Business Analytics and Project Management (MS)
The Master of Science in Business Analytics and Project Management (MSBAPM) is designed to meet the growing demand for professionals who can harness advanced business analytics and project management skills to address existing business problems and create new opportunities for small to global enterprises in information-rich environments. Students must complete 37 credits to fulfill all degree requirements. The program features a fully in-person option, a fully online course delivery option, or a hybrid course delivery that combines face-to-face and online sessions. The program can be completed on a full-time or part-time basis. The curriculum is aligned with professional examinations leading to certification and accreditation by the SAS Institute and the Project Management Institute.
Location
- Hartford Campus
- Stamford Campus
Modality
- Hybrid
- In Person
- Online
Requirements
| Course | Title | Credits |
|---|---|---|
| Required Courses | ||
| OPIM 5270 | Introduction to Project Management | 3 |
| OPIM 5272 | Data Management and Business Process Modeling | 3 |
| OPIM 5601 | Technical Communications in Business Analytics and Project Management | 1 |
| OPIM 5603 | Statistics in Business Analytics | 3 |
| OPIM 5604 | Predictive Modeling | 3 |
| OPIM 5641 | Business Decision Modeling | 3 |
| OPIM 5668 | Agile Project Management and Methodologies | 3 |
| OPIM 5671 | Data Mining and Time Series Forecasting | 3 |
| OPIM 5770 | Advanced Business Analytics and Project Management | 3 |
| Approved Electives | ||
| Select 12 credits of the following: | 12 | |
| Project Leadership and Communication | ||
| Operations Management | ||
| Supply Chain Analytics | ||
| Strategic Sourcing | ||
| Distribution and Logistics | ||
| Sustainable Supply Chain Management: Strategies for Environmental and Social Responsibility | ||
| Supply Chain Finance | ||
| Field Study Internship | ||
| Visual Analytics | ||
| Big Data Analytics with Cloud Computing | ||
| Adaptive Business Intelligence | ||
| Healthcare Analytics and Research Methods | ||
| Introduction to Deep Learning | ||
| Web Analytics | ||
| Survival Analysis with SAS | ||
| Data Science using Python | ||
| Generative AI for Business | ||
| Advanced Deep Learning | ||
| Building Advanced Generative AI Systems | ||
| AI Governance: A Risk Management Framework for Trustworthy and Responsible AI | ||
| Special Topics | ||
Approved graduate level (5000 or above) MKTG, MENT, or FNCE courses | ||
| Total Credits | 37 | |
Concentrations
If the electives that a student chooses fulfills the requirements of a concentration listed below, the student’s transcript will list the concentration in addition to the MSBAPM degree. The concentrations are optional, and students do not have to do any concentration if they choose not to. Substitutions to the courses that fulfill the requirements of the concentrations can be approved by the OPIM department head in consultation with other departments, as necessary.
Business Data Science Concentration
| Course | Title | Credits |
|---|---|---|
| Select three of the following: | 9 | |
| Visual Analytics | ||
| Big Data Analytics with Cloud Computing | ||
| Adaptive Business Intelligence | ||
| Introduction to Deep Learning | ||
| Survival Analysis with SAS | ||
| Data Science using Python | ||
| Total Credits | 9 | |
Marketing Analytics Concentration
| Course | Title | Credits |
|---|---|---|
| MKTG 5115 | Marketing Management | 3 |
| Select two of the following: | 6 | |
| Marketing Research and Intelligence | ||
| Marketing and Digital Analytics | ||
| Digital Marketing | ||
| Web Analytics | ||
| Total Credits | 9 | |
Supply Chain Analytics Concentration
| Course | Title | Credits |
|---|---|---|
| Select three of the following: | 9 | |
| Operations Management | ||
| Supply Chain Analytics | ||
| Strategic Sourcing | ||
| Distribution and Logistics | ||
| Sustainable Supply Chain Management: Strategies for Environmental and Social Responsibility | ||
| Supply Chain Finance | ||
| Total Credits | 9 | |
AI for Business Concentration
| Course | Title | Credits |
|---|---|---|
| Select three of the following | 9 | |
| Introduction to Deep Learning | ||
| Generative AI for Business | ||
| Advanced Deep Learning | ||
| Building Advanced Generative AI Systems | ||
| AI Governance: A Risk Management Framework for Trustworthy and Responsible AI | ||
| Total Credits | 9 | |
Learning Objectives
- Master statistical techniques for data analysis including descriptive and inferential statistics, data organization and visualization, and rigorous statistical estimation and testing methods.
- Develop analytical skills in data preparation, clustering, predictive modeling, classification analysis, model assessment, and implementation to support data-driven business decisions.
- Formulate business problems and apply optimization, simulation, and sensitivity analysis to optimize decision-making.
- Utilize data mining, text mining, forecasting, and time series analysis to predict trends and uncover actionable insights.
- Manage data development, storage, retrieval, and utilization to derive business intelligence.
- Design, manage, and re-engineer business processes by integrating best practices in data management and process optimization.
- Hone interpersonal communication, public speaking, emotional intelligence, and communication etiquette to effectively gather requirements, lead teams, and deliver impactful presentations.
- Develop expertise in project integration, time, scope, quality, communications, cost, risk, procurement, and team management to manage complex projects.
- Master agile methodologies to lead projects from initiation to completion, fostering adaptability and collaboration.
- Excel in project planning, integration, quality control, advanced risk management, and the critical "bilities" (availability, feasibility, flexibility, constructability, maintainability, operability, reliability, and traceability).
Accelerated Master of Science in Business Analytics and Project Management
UConn undergraduate students must apply to the accelerated track in Master of Science in Business Analytics and Project Management (MSBAPM) through the online pre-graduate application through The Graduate School after they have successfully completed 54 undergraduate credits, and then apply to the M.S. program during their senior year.
Students who are accepted into the accelerated track, will take OPIM 5603 Statistics in Business Analytics and either OPIM 5604 Predictive Modeling or OPIM 5270 Introduction to Project Management as part of their undergraduate studies. Students receiving a grade of ”B” or better in both courses who are admitted into the UConn Graduate School, may apply these six credits toward both the undergraduate and M.S. plans of study and have OPIM 5601 Technical Communications in Business Analytics and Project Management in Business Analytics and Project Management waived from their M.S. plan of study.
To complete the MSBAPM degree, students admitted to the accelerated track are required to complete 30 credit hours, having completed seven of the required credits to attain admission to the MSBPAM program.