Curriculum: Master of Science in Business Analytics

The first part of a three semester sequence of courses on calculus (MATH 1061, 1062, 2063) for students in engineering and science. Topics covered include functions, limits and continuity, differentiation, applications of the derivative, optimization, antiderivatives, fundamental theorem of calculus, definite and indefinite integrals.

The second part of a three semester sequence of courses on calculus (MATH 1061, 1062, 2063) for students in engineering and science. Topics covered include techniques of integration, applications of the integral, sequences and series, and vectors.

Study of lines and planes, vector-valued functions, partial derivatives and their applications, multiple integrals, and calculus of vector fields.

Study of linear equations, matrices, Euclidean n-space and its subspaces, bases, dimension, coordinates, orthogonality, linear transformations, determinants, eigenvalues and eigenvectors, diagonalization.

This is a course on the use of computer tools for data management and analysis. The focus is on a few popular data management and statistical software packages such as SQL, SAS, SPSS, S Plus, R, and JMP although others may be considered. Data management and manipulation techniques including queries in SQL will be covered. Elementary analyses may include measures of location and spread, correlation, detection of outliers, table creation, graphical displays, comparison of groups, as well as specialized analyses.

An introduction to modeling, solving with state-of-the-art software, and interpreting the results for real-world linear, integer, and nonlinear optimization applications. Solution techniques and analyses covered include graphical approaches, the simplex method, duality, and sensitivity for linear optimization; branch-and-bound and cutting plane techniques for integer optimization; and Newton’s method and gradient search for nonlinear optimization.

Building and using simulation models of complex static and dynamic, stochastic systems using both spreadsheets and high-level simulation software. Topics include generating random numbers, random variates, and random processes, modeling systems, simulating static models in spreadsheets, modeling complex dynamic stochastic systems with high-level commercial simulation software, basic input modeling and statistical analysis of terminating and steady-state simulation output, and managing simulation projects. Applications in complex queueing and inventory models representing real systems such as manufacturing, supply chains, healthcare, and service operations.

PROBABILITY MODELS: Events, probability spaces and probability functions; Random variables; Distribution and density functions; Joint distributions; Moments of random variables; Special expectations; Moment generating functions;Conditional probability and conditional moments; Probability inequalities; Independence; Special probability distributions including: binomial, negative binomial, multinomial, Poisson, gamma, chi-square, normal, beta, t, F, mixture distributions, multivariate normal; Distribution of functions of random variables; Order statistics; Asymptotic results including: convergence in distribution, central limit theorem, convergence in probability, Slutsky's theorem STOCHASTIC MODELS: Discrete time Markov processes, Markov pure jump processes, Birth and death processes, Branching processes, Poisson process, Pure birth processes, Yule process; applications in several areas, e.g. queuing models, machine repair models, inventory models, etc.

Nonlinear regression and generalized linear model.Logistic regression for dichotomous and polytomous responses with a variety of links. Count data regression including Poisson and negative binomial regression. Variable selection methods. Graphical and analytic diagnostic procedures. Over dispersion. Generalized additive models. Limited dependent variable regression models (Tobit), Panel Data models.

This is a course in statistical data mining with emphasis on hands-on case study experiences using various data mining/machine learning methods and major software packages to analyze complex real world data. Topics include data preprocessing, k-nearest neighbors, generalized linear regression, subset and LASSO variable selection, model evaluation, cross validation, classification and regression trees.

This is a course in statistical data mining with emphasis on hands-on case study experiences using various data mining/machine learning methods and major software packages to analyze complex real world data. Topics include advanced trees: bagging, random forests, boosting; nonparametric smoothing methods; generalized additive models; data preprocessing/scaling; neural networks; deep learning; cluster analysis; association rules.

This course covers applied statistical methods, including topics of frequency distributions, estimation, hypothesis testing, point and interval estimation for mean and proportion; comparison of two populations; goodness of fit tests, one factor ANOVA. Major statistical software is used.

This course covers applied linear regression, including topics of fitting and drawing inferences from simple and multiple linear regression models; residual diagnostics; model correction procedure for linear regression; variable selection. Major statistical software is used.

Designed for Graduate Business students to assist them in their job search. Covers such topics as your elevator speech, practice interviewing, resume construction and salary negotiations.

This course is associated with the required MS Business Analytics Capstone. The Capstone experience will be described in an essay that is reviewed and approved by two faculty members. The essay can describe: (1)a research project based onan idea proposed independently by the student or with faculty input; (2)an extension of a case analysis or project completed in a class such as BANA7095, Graduate Case Studies in Business Analytics. The essay must describe the student's contribution to the research or case.

This course provides an introduction to the use and design of databases to store, manipulate and query data. The course introduces the structured query language (SQL) used to manage data. Students who complete this course should understand how to use SQL for basic data manipulation and queries. This course is intended for users of existing databases to extract needed information and should not be taken by MSIS students or those students who wish to learn detailed database design techniques.

This course provides an introduction as well as hands-on experience in data visualization. It introduces students to design principles for creating meaningful displays of quantitative and qualitative data to facilitate managerial decision-making.

The use of visual basic for applications for the development of applications of management science models for planning and decision support in a spreadsheet environment.

This course provides an intensive, hands-on introduction to data management and data manipulation. You will learn the fundamental skills required to acquire, munge, transform, manipulate, and visualize data in a computing environment that fosters reproducibility.

This is a course in the analysis multivariate data with emphasis on appropriate choice of estimation and testing methods. Vectors and matrices, Multivariate probability distributions and their parameter, Multivariate normal distributions, Maximization and minimization of multivariate functions, The "shape" of multivariate normal data, Correlation, prediction and regression, Sample statistics and their sampling distributions for multivariate normal data; Estimation and tests for correlation, Tests of independence, Estimation and tests for multivariate means and covariance matrices, Power of multivariate tests, multivariate linear models, canonical correlation analysis, Principal components analysis, Factor analysis, Classification and discrimination analysis.

This is a course in the analysis of time series data with emphasis on appropriate choice of models for estimation, testing, and forecasting. Topics or methodologies covered include Univariate Box-Jenkins for fitting and forecasting time series; ARIMA models, stationarity and nonstationarity; diagnosing time series models; transformations; forecasting: point and interval forecasts; seasonal time series models; modeling volatility with ARCH, GARCH; modeling time series with trends; and other methods.

Real organizational problems or challenges will be presented to students by client companies. Students in groups will work with a client to develop a solution or solutions to the problems using advanced analytic techniques. Students will present the solutions to the client in both oral and written reports.

This course is used to explore topics of current interest in the BANA domain, that do not fall within the scope of any of the regularly scheduled courses. By the nature of the course, specific topics covered will vary with each offering.

Students will know and comprehend the fundamental concepts in non-cooperative game theory. They will apply non-cooperative game theory to analyze imperfect competition, moral hazard, adverse selection, market failures, and externalities and public goods. The students will be evaluated through tests, where they will solve relevant problems by employing game theoretic tools.

This course presents the mainstream and alternate view of portfolio management using research papers, articles, and materials from academics and the markets. Many of the concepts covered are covered in the body of knowledge leading to the CFA designation.

This course is an introduction to the development of web-based applications, using Microsoft's Visual Studio and covering ASP.Net using Visual C#. Students will be expected to develop a simple web application that incorporates these technologies. Students will learn how to integrate the front-end (web site) with the back end (database) of an application. The course will cover the implementation of navigational structures, input and validation controls, and data controls in web applications.

This course is designed for the comprehensive learning of data warehousing technology for business intelligence. Data warehouses are used to store (archive) data from operational information systems. Data warehouses are useful in generating valuable control and decision-support business intelligence for many organizations in adjusting to their competitive business environment. This course will introduce students to the design, development and operation of data warehouses. Students will apply and integrate the data warehousing and business intelligence knowledge learned in this course in leading software packages.

This course discusses key concepts in the management of Business Intelligence Projects. Using the Systems Development Life Cycle as an organizing framework, and a case discussion based pedagogy, students are exposed to the major challenges in justifying BI projects, eliciting user requirements, selecting the right tools and technologies, and implementing the final solution.

Explores the role of marketing research in marketing management. Involves hands-on activities to perfect understanding of methods for collecting, analyzing, and summarizing data pertinent to solving marketing problems.

This course covers detailed issues related to managing product development and projects in organizations. The course covers, in two separate modules: -Concepts of project planning and organization, budgeting and control, and project life cycles and concepts related to organizational workflow including the staffing process, and project planning elements; related concepts of organizational forms, conflict resolution, and issues related to leadership and task management in a project environment. -Advanced concepts of project scheduling, including WBS, CPM, PERT, simulation, project budgeting, earned value analysis, project tracking and resource constrained scheduling. This includes setting up projects on Microsoft project and using the information for budgeting, resource management, tracking and ongoing communication and evaluation of projects.

Presents an overview of issues relating to the design and operation of an organization's supply chain. Information is presented as a mix of technical models and applied case studies. Topics may include inventory planning, logistics, sustainability, global operations, supply chain collaboration and contracting.