CURRICULUM
Our Master of Science in Business Analytics degree is expertly designed to emphasize practical, real-world education and promote work across disciplines. The online MSBA curriculum is comprised of three components: the Fundamentals, Core and Capstone courses. Consisting of 10 courses taken over five terms — each of which is divided into two sessions — the 30-credit-hour business analytic curriculum is designed to be completed in 18 months.
Upon graduation, your MSBA course curriculum will allow you to become a proficient programmer in R, Python, and SQL, while experiencing an array of cutting-edge big data management and cloud analysis tools like Amazon SageMaker, Hadoop, Hive and Pig. These tools will allow you to confidently collect, manage, and visualize data, build models, and use this information to make better business decisions. Our MSBA curriculum concludes with a capstone practicum course, where you will implement analytics solutions for a rotating set of companies, using real problems and real data.
FOUNDATION
Introduction to Business Analytics
Provides an overview of the business analytics process and important analytic techniques, data visualization, data mining, optimization, and simulation. Exposes students 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.
Programming in R & Python
The fundamentals of the usage of R and Python as programming languages, with emphasis on applications in business. Students will learn fundamentals of both languages and will be exposed to cutting edge packages and libraries to execute analytic tasks.
Data Models & Structured Analysis
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
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.
PLEASE NOTE: All Foundation Courses Are Required
CORE
Analytical Methods for Data Mining
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.
Business Intelligence
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.
Machine Learning & Artificial Intelligence Application with Python
This course covers the use of machine learning algorithms in business decision making and the potential drawbacks and ethical challenges. A particular focus will be on preprocessing, coding, and evaluation methodologies for deep learning.
Enterprise Data Management
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".
Analytical Methods for Text Web Mining
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.
Enterprise Data Management
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".
Advanced Business Applications
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.
Analytical Methods for Optimization and Simulation
This course builds on the material from earlier courses in the program. It provides students with a chance to dive deeper into critical optimization, probability, and simulation modeling techniques useful in today's business environment. This course begins with a review of modeling basics, expands the student's exposure to optimization modeling techniques for both linear and non-linear problems, and introduces simulation modeling using an industry-leading simulation software package. 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 to improve
Machine Learning & Artificial Intelligence Application with Python
This course covers the use of machine learning algorithms in business decision making and the potential drawbacks and ethical challenges. A particular focus will be on preprocessing, coding, and evaluation methodologies for deep learning.
PLEASE NOTE: All Students Will Have The Option To Explore Classes Throughout Our Diverse Curriculum.
CAPSTONE
Analytics Practicum
Capstone course for the MSA program. This course focuses on the 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.
PLEASE NOTE: Curriculum Is Subject To Change.