Analytics (ANLY)

ANLY 2300. Introduction to Data Analytics.

This course introduces fundamental data science and analytics concepts alongside their diverse practical applications. Students learn to use visualization software while exploring the essential mechanics of data wrangling, descriptive, predictive, and prescriptive analytical models. The curriculum also covers critical topics such as data governance and the significant societal implications of analytics. By focusing on the development of data storytelling, students learn to communicate effectively. This comprehensive approach ensures learners can navigate technical challenges while understanding the broader organizational landscape of modern data in a professional environment.

ANLY 2333. Business Statistics.

This course covers essential descriptive and inferential statistical techniques designed specifically for effective business and economic decision-making. Students explore key topics including measures of central tendency and dispersion, probability distributions, sampling distributions, and confidence intervals. The curriculum also delves into hypothesis testing, simple linear regression, and correlation analysis. By using these quantitative methods, students develop the ability to analyze data accurately and interpret statistical results within professional contexts. This foundational knowledge empowers students to make evidence-based decisions and solve organizational challenges efficiently. Prerequisite: [ISAN 1325 or ISAN 1323] and [MATH 1329 or MATH 2471] both with grades of "D" or better.

ANLY 3314. Decision Analytics.

This course introduces the theory, algorithms, and practical applications of decision-making methods used for analyzing and solving business problems. Students explore diverse quantitative approaches, including linear programming, integer programming, network optimization, and simulation. The curriculum also examines decision models designed to navigate uncertainty within professional contexts. By focusing on these analytical techniques, learners develop the skills necessary to model organizational systems and evaluate alternative strategies effectively. This foundation allows students to provide rigorous, data-driven support for critical management decisions within modern organizations. Prerequisite: ANLY 2333 or MATH 2328 or [ANLY 2300 and [GEO 3301 or PSY 2301 or IE 3320 or SOCI 3307 or PH 3315]] with a grade of "D" or better and a minimum 2.0 Overall GPA.

ANLY 3330. Introduction to Business Analytics.

This course introduces fundamental business analytics concepts alongside diverse practical applications. Students learn to use visualization software while exploring the essential mechanics of data wrangling and various analytical models. The curriculum also examines the ethical and societal implications of data usage within modern professional environments. By focusing on the development of data storytelling, students learn to communicate complex findings effectively. This comprehensive approach ensures learners can navigate technical challenges while understanding the broader organizational landscape of data-driven decision-making.

ANLY 3334. Statistical Modeling.

This course allows students to apply a broad range of statistical analysis techniques using software for business decision-making. Students explore essential topics including probabilistic simulation, general and generalized linear models, and time series forecasting. Significant emphasis is placed on model formulation, interpretation, and validation within real-world contexts. By focusing on these quantitative methods, learners develop the skills necessary to analyze complex datasets and support organizational objectives. This comprehensive approach ensures that students provide rigorous, data-driven insights. Prerequisite: ANLY 2333 or MATH 2328 with a grade of "D" or better and a minimum 2.0 Overall GPA.

ANLY 3339. Data Mining and Visualization.

This course introduces fundamental data mining concepts and practical skills for applying advanced techniques to solve complex business problems. Students explore essential topics including data visualization, various analysis algorithms such as prediction, classification, clustering and corresponding performance metrics. Significant emphasis is placed on model assessment specifically for big data sets within professional contexts. By focusing on these quantitative methods, learners develop the ability to analyze large-scale information and interpret results effectively to support strategic, data-driven organizational decisions. Prerequisite: ANLY 2333 or MATH 2328 or [ANLY 2300 and [GEO 3301 or PSY 2301 or IE 3320 or SOCI 3307 or PH 3315]] with a grade of "D" or better and a minimum 2.0 Overall GPA.

ANLY 3341. Computational Methods for Analytics.

This course provides a comprehensive introduction to statistical programming, focusing on the use of programming tools and quantitative methods to analyze large datasets. Students explore key topics including the creation of graphs for statistical analysis, rigorous statistical modeling, and diverse visualization techniques. The curriculum also covers simulation and optimization methods essential for modern data analytics. By learning these programming skills, students develop the ability to process extensive information and derive meaningful insights to support evidence-based decision-making. Prerequisite: ANLY 2333 or QMST 2333 or MATH 2328 or [ANLY 2300 or QMST 2300 and [GEO 3301 or PSY 2301 or IE 3320 or SOCI 3307 or PH 3315]] with a grade of "D" or better and a minimum 2.0 Overall GPA.

ANLY 4320. Analytics in Practice.

This course covers the practical application of analytical methods within modern business practice. Students work on a comprehensive applied project encompassing critical stages: problem understanding, data preparation, model building, and validation. Significant emphasis is placed on the effective communication of findings to diverse stakeholders. By navigating the full analytics lifecycle, learners develop the skills necessary to transform raw data into actionable business decisions. This hands-on approach ensures students are able to successfully manage complex projects and provide data-driven solutions for real-world organizational challenges. Prerequisite: [ANLY 3334 OR ANLY 3339] AND [ANLY 3341 OR ISAN 3305] with grades of "D" or better and a minimum 2.0 Overall GPA.

ANLY 4321. Predictive Analytics.

This course covers the use of statistical learning methods, specifically focusing on regression and classification to solve business problems. Students explore feature selection and shrinkage techniques, including ridge and lasso regression. The curriculum also examines deep neural network learning, ensemble methods like bagging and boosting, and foundational concepts such as entropy. Throughout the course, the bias-variance trade-off is emphasized. By learning these sophisticated tools, students develop the ability to build predictive models that enhance strategic decision-making within organizational environments. Prerequisite: ANLY 3339 with a grade of "D" or better and a minimum 2.0 Overall GPA.

ANLY 4373A. Operations Analytics.

This course introduces analytical methods and quantitative tools used in the planning, design, and management of business operations. The course integrates data-driven decision making with operational theory, emphasizing forecasting, service and queueing systems, optimization modeling, decision analysis, simulation, and quantitative supply chain analytics. Students examine real-world operational contexts such as inventory control, logistics and distribution planning, process and capacity analysis, and quality management. Through applied problem-solving and computational tools, the course develops analytical reasoning and practical modeling skills relevant to contemporary operations and supply chain environments. Prerequisite: A minimum 2.0 Overall GPA.

ANLY 4373F. Big Data Analysis and Artificial Intelligence.

This course covers the utilization of analytical and artificial intelligence methods alongside big data to solve complex business problems. Students explore high-performance computing, big data storage, and advanced analysis techniques. The curriculum introduces distributed and parallel programming designed to increase throughput and reduce latency for selected applications. By learning these sophisticated technologies, students develop the skills necessary to manage massive datasets and optimize computational efficiency. This foundation ensures students develop the abilities to provide high-speed, scalable solutions within modern, data-intensive organizational environments. Prerequisite: [ISAN 3305 OR ANLY 3341] AND ISAN 3382 AND ANLY 4321 with a grade of "D" or better and a minimum 2.0 Overall GPA.

ANLY 4395. Independent Study in Analytics.

This course provides an in-depth, faculty-supervised study focusing on a selected analytics topic or a specific applied problem. Students conduct independent research or project-based analysis using advanced analytical methods and sophisticated technical tools. This rigorous exploration allows for the practical application of theoretical knowledge to unique business challenges. The course may be repeated once for credit, provided the student selects a different area of emphasis. By engaging in this supervised study, learners develop specialized expertise and professional-grade problem-solving capabilities. Prerequisite: Instructor Approval.

ANLY 4399. Analytics Internship.

This course provides a comprehensive integration of professional and academic experience through a structured internship with an external employer. Students apply knowledge to real-world business challenges within a professional organizational setting. This practical engagement fosters the development of essential industry skills while building valuable career networks. By completing this internship, students gain significant workplace insights that bridge the gap between advanced analytical studies and professional practice. Academic credit for this experiential learning opportunity is awarded on a pass/fail basis. Prerequisite: Instructor Approval.

ANLY 5199B. Thesis.

This course represents a graduate student’s initial enrollment in a master’s thesis sequence. Students begin formal thesis work under the supervision of a faculty thesis committee by identifying a research topic, reviewing relevant scholarly literature, and developing an approved research proposal. The course establishes the foundation for subsequent thesis research and writing in the data analytics field. No thesis credit is awarded until the thesis is completed, approved, and submitted for binding. The course is graded on a credit (CR), progress (PR), or no credit (F) basis.

ANLY 5299B. Thesis.

This course represents a graduate student’s initial enrollment in the master’s thesis. Students begin formal thesis work under the supervision of a faculty thesis committee by identifying a research topic, reviewing relevant scholarly literature, and developing an approved research proposal. The course establishes the foundation for subsequent thesis research and writing in the data analytics field. No thesis credit is awarded until the thesis is completed, approved, and submitted for binding. The course is graded on a credit (CR), progress (PR), or no credit (F) basis.

ANLY 5330. Statistical Computing.

This course explores the intersection of programming and computational techniques essential for rigorous statistical analysis. Students master data manipulation, complex data structures, and algorithmic development alongside the mathematical foundations of matrix operations and numerical linear algebra. The course examines Monte Carlo simulations and numerical optimization as foundational methods for statistical modeling. Students develop an understanding of how computational procedures and numerical methods support advanced analytics and machine learning applications.

ANLY 5332. Optimization for Business Analytics.

This course introduces optimization theory and methods for modeling, analyzing, and solving complex business decision-making problems. Emphasis is placed on formulating real-world managerial problems as mathematical optimization models and applying appropriate solution techniques. Topics include linear programming, network optimization, integer and mixed-integer programming, nonlinear optimization, and selected advanced topics such as multi-objective, stochastic, and robust optimization.

ANLY 5334. Statistical Methods for Business.

This course provides a comprehensive quantitative foundation for business analytics and data-driven decision-making. Students explore essential topics such as inferential statistics, regression analysis, and various statistical modeling techniques used to solve complex business problems across functional areas. Significant emphasis is placed on understanding core statistical concepts, applying appropriate methods, and interpreting results within real-world business contexts. The curriculum focuses on analytical reasoning and evidence-based evaluation rather than prescriptive managerial conclusions, ensuring learners can critically assess data to support organizational objectives.

ANLY 5335. Forecasting and Simulation.

This course covers forecasting and simulation methods designed to analyze uncertainty and support organizational planning and decision-making. Students explore time series forecasting, causal forecasting, and both discrete-event and continuous-event simulation. Significant emphasis is placed on understanding model assumptions, selecting appropriate techniques, and interpreting results within diverse business contexts. The curriculum focuses on rigorous analytical modeling and evaluation rather than prescriptive managerial outcomes. By mastering these quantitative methods, students develop the skills necessary to navigate complex predictive scenarios.

ANLY 5336. Analytics.

This course covers analytics as the essential process of transforming raw data into actionable information to support strategic decision-making. Students explore foundational analytics concepts, data visualization, various applications, and the inherent challenges associated with modern business data. Participants develop practical skills in using analytical software, performing rigorous data analysis, and communicating results effectively. Emphasis is placed on analytical reasoning, the interpretation of complex data, and the clear presentation of insights within business contexts, ensuring students can drive organizational value through evidence.

ANLY 5337. Supply Chain Analytics.

This course examines the application of data analytics tools and quantitative methods to analyze supply chain performance at strategic, tactical, and operational levels. Topics include performance measurement, demand planning, inventory management, logistics optimization, and supply chain risk analysis from an analytics perspective. Students use statistical analysis, optimization, and simulation techniques to analyze data and support decision-making across integrated supply chain processes. Prerequisite: ANLY 5334 with a "C" or better. Corequisite: ANLY 5335 with a grades of a "C" or better.

ANLY 5338. Operations Management.

This course introduces the concepts and strategies used to design, manage, and continuously improve the processes that create and deliver goods and services. The course examines operational and tactical challenges organizations face and explores both qualitative and quantitative approaches to addressing them. Students analyze how process decisions influence organizational performance while considering emerging technologies, digital transformation, and data-enabled operational practices across diverse organizational settings.

ANLY 5339. Analytics Applications in Supply Chain Management.

This course examines the application of descriptive, predictive, and prescriptive analytics within various supply chain management contexts. Students analyze complex case studies and diverse datasets to evaluate planning, coordination, and operational challenges across global supply chain processes. Significant emphasis is placed on applying analytical techniques, artificial intelligence methods, and advanced software tools to model systems, interpret results, and assess alternative approaches. The curriculum focuses on rigorous analytical reasoning and evidence-based evaluation rather than prescriptive managerial decisions. Prerequisite: ANLY 5337 with a grade of a "C" or better.

ANLY 5342. Probability and Statistical Models.

This course covers probability theory and statistical modeling techniques essential for advanced data analysis. Students explore probability distributions, general and generalized linear models, mixture and hierarchical models, and various related extensions. Significant emphasis is placed on rigorous model formulation, interpretation, selection, and validation. The curriculum focuses on understanding the underlying assumptions and inherent limitations of statistical models while applying appropriate methods to analyze complex datasets. By mastering these concepts, students develop the analytical skills necessary to extract meaningful insights from sophisticated data structures.

ANLY 5343. Data Mining.

This course examines data mining concepts and techniques used to analyze large, complex datasets. Students explore key topics including classification, clustering, association analysis, and text mining. Significant emphasis is placed on understanding algorithmic foundations, model selection, and performance assessment. Students apply these data mining methods to analyze real-world datasets and interpret results within applied analytics contexts. Throughout the curriculum, students pay close attention to methodological assumptions and limitations, ensuring a robust and critical approach to extracting meaningful patterns from massive amounts of data. Prerequisite: ANLY 5336 with a grade of "C" or better.

ANLY 5369. Independent Study in Analytics.

This course provides an opportunity for faculty-supervised independent study in a selected area of analytics or quantitative methods. Students pursue in-depth research or applied project work focused on a specialized topic of interest, using appropriate analytical tools and techniques. Emphasis is placed on independent inquiry, methodological rigor, and critical evaluation of results. The course may be completed individually or in small teams and may be repeated with departmental approval when the topic or analytical focus differs. Prerequisite: Instructor approval.

ANLY 5395. Internship in Analytics.

This course provides supervised experiential learning through an approved internship in analytics or quantitative methods. Students apply analytical concepts, tools, and techniques in a professional setting while reflecting on the relationship between academic training and workplace practice. Emphasis is placed on integrating professional experience with analytical reasoning, documentation, and communication of work performed. The internship is completed with an external organization under faculty supervision. Prerequisite: Instructor approval.

ANLY 5399A. Thesis.

This course represents a graduate student’s initial enrollment in the master’s thesis. Students begin formal thesis work under the supervision of a faculty thesis committee by identifying a research topic, reviewing relevant scholarly literature, and developing an approved research proposal. The course establishes the foundation for subsequent thesis research and writing in the data analytics field. No thesis credit is awarded until the thesis is completed, approved, and submitted for binding. The course is graded on a credit (CR), progress (PR), or no credit (F) basis.

ANLY 5399B. Thesis.

This course represents a graduate student’s initial enrollment in the master’s thesis. Students begin formal thesis work under the supervision of a faculty thesis committee by identifying a research topic, reviewing relevant scholarly literature, and developing an approved research proposal. The course establishes the foundation for subsequent thesis research and writing in the data analytics field. No thesis credit is awarded until the thesis is completed, approved, and submitted for binding. The course is graded on a credit (CR), progress (PR), or no credit (F) basis.

ANLY 5599B. Thesis.

This course represents a graduate student’s initial enrollment in the master’s thesis. Students begin formal thesis work under the supervision of a faculty thesis committee by identifying a research topic, reviewing relevant scholarly literature, and developing an approved research proposal. The course establishes the foundation for subsequent thesis research and writing in Data Analytics field. No thesis credit is awarded until the thesis is completed, approved, and submitted for binding. The course is graded on a credit (CR), progress (PR), or no credit (F) basis.

ANLY 5999B. Thesis.

This course represents a graduate student’s initial enrollment in the master’s thesis. Students begin formal thesis work under the supervision of a faculty thesis committee by identifying a research topic, reviewing relevant scholarly literature, and developing an approved research proposal. The course establishes the foundation for subsequent thesis research and writing in Data Analytics field. No thesis credit is awarded until the thesis is completed, approved, and submitted for binding. The course is graded on a credit (CR), progress (PR), or no credit (F) basis.