MSA PROGRAM DETAILS

Specialization: ANLT

Business analytics has been defined as the use of business intelligence and quantitative methods, including statistical analysis, forecasting/extrapolation, predicative modeling, optimization and simulation in the context of organizational decision making and problem solving. This course provides an overview of the business analytics process and important analytic techniques; data visualization, data mining, optimization, and simulation. Students are exposed to a variety of business problems in analytics (marketing, finance, operations). Throughout the course, students will learn to model and analyze complex business decisions with various tools on spreadsheets to improve decision making across business functions.

The statistical programming language R is rapidly becoming the language of choice for business analysts due to its full array of software capabilities for data preparation, analysis, and graphical display.  This course covers the fundamentals of the usage of R as a programming language, with emphasis on applications in business.  Students will learn how to use the software environment of R to efficiently source, manipulate, and analyze data.

This course covers the concepts and techniques used to analyze and report structured data. Students will learn tools and methods for understanding the data models supporting various business processes and for analyzing data from structured databases.

This course focuses on analyzing data using multivariate methods. The objective of the course is to give students the skills to be able to analyze and interpret multivariate data sets. Through real world business applications, students will learn to analyze business data and interpret results using a variety of methods including factor analysis, multiple linear regression, ANOVA, discriminant analysis, and Chi-square/contingency tables.

This course introduces the idea of how the data warehouse provides the foundation for analytics within the enterprise. Students learn the dimensional model and how data warehouses and data marts are designed and created. Central to the creation of the data warehouse is the ETL process (Extract-Transform-Load) where the data is cleaned, transformed and structured as needed for analysis. The course ends with an examination of how the data warehouse concept is extended into the realm of “Big Data”.

This course examines the concepts and approaches in Business Intelligence (BI) from a business user/analyst perspective. Students will learn to use BI tools for creating applications and dashboards in the context of fact-based decision-making.

Data mining is the process of selecting, exploring, and modeling large amounts of data to uncover previously unknown patterns and gain insights. The objective of this course is to teach students how (and when) to use various techniques for mining data. Topics include logistic regression, decision tree networks, and neural networks. Students will mine large datasets from a variety of business areas and use their findings to support business decision making.

This course focuses on text and web mining and their applications. Roughly 80% of data is unstructured. However, it is difficult to work with unstructured data. This course covers techniques for mining text and web data to improve business decision making. Topics include text/web retrieval, classification/clustering, transforming text data into a structured format, text summarization, and social network analysis. Students will also be exposed to big data issues and interact with web APIs from popular web sites for data collection.

This course is designed to provide a foundation in the use of analytical modeling techniques in managerial decision-making. We will cover two areas of modeling – computer simulation and optimization. These topics were introduced in the first course but we will follow up with a more advanced treatment of the topics and solve problems with industrial strength packages. We will also devote time to forecasting and cover a broad overview of key forecasting techniques.

This course is a continuation of Analytical Methods for Optimization and Simulation I.

This course focuses on advanced applications of analytics in business. Case discussion will be used to expose students to diverse applications of analytics in organizations. Applications include fraud detection, financial analytics, risk analytics, marketing and customer analytics, and geospatial analytics. A combination of lecture, case discussion, problem solving, group projects, and completion of exercises will be used to further the knowledge and skills of students.

Capstone course for the MSA program. This course focuses on an application-based practicum project completed during the capstone term. Students will combine the concepts and skill set learned throughout the program to navigate the analytics process and partner with an organization on a real business analytics project. The course will blend lectures and assignments to help students obtain communication skills and project management skills needed to support their project and interactions with the client.