Program Specific Information for Online MS in Business Analytics & AI students can be found at the links below:
Fall 2026 Admits
| Fall 2026 Semester (6 credits) | ||
| BMSO 600 | Data, Models, and Decisions | 3 credits |
| BMSO 758P | Data Processing and Analysis in Python | 3 credits |
| Spring 2027 Semester (8 credits) | ||
| BMSO 601 | Database Management Systems | 3 credits |
| BMSO 602 | Decision Analytics | 3 credits |
| Elective Options, Select One: | ||
BMSO 758N BMSO 758R | Introduction to Financial Accounting Introduction to Supply Chain Analytics | 2 credits |
| Summer 2027 Semester (8 credits) | ||
| BMSO 603 | Data Mining and Predictive Analytics | 3 credits |
| BMSO 727 | Data Visualization | 3 credits |
| Elective Options, Select One: | ||
BMSO 758B BMSO 758G | Strategy Analytics Customer Equity Management | 2 credits |
| Fall 2027 Semester (8 credits) | ||
| BMSO 758L | AI and Smart Agents | 3 credits |
| BMSO 758T | Enterprise Cloud Computing and AI | 3 credits |
| Elective Options, Select One: | ||
BMSO 758E BMSO 758F | Advanced Marketing Analytics Financial Analytics | 2 credits |
The Online MS in Business Analytics & AI curriculum is 30 credits. The curriculum and course offerings are subject to change.
Students join the Online MS in Business Analytics & AI program with a variety of technical skills and educational and professional backgrounds. The resources below have been compiled to assist incoming new Online MS in Business Analytics & AI students with preparing for their fall coursework.
All UMD students have unlimited complimentary access to LinkedIn Learning, an online library of >60,000 videos, courses, and career development paths focused on the latest software, creative, and business skills.
Required Software Installation and Technology Setup
Online MS in Business Analytics & AI students must have the following software ready to use on their personal computers before the first day of class:
- Microsoft Excel (access at https://terpware.umd.edu/Windows/Title/3107)
- Python (access/download at https://www.anaconda.com/download)
- Respondus Lockdown Browser for Students (https://go.umd.edu/UMDrespondus)
Students should do the following before the first day of class:
- Review Technology and vSmith pages
- Review Kumo and set preferences
- Review Canvas for communications from course faculty (once courses are published)
Strongly Suggested Assignments
First semester courses will focus on analytical principles that can guide complex decision-making. A strong understanding of basic business math, basis statistics, and spreadsheet skills will be critical to successfully completing the semester.
- LinkedIn Learning: Statistics Foundations 1: The Basics
- LinkedIn Learning: Statistics Foundations 2: Probability
- LinkedIn Learning: Excel Essential Training
Required Pre-Semester Online Synchronous Workshops
Two online synchronous workshops are required for incoming Online MS in Business Analytics & AI students who do not have prior experience with Python.
Workshop 1: Getting Started with Python
Date: Sunday, August 2
Time: 9:00 - 11:00 a.m.
Students who missed this session may view the recording
Workshop 2: Python for Data Science
Date: Sunday, August 9
Time: 9:00 - 11:00 a.m.
Students who missed this session may view the recording
Additional Optional Assignments and Reading
Incoming students do not need to be proficient with all of these tools prior to the start of classes, however, having some prior experience could help them learn more effectively.
- Business Writing and Communications
- Tableau
Access/download at https://www.tableau.com/academic/students; free for academic use when using @umd.edu email address - Power BI
- Python
Access/download at https://www.anaconda.com/download (also available on vSmith at https://go.umd.edu/vsmith-setup)- LinkedIn Learning Course: Python Quick Start
- LinkedIn Learning Course: Python Statistics Essential Training
- LinkedIn Learning Course: Advanced Python
- Books
- Python for Data Analytics: A Business-Oriented Approach (by Daniel H. Groner)
- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Jupyter, 3rd Edition (by Wes McKinney)
- SQL
Access at http://vsmith.umd.edu
Wednesday, May 27, 2026
Failure to register within one week of receiving instructions may result in the termination of admission to the program. Please review all of the information on this page.
HOW TO REGISTER
Your Directory ID and password must be set BEFORE you can register; see Directions for Setting Up Directory ID and Password.
Step 1: Log-in to Testudo
Go to: http://www.testudo.umd.edu/
Click on: Registration (Drop/Add)
Enter your Directory ID and Password
Select: Fall 2026
Step 2: Add Course Track
Enter the following information in the "Registration (Drop/Add)" screen of Testudo. Registering for the course track will automatically enroll you in the Fall 2026 required courses. Testudo will not allow you to register for these courses individually.
| Course | Section | Grading Method | Credits |
|---|---|---|---|
| MSBD99MO | MB11 | None | Leave blank |
Click "Submit Changes" to complete your registration.
Step 3: Click "View Schedule" to Confirm Courses
COURSE SCHEDULE
These courses are 14-week, full-semester courses. Please be sure to review and bookmark the Business Master's Academic Calendar, as it varies from the University of Maryland Academic Calendar.
BMSO 600 WN01 - Data, Models and Decisions (3 credits) BMSO 758P WN01 - Data Processing and Analysis in Python (3 credits) |
The Fall 2026 schedule is subject to change.
ACADEMIC ADVISING, SCHEDULE CHANGES & COURSE REFUNDS
Students with academic advising questions can contact Alex Mainardi. With permission from the academic advisor, students can add, drop, or change course sections during the schedule adjustment period by logging into Testudo. Once the course term has started, students may not receive a full refund when dropping a course. For additional information, please review Penalties for Drops During Schedule Adjustment and Non-Standard Course Dates and Deadlines.
Tuition & Fee information, including due dates and residency information can be found at: