Business Analytics

Chair: Ronald H. Wright

Program Director: Thaddeus KT Sim (of Business Analytics)

Professor: Greg M. Lepak, Ronald H. Wright

Associate Professor: George E. Kulick, Thaddeus KT Sim

Assistant Professor: Furkan Oztanriseven

Business analytics is the discipline of applying quantitative analytical models to convert data into useful information to help make better business decisions. Business analytics consists of descriptive analytics (analyzing what has happened in the past), predictive analytics (predicting what could happen in the future) and prescriptive analytics (prescribing optimal actions that will result in the best outcomes).

The business analytics major introduces students to quantitative modeling and analysis. Students learn methods and techniques in the context of diagnosing and solving problems from different disciplines of business including finance, marketing, information systems and operations. A dual major in business analytics and either finance, information systems or marketing is available for those students who wish to complement their analytical skills with focused studies in these disciplines. Classes are held in a computer lab to provide hands-on real world experience in the art of modeling and analysis.

Business Analytics (ANL)

ANL 301. Business Analytics. 3 Credit Hours.

This course introduces quantitative modeling and analysis. The course includes applications from different disciplines of business including finance, marketing, information systems, and operations. The course focuses on diagnosing and solving business problems based on quantitative analysis. Modeling methods and techniques are introduced in the context of specific business situations. These techniques include forecasting, optimization, project management, supply chain management and planning, and system simulation.

Prerequisite: STA 201.

ANL 400. Applied Forecasting Analysis. 3 Credit Hours.

This course provides techniques for the parsimonious description of univariate and multivariate time-ordered data. Various models are discussed, including Box-Jenkins models, for purposes of inference, estimation, and prediction. Techniques of analysis are illustrated using actual data sets with emphasis on using the computer as an exploratory tool.

Prerequisite: STA 202 and ANL 301, or permission of instructor.

ANL 410. Supply Chain Analysis. 3 Credit Hours.

Industrial supply chains are integral part of contemporary business practices. This course will examine key issues related to the design and management of supply chains, It will include discussions on the integration of various parts of the supply chain including suppliers, factories, distribution centers, warehouses and retailers. Theories related to the efficient distribution of products to customers will be introduced. Also, management techniques addressing tradeoffs between cost and service will be discussed. Much of the course concepts will be covered through case studies and simulations.

Prerequisites: STA 202 and ANL 301.

ANL 420. Strategic Management Analysis. 3 Credit Hours.

Management science analyses are the basis of many successful strategic decisions. This course introduces many of the management science techniques in the context of strategic decision making. These techniques include linear programming, transportation, decision theory, queuing theory, and simulation. The course entails analyzing cases from all business disciplines and evaluating various strategic decisions within the framework of these cases.

Prerequisites: STA 202 and ANL 301.

ANL 430. Simulation and Risk Analysis. 3 Credit Hours.

This course is designed to provide students with basic understanding of concepts of simulation and provide them the opportunity to design several simulations for various applications (including fun and games). Methodologies are introduced in the context of financial and operations applications and include techniques for risk analysis. Models will include both event and process simulations. Simulation software packages are introduced as tools for problem solving.

Prerequisites: STA 202 and ANL 301.

ANL 440. Advanced Business Analytics. 3 Credit Hours.

Data is useful if relevant and insightful information can be extracted from it to better understand the past (descriptive analytics), anticipate future events (predictive analytics), and direct the coise of the best decision (prescriptive analytics). This course will cover different supervised and unsupervised machine learning algorithms, and their applications to structured and unstructured data including financial, marketing, health care, social media, entertainment, and socio-economic data. The effective communication of the results and insights from the analysis, including via well-designed visualizations, will be emphasized throughou the course.

Prerequisites: ANL 301 and STA 202, or permission of instructor.

ANL 601. Supply Chain Management. 3 Credit Hours.

This course provides the analytical experience for modeling manufacturing and service systems, and the understanding of how they utilize limited resources to provide goods and services. The course introduces students to different quantitative techniques and decision-making approaches and their applications to operations management problems. The problem-solving approach also involves the use of several personal computer packages containing management science and operations research programs. Topics include forecasting, facility layout, production processes, planning, scheduling, resource allocation, inventory systems, project management, decision analysis and quality control. Recommended prerequisites: STA 501 and MIS 501.

ANL 702. Cases in Business Analytics. 3 Credit Hours.

This course is designed to provide students with problem-solving skills in the field of quantitative management. The case approach is adopted to introduce complex real life examples to student-teams in a competitive environment. The course also introduces theoretical grounds for some analytical models emphasizing the assumptions and limitations of these models. The assigned cases include applications of regression, networking, linear programming, PERT, queuing theory, decision making under uncertainty and simulation. The students are required to use available computer packages as problem-solving tools and are encouraged to conduct sensitivity (what-if) analysis in their decision making approaches.

Prerequisite: ANL 601.

ANL 790. Special Topics in Business Analytics. 3 Credit Hours.

Courses in this series offer an in-depth exploration of specific issues within the field of operations management, as well as topics of current interest to students and instructors.

Statistics (STA)

STA 201. Statistics I. 3 Credit Hours.

These courses investigate the use of statistical methods in the process of optimizing decisions under uncertainty. Applications in the first semester involve the use of such statistical topics as descriptive statistics, frequency distribution, measures of central tendency and dispersion, probability and sampling theory. The second semester incorporates applications of analysis of variance, regression and correlation analysis, statistical decision making, Bayesian statistical decision making and value theory. Second semester presupposes the first.

STA 202. Statistics II. 3 Credit Hours.

These courses investigate the use of statistical methods in the process of optimizing decisions under uncertainty. Applications in the first semester involve the use of such statistical topics as descriptive statistics, frequency distribution, measures of central tendency and dispersion, probability and sampling theory. The second semester incorporates applications of analysis of variance, regression and correlation analysis, statistical decision making, Bayesian statistical decision making and value theory.

Prerequisite: STA 201.

STA 501. Quantitative Decision Making. 3 Credit Hours.

This course provides the principles of statistical inference. Probability, random variables, univariate distribution theory, hypothesis testing and estimation theory will be the focus of the first part of the course. Additional topics are selected from decision theory, nonparametric methods and linear modeling. Emphasis is placed on the use of statistical software packages to handle practical statistical analyses.