Course Descriptions

This course introduces the most used descriptive, predictive, and prescriptive business analytics techniques and shows how these tools can be implemented using Microsoft® Excel. In this course students will get hands-on experience working with datasets using spreadsheets, which have also become the standard vehicle for introducing undergraduate and graduate students in business and engineering to the concepts and tools covered in the business analytics course. Students will explore techniques such as descriptive data mining, data visualization, statistical analysis, time series analysis and forecasting, predictive data mining and decision analysis to discover relationships and patterns in data. In addition, students will learn how to compare different models, evaluate results, communicate findings and data mining in project management.

Prerequisite: PMBA 6312 or a similar course. Offered: Summer, Fall, Spring.

Data Science is the study of the generalizable extraction of knowledge from data. It incorporates varying elements and builds on techniques and theories from many fields, including containing mathematics, probability models, machine learning, statistical learning, computer programming, data engineering, pattern recognition and learning, visualization and data warehousing aiming to extract value from data. This course introduces students to this rapidly growing field and equips them with some of its basic principles and tools as well as its general mindset. Students will learn concepts, techniques and tools they need to deal with various facets of data science practice.

Prerequisite: PMBA 6312 or a similar course. Offered: Summer, Fall, Spring.

Applied Data Analysis, Computation, and Programming gives students an overview of introductory programming techniques for the purpose of data collection, cleansing, management, and analysis. Emphasis will be placed on applied programming topics including functions, debugging, data classes, looping, data structures, and data types and operations. Students will also learn how to use Python to apply computational methods including generating descriptive statistics, performing graphical analyses, estimating inferential models, big data analysis and inference, and utilizing machine learning techniques with emphasis on the application of algorithms versus their development. Students will also be introduced to dynamic programming, stochastic programs, Monte Carlo simulation and Bayesian statistics. How to communicate the results of data analysis projects to firm stakeholders will also be covered.

Prerequisite: PMBA 6330 or a similar course. Offered: Summer, Fall, Spring.

Database Management for Data Analytics provides students with the knowledge required to perform efficient data analysis with the power of databases like SQL and MongoDB. The course begins with an overview of data types, data structures, working with tables, and writing queries to assemble, transform, and assess data. Students will learn how to create and use databases to prep data, perform descriptive analyses, and to conduct more advanced statistical techniques. Students will also learn database scanning methods, how to write functions, how to create triggers, and how to work with dates, times and locations. Finally, how to copy data to and from a database will be covered, making it possible for the analyst to quickly transition from a relational database to statistical processing software packages for more robust data analyses.

Prerequisite: PMBA 6330 or a similar course. Offered: Summer, Fall, Spring.

Machine learning has emerged as an indispensable subject in applied data analytics with the goal of creating flexible yet consistent predictive statistical models. This course begins with an overview of the connection between multiple regression analysis and machine learning techniques with a focus on process. Opensource statistics software, namely R, will be used as a vehicle to explore machine learning topics including cross-validation, model selection in machine learning, variable selection, algorithms for multiple regression, machine learning techniques for categorical dependent variables, classification techniques, neural networks, support vector machines, recursive partitioning, ensemble models, and the evaluation of model performance. Special emphasis will be placed on the application of widely used machine learning algorithms versus their construction. How to communicate the results of data analysis projects to firm stakeholders will also be covered.

Prerequisite: PMBA 6330 or a similar course. Offered: Summer, Fall, Spring.

Practical Data Wrangling, Visualization, and Analysis prepares students to collect, manage, and unify complex data from multiple sources for the purpose of improved data informed decision making. The course begins with an overview of data collection, cleaning, and preparation tactics utilized to transform raw data into structures more amenable to decision-oriented analyses with special emphasis on the interpretation and presentation of descriptive and inferential statistical results. This course goes beyond practical data wrangling to include data mining techniques, data visualization best practices, relational and nonrelational database management, sentiment analysis using natural language processing and API-based website scraping. Finally, how to communicate the results of data analysis projects to firm stakeholders will be covered.

Prerequisite: PMBA 6330 or a similar course. Offered: Summer, Fall, Spring.

Quantitative methods and research applies quantitative methods including decision theory, linear programming, regression analysis, simulation, etc. to real-world business problems in the areas of marketing, finance, and operations. Operations applications will be extended to include concepts related to business process improvement, supply chain management, and job, facility, and office design. Students will also learn techniques to collect, organize, and structure data for analysis including sampling, measurement, and the evaluation of survey worth. This course will culminate in research that applies knowledge to a real-world business problem. Key steps include defining a problem, assessing current knowledge, determining the value of additional information, measuring where information value is high, and using the results to prepare a detailed action plan.

Prerequisite: BMDS 3370 or BMDS 3371 or the equivalent in the past five years (or the instructor’s approval)

Data analytics introduces students to methods of data collection, storage, organization, and analysis. The course begins with an overview of descriptive statistics, graphical methods, probability, hypothesis testing, and modeling using linear regression analysis. Exploratory and confirmatory data analysis will be used to examine model specification issues such as dealing with measurement error, handling omitted variable bias, and determining the correct functional form. Students will then learn how to solve problems associated with the violation of the assumptions of linear regression including heteroskedasticity, multicollinearity, and autocorrelation. Finally, an introduction to maximum likelihood estimation for nonlinear, categorical, and limited dependent variable models will be provided. A portion of every class will be dedicated to learning how to use SAS in a lab-like setting to write programs to structure, estimate and interpret statistical models.

Prerequisites: PMBA 6312 and PMBA 6311

Forecasting methods in business introduces students to quantitative techniques that use historical data to make predictions. The course begins with an overview of basic statistical concepts, time series regression analysis, and model building and residual analysis. Emphasis is placed on obtaining point forecasts, prediction intervals for mean values, prediction intervals for individual values, detecting autocorrelation, and assessing forecast error. Students will also learn how to model trend, cyclical, and seasonal variation using polynomial, trigonometric and growth curve regression models. Forecasting using additive decomposition, multiplicative decomposition, simple exponential smoothing, trend-corrected exponential smoothing, and Holt-Winters methods will also be covered. Finally, students will use Box-Jenkins analysis to identify, estimate, and forecast time series models.

Prerequisite: PMBA 6312

Data management introduces students to techniques used to systematically collect, organize, store, and manage data. Students will build upon their statistical software programming skills to learn how to more efficiently manage data for analysis. Students will also learn how to implement smart data coding procedures so that their organizations can more intelligently store, organize, access, and analyze information to obtain competitive advantage. Emphasis will be placed on database design, management, and information extraction, particularly mining the internet and exporting and importing data to and from SQL and MS Excel. Finally, students will be introduced to computational procedures used for data mining social media including Facebook, Twitter, LinkedIn and Google+.

Prerequisite: PMBA 6312