, and assumptions of different algorithms as well as implementation of these algorithms with data examples using the R statistical software.

** Topics on High-dimensional Data Analytics (ISYE 8803)** This course focuses on analysis of high dimensional structured data including profiles, images, and other types of functional data using statistical machine learning. A variety of topics such as functional data analysis, image processing, multilinear algebra and tensor analysis, and regularization in high-dimensional regression and its applications including low rank and sparse learning is covered. Optimization methods commonly used in statistical modeling and machine learning and their computational aspects are also discussed - convex conic optimization, which is a significant generalization of linear optimization. The fourth and final module is on integer optimization, which augments the previously covered optimization models with the flexibility of integer decision variables. The course blends optimization theory and computation with various applications to modern data analytics.

** Digital Marketing (MGT 6311)** 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.).

**Financial Modeling (MGT 8813)** Students will create spreadsheets using pivot tables, Excel functions, solver, goal seek, and VBA. The course will also include topics such as time value of money, stock and bond valuation, firm valuation, financial statements, cost of capital, option pricing models, and portfolio optimization. This course is intended to prepare students to build financial models.

** Data Analysis for Continuous Improvement (MGT 8823**) Because it is one thing to know how to analyze data and another thing to use it to solve problems, the purpose of this course is to show how mastery of data analysis can be applied to the real world. In doing so, we will explore the development of key performance indicators (KPIs) and how to use KPIs to drive improvement in an organization. We will also discuss the four methods of continuous improvement and how data analysis can be leveraged in each method. Because the course weaves examples from both industry and everyday life, what is learned can be directly applied to both your personal and professional lives. In addition to the practical knowledge gained, students will also be shown tools that are outside of the typical analytics course and provided with the knowledge of how to use these tools on their own and in conjunction with the data analysis. Because one must know not only how to analyze data but also how to determine from where the actual data should come, the tools chosen will assist the student in coming up with what to analyze in the first place.

**Privacy for Professionals (MGT 6727)** Because it is one thing to know how to analyze data and another thing to use it to solve problems, the purpose of this course is to show how mastery of data analysis can be applied to the real world. In doing so, we will explore the development of key performance indicators (KPIs) and how to use KPIs to drive improvement in an organization. We will also discuss the four methods of continuous improvement and how data analysis can be leveraged in each method. Because the course weaves examples from both industry and everyday life, what is learned can be directly applied to both your personal and professional lives. In addition to the practical knowledge gained, students will also be shown tools that are outside of the typical analytics course and provided with the knowledge of how to use these tools on their own and in conjunction with the data analysis. Because one must know not only how to analyze data but also how to determine from where the actual data should come, the tools chosen will assist the student in coming up with what to analyze in the first place.

** Information Security Policies and Strategies (PUBP 6725)** This course introduces students to the policy and management aspects of cybersecurity. It is based on the idea that cybersecurity policy can be sorted into three “layers” representing different levels of social organization: individual organization, the national level, and the transnational level. The course is divided into four modules: the first exposes students to basic concepts and definitions regarding policy, governance, and threats; the second deals with cybersecurity policy at the organizational level; the third deals with cybersecurity public policy at the national level; the fourth deals with cyber conflict, policy, and diplomacy at the transnational level. This course situates cybersecurity in the overall Internet ecosystem.

**Applied Analytics Practicum (CSE 6748)**

A practical analytics experience that enables you to apply previously learned concepts and classroom teachings to a project of significant interest, either within your current organization or within a Georgia Tech sponsored company (partnering companies pre-determine their projects). The objective of the practicum is to properly define and scope the analytics project, apply appropriate methodologies, create value, manage the project, and provide results in writing. The practicum project can be a project at a self-secured internship or work you do for your current employer. If either apply, you still must gain approval to register for (and fulfill the requirements of) the Applied Analytics Practicum course within the semester you are doing the project work. This course is by permit. The prerequisites for registration are completion of at least eight courses, including Data and Visual Analytics (CSE 6242) and Data Analytics in Business (MGT 6203) prior to (not concurrent with) the practicum. Offered: Fall, Spring, Summer

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