The Villanova MSBA is expertly designed to expand students’ proficiency in the latest analytics technologies, applications, and practices that are actively reshaping the business world. All classes at VSB emphasize a practical, real-world education and promote work across disciplines. The MSBA curriculum is comprised of three components: the Fundamentals, the Core and the Capstones. Consisting of 12 three-credit courses taken over six semesters — each of which is divided into two sessions — the 36-credit-hour program is designed to be completed in 24 months.

Upon graduation, students will be proficient programmers in R, Python, and SQL. Additionally, they will have experience with an evolving array of cutting-edge big data management and cloud analysis tools like Amazon SageMaker, Hadoop, Hive and Pig.

MSBA Curriculum


Provides an overview of the business analytics process and important analytic techniques: data visualization, data mining, optimization and simulation. Students will model and analyze complex business decisions with various tools on spreadsheets to improve decision making across business functions.

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 essential analytic tasks. Prerequisite: Introduction to Business Analytics with concurrency

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. Prerequisites: Introduction to Business Analytics; Programming in Rand Python

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. Prerequisites: Introduction to Business Analytics; Programming in R and Python; Data Models and Structured Analysis


Explores how (and when) various techniques can be used for mining data to uncover previously unknown patterns and gain insights. Students will mine large datasets from a variety of business areas and use their findings to support managerial decision-making. Prerequisites: Introduction to Business Analytics; Programming in R and Python; Data Models and Structured Analysis; Multivariate Data Analysis

This course provides an advanced coverage of the concepts, techniques and applications for mining text/web data to improve business decision-making. Topics include text mining applications, software and methodologies including, information extraction, classification, clustering, sentiment analysis, data visualization and social network analysis. RapidMiner, R and Python will be used in this course. Prerequisites: Introduction to Business Analytics; Programming in R and Python; Data Models and Structured Analysis; Multivariate Data Analysis

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. Prerequisites: Introduction to Business Analytics; Programming in R and Python; Data Models and Structured Analysis; Multivariate Data Analysis

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 students’ exposure to optimization modeling techniques for both linear and nonlinear 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 decision-making across business functions. Prerequisites: Introduction to Business Analytics; Programming in R and Python; Data Models and Structured Analysis; Multivariate Data Analysis

Machine learning is pervasive, with high-stakes applications spanning all business sectors, including fraud detection, high-frequency trading and highly personalized and relevant marketing campaigns. Machine learning requires interdisciplinary techniques to create algorithms that sift through large volumes of data to support business decision making. This class will equip students with the analytical techniques and skills to build and evaluate machine learning models using Python. In addition, students will use Python for a hands-on exploration of a broad cross-section of algorithms for machine learning, including linear models and dimensionality reduction. Students will gain additional familiarity with deep learning models such as artificial, recurrent and long short-term memory neural networks. Cloud-based resources and the open-source frameworks TensorFlow and Keras will be leveraged. At the end of the course, students will be prepared for accurate, effective and ethical research or industry application of machine learning techniques. Prerequisites: Introduction to Business Analytics; Programming in R and Python; Data Models and Structured Analysis; Multivariate Data Analysis; Analytical Methods for Data Mining

Explores how the data warehouse provides the foundation for analytics within the enterprise. Topics include dimensional models, design and creation of data warehouses and data marts, ETL process and the extension of the data warehouse concept to big data. Prerequisites: Introduction to Business Analytics; Programming in R and Python; Data Models and Structured Analysis; Multivariate Data Analysis


Exposes student to advanced and diverse applications of analytics in business. 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. Prerequisites: Analytical Methods for Data Mining; Analytical Methods for Text and Web Mining; Business Intelligence; Analytical Methods for Optimization and Simulation 

Capstone course for the MSBA program. Students will partner with an organization to complete an application-based practicum project, using skills learned throughout the program. The course blends lectures and assignments to help students build requisite communication and project management skills. Prerequisite: Advanced Business Applications


PLEASE NOTE: Curriculum is subject to change.


MSBA Program Curriculum


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James Dietz
Assistant Director, Admissions
MSBA Program