MSA PROGRAM DETAILS

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 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.

This course covers the fundamentals of the usage of R and Python as programming languages, with emphasis on applications in business. Both R and Python have become languages of choice for business analysts due to being open source and containing a full array of software capabilities for data preparation, analysis, and visualization. Students will learn fundamentals of both languages and will be exposed to cutting edge packages and libraries to execute essential analytic tasks.

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.

Multivariate Data Analysis focuses on the skills students need to be able to analyze and interpret multivariate data sets. Through real-world applications, students will learn to analyze data and interpret results using a variety of methods including data visualizations, multiple linear regression, analysis of variance models, and Chi-square models.

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.

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.

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.

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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 focuses on advanced application 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 consumer 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.