The program requires 37 credit hours, including five 3-credit courses in Business Analytics, 1 credit in technical communications, four 3-credit courses in Project Management, and 9 credit hours in elective courses. Elective courses can be chosen from the School’s MBA program or any approved UConn graduate-level courses and may include experiential learning credits.
A copy of the program plan of study can be found here.
Other study options are possible. Please contact the program for advice.
The University of Connecticut Academic Calendar is available through the Office of the Registrar.
Required courses (5):
- Business Process Modeling and Data Management (OPIM 5272)
- Predictive Modeling (OPIM 5604)
- Business Decision Modeling (OPIM 5641)
- Data Mining and Business Intelligence (OPIM 5671)
- Statistics in Business Analytics (OPIM 5603)
Additional optional course:
- Real-time Enterprise Data Integration and Audit (OPIM 5894*)
- Enterprise Security, Governance and Audit (OPIM 5894*)
- Data Analytics using R (OPIM 5503)
- Adaptive Business Intelligence (OPIM 5504)
- Big Data Analytics with Hadoop (OPIM5502)
- Visual Analytics (OPIM 5501)
- Analytical Consulting for Financial Services (OPIM 5505)
- Social Media Analytics (OPIM 5894*)
- Data Science using Python (OPIM 5894*)
- Survival Analysis using SAS BASE (OPIM 5894*)
Required courses (4):
- Introduction to Project Management (OPIM 5270)
- Project Leadership and Communications (MGMT 5620)
- Project Risk and Cost Management (OPIM 5668)
- Advanced Business Analytics and Project Management (OPIM 5770)
Additional optional course:
Elective courses can be chosen from the wide array of offerings in the School’s professional MBA program and may include experiential learning credits. With the approval of the Program Director, electives may include other University of Connecticut graduate programs. Please click here to see a choice of other electives.
Please note, the program has a Windows-based laptop requirement as many courses require the use of Windows based software and application
*All OPIM 5894 classes are special topics, look in the description for exact seminar topic
***exceptions may apply, if approved by program with appropriate internship, if student has a minimum of 5 years work experience.
OPIM 5894 – Special Topic’s Course Descriptions
Companies store and collect large amounts of data during day-to-day transactions. To analyze long-term trends and patterns in the data and provide actionable intelligence to managers, this data needs to be consolidated in a data warehouse. A data warehouse is “a repository of subject-oriented, time-variant data from multiple sources, used for information retrieval and decision support”. It provides a single consolidated interface to the entire corporate data. Data analysis for enterprise-wide business intelligence can then be performed on such consolidated data.
This course material will cover various aspects of the data-warehousing environment, followed by data mining techniques for business intelligence (BI). It is a combination of lecture, class discussion and hands-on computer work, and will:
- Help you understand how to design and implement a data warehouse for various business applications.
- Perform OLAP and data mining operations
- See an integrated view of data warehousing and BI.
Course includes: Setting up the Warehouse Environment; Building the Warehouse; Deploying and Maintaining the Warehouse
This course discusses the business risks arising from the digital information processing and identifies ways to prevent, detect, and mitigate negative consequences of information security breaches. First, students will be introduced to the basic principles of information security, its role in reducing information risk exposure, and tools and solutions that can be used to prevent information loss or costly business interruptions. Second, students will explore the role of information technology governance in business organizations, discuss important relevant laws (for example, Sarbanes-Oxley Act of 2002), reporting requirements, and industry standards for IT Governance (for example, COBIT). Third, students will study the process of information systems audit, IT audit tools, and audit procedures to help in detection and prevention of fraud.
New technology advances and the drastically changed culture of producing and sharing information on the web make vast amounts of individually produced data available for research and analysis. The informed decision making based on these data may foster new insights, create new business models and promote new behaviors. The analysis of social media networks provides economic value derived from the user generated content and leads to the creation of new monetizing models and interactive online tools. It also provides more understanding of the drivers of social change in the society, can explain the activities behind political movements, and may enable public and private organizations predict future events.
This course will cover social media strategies and applications, implications for business, privacy issues associated with social media and factors contributing to social change. Business cases based on using various social media such as blogs, micro blogs, social networking platforms, mobile marketing, crowdsourcing and crowdfunding will be discussed.
The focus of the course will be on managing social media and on data analytics. Students will be introduced to social network analytics tools, and will learn how organizations can leverage social media analytics to improve decision making and investigate key questions.
The primary aims of the course are to introduce ethical concepts that are relevant to resolving legal and moral issues in business; to impart the reasoning and analytical skills needed to apply ethical concepts to legal and business decisions; to identify the moral issues involved in the management of specific legal and ethical problem areas in business; to provide an understanding of the social and natural environments within which legal and moral issues in business arise; and to analyze case studies of actual moral dilemmas faced by businesses.
Course includes: Breach of Contract, Judicial Remedies, and Alternative Dispute Resolution Mechanisms
Organizations of all kinds around the world today, whether private or public, large or small, for profit or not for profit, achieve their strategic and operational goals by carrying out projects. The emphasis of this course is on the application of project management knowledge, tools, and techniques to the planning, organization, and delivery of international development projects and programs. Funded by institutions (e.g., multilateral or regional development banks, United Nations associated agencies, bilateral government agencies, non-governmental organizations, global funds), these projects/programs cover a wide range of sectors and focus on poverty reduction/alleviation and improving living standards of people in developing and emerging countries, assistance to victims of natural or people caused disasters, capacity building and development of basic physical and social infrastructures, and on promoting environmentally sound development and basic human rights protection.
The format of the course consists of a mixture of lectures, case studies, class discussions, and presentations to peers. The material is organized to enable both technical and non-technical participants to appreciate the value offered by a variety of management practices and planning tools as means for administering, directing, and coordinating international development projects.
This course teaches data science concepts using the Python programming language. It is divided into five modules. In the first module, students will learn the Python programming language, with special focus on data management tools in the Python ecosystem, such as Pandas and Numpy. In the second module, we will cover the fundamentals of probability and statistics, with illustrations using Python. (See tentative schedule for specific topics.) In the third module, we will Bayesian statistics with illustrations using Python. In the fourth module, we will cover linear regression from first principles, focusing on the theory behind linear regression, and we will work on analysis examples in Python. In the fifth module, we will cover text mining, focusing on fundamental concepts, classification, and information extraction, with hands-on examples in Python