Admissions Requirements
To be eligible for the online MSA program, you must provide the following materials:
- Completed online application
- Resume
- Two essays
- Official transcripts from all institutions attended
- Two letters of recommendation (professional or academic)
- GMAT or GRE score (optional)
- TOEFL, transcript evaluation and interview (international applicants)
In addition, all incoming students must have completed an undergraduate or graduate statistics course and earned a minimum grade of B. Students can start the online MSA in the Fall or Spring semesters.
Cost of Attendance
ONLINE MSA TUITION FOR FALL 2020 & SPRING 2021
Cost Per Credit: $1,400
Total Tuition: $50,400
Fixed tuition rates are in place for the MSA program. Newly admitted students will be locked in for the duration of the program at the official rate at their point of enrollment.
Financial aid in the form of student loans is available.
TECHNOLOGY REQUIREMENTS
All students are required to have a Windows PC with Excel 2010 or later. Throughout the program, faculty members will employ a variety of different software packages, some of which do not work on a MAC computer. In order to avoid issues with conflicting security requirements and administrative settings, it is also highly recommended that students do not use a work computer.
BOOKS/SUPPLIES
Varies per course. The cost of course materials for Villanova MSA students ranges from approximately $100-$400 per semester.
APPLICATION FEE
$65 non-refundable fee
TRAVEL
None. The degree can be completed 100% online.
Online MSA Curriculum
The Villanova MSA is expertly designed to expand students’ proficiency in the latest analytics technologies, applications, and practices that are actively reshaping the business world. All of the classes at VSB emphasize a practical, real-world education and promote work across disciplines. The MSA 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.
Fundamentals
| Introduction to Business Analytics | 3.0 Credits | |
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. | ||
| Introduction to Programming in R & Python | 3.0 Credits | |
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. | ||
| Data Models and Structured Analysis | 3.0 Credits | |
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 | 3.0 Credits | |
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. | ||
Core
| Business Intelligence | 3.0 Credits | |
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. | ||
| Analytical Methods for Optimization and Simulation | 3.0 Credits | |
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. | ||
| Analytical Methods for Data Mining | 3.0 Credits | |
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. | ||
| Analytical Methods for Text and Web Mining | 3.0 Credits | |
Advanced coverage of techniques for mining text/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 | ||
| Machine Learning & Artificial Intelligence Applications with Python | 3.0 Credits | |
Currently in development | ||
| Enterprise Data Management | 3.0 Credits | |
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” | ||
Capstones
| Advanced Business Applications | 3.0 Credits | |
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. | ||
| Analytics Practicum | 3.0 Credits | |
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. | ||
Courses and course descriptions are for informational purposes only and are subject to change.
Important Notice for Online Prospective and Current Students
The following page includes important information regarding the University’s authorization status, students’ responsibilities related to relocation and programs leading to professional licensures, and the complaint process for online students. View Notices for Distance Education Programs (PDF).