Master of Science in Business Analytics

Foundations of Data Management provides students exposure to fundamental data management skills used in modern information systems that support various operational and functional areas within a business organization. Topics covered in the course emphasize how data are identified, organized, processed and managed, in a manner warranting being considered one of the most valuable organizational assets. The course emphasizes a combination of theoretical ‘why’ and experiential ‘how’ within the confines of relational data modeling and querying. It is intended to help students develop fundamental data management skills that are essential to succeeding in subsequent courses.

This course provides students with foundational knowledge of descriptive and inferential statistics. The scope of this course is limited to univariate and bivariate statistics that are commonly used to conduct basic analyses of data, including reading-in data, reviewing individual variables and their properties, conducting any requisite data corrections and enhancements, developing an overall descriptive baseline, and taking initial steps of discovering and testing possible relationships. Also included in the course scope is an introductory overview of the basics of probability and sampling theories. Topic-wise, students will develop an understanding of numerous core statistical notions, including variable types and the underlying measurement scales, variable distributions, sampling distributions, the Central Limit Theorem, Bayes Theorem, measures of central tendency and variance, statistical significance, hypothesis testing, tests of difference, as well as correlation and cross-tabulation.

This course equips students with foundational concepts and techniques required for telling compelling stories with large complex data sets, using static and interactive graphics to depict outcomes, networks, time and maps. The importance of visualizing information for many analysts is often overlooked or downgraded, but if the visualization is ineffective, the decision-making processes and knowledge discovery will be compromised. This is a project-based course that begins with reviewing concepts of human perception and cognition and perceptual accuracy and preferences, followed by exploration of the basics of graphic design and making a “good” graph. Students explore why some data visualizations present information effectively and others do not, all while learning to appreciate visualization as a key component of Data Scientists’ and Business Analysts’ skill set.

This course introduces students to the fundamentals of statistical learning, framed within the confines of exploratory data analyses. Students will learn common strategies and techniques used in extracting valid and reliable insights out of available data, all framed within the combination of theoretical ‘why’ and experiential ‘how’ competencies. Building on the foundation acquired in Foundations of Data Management and Foundations of Statistical Analysis courses, the Data Exploration course immerses students in basics of statistical significance testing and story-telling focused on identification of meaningful data patterns and associations. Prerequisite: Grade of B or higher in DSA5020, Foundations of Statistical Analysis.

This course introduces students to the fundamental concepts surrounding legal rights and responsibilities associated with data capture, storage and leveraging data for decision-making. Given the very diverse mix of topics falling under this broad umbrella, the aim of the course is to provide a general overview of the applicable aspects of the US regulatory and legislative framework, and then to offer more topically-focused overview of the key notions falling within the domains of data-capture related rights and responsibilities, data governance design and management, data security and privacy, information quality, and the ethical aspects of data access, usage, and sharing.

The goal of this course is to introduce students to the dual role and the dual benefit of dependence-focused multivariate statistical modeling techniques, commonly used in estimating the likelihood of outcomes of interest, and delineation of the key drivers of those outcomes. The former, often referred to as ‘predictive analytics,’ is focused on making forward-looking predictions, while the latter, commonly referred to as ‘prescriptive analytics,’ is geared at delineating and describing factors contributing to those predictions. The course is focused on developing rudimentary understanding of those two broad data analytical dimensions, and on introducing students to some of the more widely used predictive and prescriptive statistical techniques, including linear and logistic regression models, decision trees, as well as some of the more recent, machine learning focused techniques such as random forest.

This course introduces students to the fundamental conceptual, operational and experiential aspects of automated data mining approaches and techniques, focusing primarily on the use of pattern recognition algorithms to address common business problems involving classification and prediction. In this course students develop basic theoretical and experiential knowledge of supervised and unsupervised data mining approaches, framed in the context of the common data mining related challenges including dimensionality, regularization, overfitting and generalization. Various pattern and association rules and pattern discovery modes are also addressed, including compressed, sequential and spatial patterns, as well as multi-level and multi-dimensional associations. Lastly, common data mining applications, including clustering and prediction, are discussed and illustrated.

This course is the culmination of students’ learning – its intent is to offer students opportunities to apply the knowledge acquired in the program, in a directed, hands-on setting. As such, the course is a practicum built around solving applied industry data analytical problems, using available raw data. While substantial data processing, preparation, and outcome estimation guidance is offered throughout the course, the course does not encompass formal lectures, pre-determined assignments or examinations; instead, student’s performance in the Capstone course is based solely on successful completion of an assigned, student-specific project.