Online Master of Science in Analytics

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 

Bayesian Statistics (ISYE 6420)

Computational Data Analysis (ISYE 6740)

Deterministic Optimization (ISYE 6669)

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
 
Simulation (ISYE 6644)

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)

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.

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)

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)

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)

Human Computer Interaction (CS 6750) 

Knowledge-based AI (CS 7637) 

Reinforcement Learning (CS 7642) 

Deep Learning (CS 7643)  

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.

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)

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)

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)

Privacy for Professionals (MGT 8833)

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

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

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)

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)

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.

Data Analysis for Continuous Improvement (MGT 8823)

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)

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)

Knowledge-based AI (CS 7637) 

Reinforcement Learning (CS 7642) 

Deep Learning (CS 7643) 

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)

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)

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)

Privacy for Professionals (MGT 8833)

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

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 

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)

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)

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)

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)

Human Computer Interaction (CS 6750) 

Network Science (CS 7280)

Knowledge-based AI (CS 7637) 

Reinforcement Learning (CS 7642) 

Deep Learning (CS 7643) 

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)

Applied Natural Language Processing (CSE 8803)

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)

Human Computer Interaction (CS 6750) 

Knowledge-based AI (CS 7637) 

Reinforcement Learning (CS 7642) 

Deep Learning (CS 7643) 

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)

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)

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)

Privacy for Professionals (MGT 8833)

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