Online Master of Science in Business Analytics

Foundations in Accounting

This course educates students in the fundamentals of finance and accounting. The methods covered are used extensively throughout the MBA program. Topics include: the accounting process that results in the preparation of financial statements for external users, techniques for analyzing a basic set of financial statements, using accounting information to support management decisions, and using time value of money techniques to evaluate capital asset decisions. (MS Accounting students cannot earn credit by taking this course.) This course cannot be used as an elective course for Lindner College of Business Master's programs.

Foundations in Economics

This course provides an introduction to the fundamentals of economics at the graduate level for students without previous economics coursework. Students will be exposed to the essentials of both microeconomics and macroeconomics. Microeconomics topics to be discussed include the supply and demand mechanism,how markets are affected by regulation and taxation, costs of production, and how market structure affects outcomes. Macroeconomic topics to be discussed include the fundamental measures of the aggregate economy, the sources of economic growth, explaining short-run fluctuations in economic activity, and how government policies can affect these fluctuations. A particular focus will be to understand how fundamental economic principles at both the micro and macro level can affect companies, investments, industries, and national economies. This course may not be used as an elective course for Lindner College of Business Master's programs.

Foundations in Finance

Upon completion of this course, students should be able to: 1. Apply concepts and perform Time Value of Money calculations 2. Understand differences in interest rates (due to differences in risk, horizon, and compounding) 3. Use present value calculations to solve bond pricing and risk applications 4. Use present value calculations to solve stock valuation applications This course cannot be used as an elective course for Lindner College of Business master's programs.

Marketing Foundations

The purpose of this course is to provide students with a foundation in Marketing. Concepts such as segmentation, targeting, positioning, customer and market analysis, and basic marketing planning will be introduced. This course cannot be used as an elective for Linder College of Business master's students.

Operations and Supply Chain Management

This course helps develop knowledge of the basic principles for operations and supply chain management (OSCM) through simulations and case studies. The focus is on decisions and activities involving the effective management of resources including, process improvement, supply management, logistics, and supply chain coordination

Organizations

The purpose of this course is to provide students with a foundation in the study of Organizations (Management) in preparation for the MBA or MS program. The goal is to provide students with an introduction to the study of organizations (strategy, structure, design, and context) to help students navigate through the advanced graduate course work and to become a more effective manager. This entails understanding how organizations work as well as developing requisite personal skills in problem analysis and writing. This course cannot be used for an elective course for Lindner College of Business master's programs.

Data Visualization

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.

Optimization

An introduction to modeling, solving with state-of-the-art software, and interpreting the results for real-world linear, integer, and optimization under uncertainty applications. Solution techniques and analyses covered include graphical approaches, the simplex method, and sensitivity for linear optimization; branch-and-bound and cutting plane techniques for integer optimization; and two-stage stochastic programming and robust optimization for optimization under uncertainty. Upon completion of this course, students will be able to formulate real applications as mathematical problems, understanding the underlying assumptions, and the scalability/difficulty of the proposed models.

Data Wrangling

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.

Simulation Modeling and Methods

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

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.

Statistical Modeling

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.

Data Mining I

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.

Data Mining II

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.

Applied Statistical Methods

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.

Applied Linear Regression

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.

Career Management

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.

Data Management

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.

MS Capstone

This course is associated with the required MS Business Analytics Capstone. The Capstone experience will be described in a recorded presentation that is reviewed by two quality individuals. At least one individual will be an OBAIS faculty member and the other will have expertise in business analytics and/or the tools and methods presented. The presentation can describe: (1) a research project based on an idea proposed independently by the student or with faculty input; (2) an extension of a case analysis or project completed in a class, or (3) work completed in an internship or job. Graduate Case Studies in Business Analytics. The presentation must describe the student's contribution to the research or job.

Statistical Computing

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.

Multivariate Statistical Methods

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.

Forecasting and Time Series Methods

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.

Machine Learning Design for Business

This course provides a framework for developing real-world machine learning systems that are deployable, reliable, and scalable. Designing machine learning systems is the process of defining the software architecture, infrastructure, algorithms, and data for a machine learning system to satisfy specified business requirements. Without deliberate design, machine learning systems can get outdated quickly because (1) the tools continue to evolve, (2) business requirements change, and (3) data distributions constantly shift. Students will learn about data management, data engineering, feature engineering, approaches to model selection, training, scaling, and how to continually monitor and deploy changes to ML systems for successful business applications. They will also be exposed to managing the human side of ML projects such as team structure and business metrics.

Graduate Case Studies in Business Analytics

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.

Special Topics in Business Analytics

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.

Intelligent Data Analysis

This course will introduce students to the theoretical and practical aspects of the field of data mining. Algorithms for data mining will be covered and their relationships with statistics, mathematics, and algorithm design foundations will be explored in detail.

Game Theory

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.

Portfolio Management

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.

Programming and Modern Frameworks

This course introduces full-stack development, emphasizing modern programming languages, frameworks, and best practices for building scalable, high-performance web applications. Through hands-on projects, students will develop real-world applications that integrate both front-end and back-end technologies. By the end of the course, students will have built and deployed a fully functional web application using contemporary frameworks and cloud-based deployment strategies.

Data Warehousing and Business Intelligence

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.

Generative Artificial Intelligence for Business

This course examines the technology underlying modern generative artificial intelligence / machine learning models from a business perspective, including their uses in coding, professional and artistic applications, and the various controversies and challenges to work and/or society they may pose.

Governance of AI/ML Systems

This course teaches students how to develop, scale-up, and sustainably manage high-performing Artificial Intelligence/Machine Learning systems in business organizations. It introduces concepts and techniques that enable the development of surrogate approaches to explain AI/ML models, build redundancy in AI/ML systems, and calculate and minimize risk of failures while using such approaches.

Big Data Integration

This course presents an overview of the principles of data integration, the fundamental basis for developing useful and flexible business intelligence platforms. Modern data integration needs differ from traditional approaches in four main dimensions that parallel differences between big data and traditional data: volume, velocity, variety, and veracity.

Survey of Machine Learning and Artificial Intelligence

This course is a survey of Machine Learning (ML) and Artificial Intelligence (AI) from the Data Scientist’s perspective. It explores ML and AI topics, current and emerging technologies, and applications for students to gain understanding of the successful implementation of ML and AI to address key business and industry problems.

Special Topics in Information Systems

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

Marketing Research for Managers

Explores the role of marketing research in marketing management. Students do hands-on assignments to develop their understanding of methods for designing and implementing marketing research projects, including collecting, analyzing, and summarizing data pertinent to solving marketing problems. Developing experience in key aspects of marketing research is stressed.

Managing Project Operations

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.

Supply Chain Strategy and Analysis

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.