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Online Master of Science in Analytics

Fall 2019 decisions will be finalized mid-June

Curriculum

There's no better place to get your education in a field that requires computing, business, statistics, and research, than a university which ranks in the top 10 in all four categories — a place like Georgia Tech. In our Online Master of Science in Analytics (OMS Analytics) program you'll have access to 23 different courses to complete your education.

Designed to be completed in one-to-two years, our OMS Analytics consists of 15 hours of core coursework on big data analytics in business, visual analytics, computing, statistics, and operations research essentials. An additional 15 hours of electives will provide you with the flexibility to focus on your specific areas of interest and are selected from one of three tracks.

Six-Hour Practicum
Just like students in our Master of Science Analytics program on-campus, you'll get the opportunity to develop real-world analytics experience potentially working with your own company through our six-credit-hour applied analytics practicum. You can propose a project using data from your own employer or you can secure the experience through one of our pre-selected projects.

Analytical Tools Track

The Analytical Tools track focuses on the quantitative methodology: how to select, build, solve and analyze models using methodology, regression, forecasting, data mining, machine learning, optimization, stochastics, and simulation.
Introductory Core Requirements (9 hours)
Special Note

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)
An introduction to important and commonly used models in Analytics, as well as aspects of the modeling process.

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.

Advanced Core Requirements (6 hours)

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 the methodologies, algorithms, and challenges related to analyzing business data.

Statistics Electives (6 hours)
Special Note

Select two courses from the list.

Machine Learning/Computational Data Analytics (CS 7641 or CSE/IYSE 6740)
Machine learning techniques and applications. Topics include foundational issues; inductive, analytical, numerical, and theoretical approaches; and real-world applications.

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

Nonparametric Data Analysis (ISYE 6404)
Nonparametric statistics and basic categorical data analysis.

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

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.

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

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

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

Operations Elective (3 hours)
Special Note

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.

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

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

Track Electives (6 hours)
Special Note

Select at least two courses from the list.

Machine Learning/Computational Data Analytics (CS 7641 or CSE/IYSE 6740)
Machine learning techniques and applications. Topics include foundational issues; inductive, analytical, numerical, and theoretical approaches; and real-world applications.

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

Nonparametric Data Analysis (ISYE 6404)
Nonparametric statistics and basic categorical data analysis.

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

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.

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

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

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

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

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

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

Additional Electives (0-9 hours)
Special Note

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 CS 7641 or CSE/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.

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

Machine Learning/Computational Data Analytics (CS 7641 or CSE/IYSE 6740)
Theoretical/computational foundations of analyzing large/complex modern datasets, including the fundamental concepts of machine learning and data mining needed for both research and practice Cross-listed with CSE 6740.

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

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

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.

Nonparametric Data Analysis (ISYE 6404)
Nonparametric statistics and basic categorical data analysis.

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

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.

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

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

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

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

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

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

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.).

Data Analysis for Continuous Improvement (MGT 8803)

Financial Modeling (MGT 8803)

Applied Analytics Practicum (6 hours)

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 partner 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 an internship or work you do for your current employer. If either apply, you still must register for (and fulfill the requirements of) the Applied Analytics Practicum course within the semester you are doing the project work. 

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.

More

Business Analytics Track

The Business Analytics track explores the understanding, framing, and solution of problems in marketing, operations, finance, management of information technology, human resources, and accounting in order to develop and execute analytics projects within businesses.
Introductory Core Requirements (9 hours)
Special Note

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)
An introduction to important and commonly used models in Analytics, as well as aspects of the modeling process.

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.

Advanced Core Requirements (6 hours)

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 the methodologies, algorithms, and challenges related to analyzing business data.

Statistics Electives (6 hours)
Special Note

Select two courses from the list.

Machine Learning/Computational Data Analytics (CS 7641 or CSE/IYSE 6740)
Machine learning techniques and applications. Topics include foundational issues; inductive, analytical, numerical, and theoretical approaches; and real-world applications.

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

Nonparametric Data Analysis (ISYE 6404)
Nonparametric statistics and basic categorical data analysis.

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

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.

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

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

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

Operations Research Electives (3 hours)
Special Note

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.

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

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

Track Electives (6 hours)
Special Note

Select at least two courses from the list.

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.).

Data Analysis for Continuous Improvement (MGT 8803)

Financial Modeling (MGT 8803)

Additional Electives (0-9 hours)
Special Note

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 CS 7641 or CSE/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.

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

Machine Learning/Computational Data Analytics (CS 7641 or CSE/IYSE 6740)
Theoretical/computational foundations of analyzing large/complex modern datasets, including the fundamental concepts of machine learning and data mining needed for both research and practice Cross-listed with CSE 6740.

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

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

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.

Nonparametric Data Analysis (ISYE 6404)
Nonparametric statistics and basic categorical data analysis.

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

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.

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

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

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

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

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

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

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.).

Data Analysis for Continuous Improvement (MGT 8803)

Financial Modeling (MGT 8803)

Applied Analytics Practicum (6 hours)

Applied Analytics Practicum (MGT 6748)
Practical analytics project experience applying ideas from the classroom to a significant project of interest to a business, government agency, or other organization.

More

Computational Data Analytics Track

The Computational Data Analytics track explores a deeper understanding of big data—including how to acquire, preprocess, store, manage, analyze, and visualize large datasets.
Introductory Core Requirements (9 hours)
Special Note

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)
An introduction to important and commonly used models in Analytics, as well as aspects of the modeling process.

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.

Advanced Core Requirements (6 hours)

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 the methodologies, algorithms, and challenges related to analyzing business data.

Statistics Electives (6 hours)
Special Note

Select two courses from the list. All C-track students must take CSE/ISYE 6740 or CS 7641 as either a Statistics elective or a C-track elective.

Machine Learning/Computational Data Analytics (CS 7641 or CSE/IYSE 6740)
Machine learning techniques and applications. Topics include foundational issues; inductive, analytical, numerical, and theoretical approaches; and real-world applications.

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

Nonparametric Data Analysis (ISYE 6404)
Nonparametric statistics and basic categorical data analysis.

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

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.

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

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

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

Operations Research Elective (3 hours)
Special Note

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.

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

Deterministic Optimization (ISYE 6669)
An introduction to deterministic optimization methodologies including approaches from 

Track Electives (6 hours)
Special Note

Select at least two courses from the list. All C-track students must take CSE/ISYE 6740 or CS 7641 as either a Statistics elective or a C-track elective.

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.

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

Machine Learning/Computational Data Analytics (CS 7641 or CSE/IYSE 6740)
Machine learning techniques and applications. Topics include foundational issues; inductive, analytical, numerical, and theoretical approaches; and real-world applications.

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

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

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.

Additional Electives (0-9 hours)
Special Note

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 CS 7641 or CSE/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.

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

Machine Learning/Computational Data Analytics (CS 7641 or CSE/IYSE 6740)
Theoretical/computational foundations of analyzing large/complex modern datasets, including the fundamental concepts of machine learning and data mining needed for both research and practice Cross-listed with CSE 6740.

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

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

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.

Nonparametric Data Analysis (ISYE 6404)
Nonparametric statistics and basic categorical data analysis.

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

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.

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

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

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

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

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

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

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.).

Data Analysis for Continuous Improvement (MGT 8803)

Financial Modeling (MGT 8803)

Applied Analytics Practicum (6 hours)

Applied Analytics Practicum (CSE 6748)
Practical analytics project experience applying ideas from the classroom to a significant project of interest to a business, government agency, or other organization.

More

Questions?

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