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)
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.

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.

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.

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.

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.

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 (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.

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.

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.

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.

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.

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)

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 (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.

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.

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.

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.

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 

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.

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 (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.