• 400. Mathematics and Statistics for Analytics

    Units: 2

    Lecture, three hours (five weeks). Limited to Master of Science in Business Analytics students. Review of mathematics, statistics, and probability concepts utilized in business analytics. Topics include basics of calculus, linear algebra, probability, and statistics. S/U or letter grading.

  • 401. R Programming Essentials

    Units: 2

    Lecture, three hours (five weeks). Limited to Master of Science in Business Analytics students. Basics of R programming language as required to succeed as data scientists. Emphasis on how to extend language by function programming and package development. Introduction to scientific document creation and reproducible research in R environment. S/U or letter grading.

  • 402. SQL and Basic Data Management

    Units: 2

    Lecture, three hours (five weeks). Limited to Master of Science in Business Analytics students. Introduction to and practice in Structured Query Language (SQL) syntax and constructs pertaining to data definitions, data manipulation, and data controls in relational databases using MySQL; and important concepts of data management including data analysis and modeling for relational database management systems (RDBMS). S/U or letter grading.

  • 403. Optimization

    Units: 2

    Lecture, three hours (five weeks). Limited to Master of Science in Business Analytics students. Introductory course in optimization. Introduction to modeling and spreadsheet modeling, linear programming, logistics and network programming, integer programming, and non-linear programming. Emphasis on model building and solving problems using Excel-based solvers. S/U or letter grading.

  • 404. Business Fundamentals for Analytics

    Units: 4

    Lecture, three hours. Limited to Master of Science in Business Analytics students. Application of economic, financial, and marketing principles to key management decisions within organizations. Analytical tools for better understanding of external business environment in which organizations operate. S/U or letter grading.

  • 405. Data Management

    Units: 4

    Lecture, three hours. Limited to Master of Science in Business Analytics students. Tactics and strategies related to managing, manipulating, storing, and delivering data. Letter grading.

  • 406. Prescriptive Models and Data Analytics

    Units: 4

    Lecture, three hours. Limited to Master of Science in Business Analytics students. Fundamental tools in data analytics, including experimental design and analysis, regression analysis, and model design, and how to implement these approaches using statistical analysis package R. S/U or letter grading.

  • 407. Data Analytics Industry Seminar I

    Units: 2

    Seminar, 90 minutes to three hours. Required of Master of Science in Business Analytics students. Industry guest speaker presentations. S/U or letter grading.

  • 408. Operations Analytics

    Units: 4

    Lecture, three hours. Limited to Master of Science in Business Analytics students. How business analytics can be used to optimize internal processes and resources. Applications and cases that illustrate quantitative techniques and show how to build operational competitive edge based on business analytics. S/U or letter grading.

  • 409. Competitive Analytics

    Units: 4

    Lecture, three hours. Limited to Master of Science in Business Analytics students. Application of data analytics to examine competitive conditions in industry or market. S/U or letter grading.

  • 410. Customer Analytics

    Units: 4

    Lecture, three hours. Limited to Master of Science in Business Analytics students. Analysis of customer data to make better marketing decisions using real-world cases, exercises, and projects to aggregate theories, frameworks, and methods. Estimation of demand-side models that describe, understand, and estimate aspects of consumers' decision-making process. Introduction to marketing-mix models and consumer-choice models. S/U or letter grading.

  • 411. Fieldwork/Research in Business Analytics

    Units: 4

    Fieldwork, eight hours. Preparation: one term of Master of Science in Business Analytics program. Limited to Master of Science in Business Analytics students. Internship with company in proposed area of study. Regular activity reports to faculty adviser. S/U or letter grading.

  • 412. Business Analytics Supervised Project

    Units: 2

    Fieldwork, three hours (five weeks). Limited to Master of Science in Business Analytics students. Hands-on applied analytics project that helps prepare students for career in quantitative analysis and data science by testing their ability to solve complex analytical business problems in real-world settings. Students hone their communication skills and delve deeply into area of interest beyond classroom. Students learn strategy, business consulting, entrepreneurship, business plan development, primary research collection and analysis, market assessment, financial analysis, and planning. S/U or letter grading.

  • 413. Industry Seminar II

    Units: 2

    Seminar, 90 minutes to three hours. Required of Master of Science in Business Analytics students. Industry guest speaker presentations. S/U or letter grading.

  • 431. Internet Customer Analytics

    Units: 2

    Lecture, three hours (five weeks). Limited to Master of Science in Business Analytics students. Focuses on strategic and tactical issues that come up after foundational stage, specifically those issues related to customer acquisition and customer retention. Introduction of analytics frameworks, data structures, and models needed to support best practices around these issues. S/U or letter grading.

  • 432. Health Care Analytics

    Units: 2

    Lecture, three hours (five weeks). Limited to Master of Science in Business Analytics students. Exploration of opportunities for improvement in design and management of health care systems and operations, using tools such as regression analysis, linear optimization, queuing theory, decision analysis, Monte Carlo simulation, and machine learning techniques. Identification of key operational challenges facing health care managers and techniques for improving efficiency in variety of health care settings, discussion of applications of data analytics and operations management in health care industry, and practical experience with developing quantitative tools and empirical analyses. S/U or letter grading.

  • 433. Entertainment Analytics

    Units: 2

    Lecture, three hours (five weeks). Limited to Master of Science in Business Analytics students. Introduction to business analytics in entertainment industry. Focus on movie studios, television, and online media. Entertainment and media executives have changed way they approach decision making as result of big data and analytics in last two years, including making greater use of specialized analytics tools; employing dedicated data insights team to inform strategic decisions; and relying on enhanced data analytics such as simulation, optimization, or predictive analytics. Examination of content as it is produced by studios and then goes from one stage to another, being shown in theater, broadcast on television, and Internet. Analytics of providing content looking both at investment needed to produce and disseminate content, and how revenues are being extracted covered at each stage. S/U or letter grading.

  • 434. Advanced Workshop on Machine Learning

    Units: 2

    Lecture, three hours (five weeks). Limited to Master of Science in Business Analytics students. Concise introduction to theory and practice of neural networks and deep learning. Discussion of some of mathematical foundations behind main algorithms. Application-centered, practical course. S/U or letter grading.

  • 435. Data Visualization

    Units: 2

    Lecture, three hours (five weeks). Limited to Master of Science in Business Analytics students. Offers solid basis for working with data and for exploring discipline. Collection, visualization, analysis, and processing of big data through lectures, case studies, and intensive class project. Tableau and Python are used. Addresses both theoretical underpinning of domain and intensive applied computing component. S/U or letter grading.

  • 436. Fraud Analytics

    Units: 2

    Lecture, three hours (five weeks). Limited to Master of Science in Business Analytics students. How to build analytics side of fraud detection model systems. Covers all algorithmic aspects of solving fraud problem, in particular how to approach and design algorithmic solution. Focus on algorithmic development. Does not address software engineering aspects of building and fielding fraud solution. Topics covered are background for building real-time fraud detection systems and forensic accounting principles. S/U or letter grading.

  • 437. Forecasting and Time Series

    Units: 2

    Lecture, three hours (five weeks). Limited to Master of Science in Business Analytics students. Covers principal methods of time series data analysis and forecasting that are applicable in many functional areas of business, including simple and multiple regression, seasonal decomposition, AutoRegressive Integrated Moving Average (ARIMA), vector autoregressive, dynamic linear, error correction models. Use of R, RStudio and its various packages for regression and time series econometrics analysis and forecasting models. S/U or letter grading.