5272. Data Management and Business Process Modeling
3.00 credits

Prerequisites: Open only to MBA, MSBAPM, and MS FinTech students, others with consent.

Grading Basis: Graded

Introduces common techniques for relational data management, including conceptual modeling, table design and Structured Query Language (SQL). Additionally covers topics from business process re-engineering, with a focus on process modeling and how process improvement influences favorable database design.

5603. Statistics in Business Analytics
3.00 credits

Prerequisites: Open only to MBA, MSBAPM, and MS FinTech students, others with consent.

Grading Basis: Graded

Advanced level exploration of statistical techniques for data analysis. Students study basic concepts in descriptive and inferential statistics, data organization and visualization, sampling, probability, random variables, sampling distributions, hypothesis testing, linear regression, and logistic regression. Topics will focus on rigorous statistical estimation and testing. Prepares students with the skills needed to work with data using analytics software.

5604. Predictive Modeling
3.00 credits

Prerequisites: Open only to MBA, MSBAPM, and MS FinTech students, others with consent. Corequisite: OPIM 5603.

Grading Basis: Graded

Introduces the techniques of predictive modeling in a data-rich business environment. Covers the process of formulating business objectives, data selection, preparation, and partition to successfully design, build, evaluate and implement predictive models for a variety of practical business applications. Predictive models such as neural networks, decision trees, Bayesian classification, and others will be studied. The course emphasizes the relationship of each step to a company's specific business needs, goals and objectives. The focus on the business goal highlights how the process is both powerful and practical.

5641. Business Decision Modeling
3.00 credits

Prerequisites: Open only to MBA and MSBAPM students, others with consent.

Grading Basis: Graded

Discusses business modeling and decision analysis. Covers topics such as optimization, simulation, and sensitivity analysis to model and solve complex business problems. The course will emphasize the representation of business decision problems as optimization problems and the use of specialized software to solve and analyze problems, as well as input data, and retrieve results.

5270. Introduction to Project Management
3.00 credits

Prerequisites: Open only to MBA and MSBAPM students, others with consent.

Grading Basis: Graded

The course introduces students to the terminology, processes, tools, and techniques for the traditional (waterfall) project management methodology. Students will be exposed to best practices in scheduling, budgeting, managing risk, allocating resources, monitoring, and controlling projects. Students will gain experience utilizing an industry leading tool to schedule, budget, and resource a project. Practical experience will be gained by working on project teams on standard project management deliverables. Designed for future project managers or technical individual contributors that want to have more knowledge on how to be a better member of a project team.

5668. Agile Project Management and Methodologies
3.00 credits

Prerequisites: OPIM 5270; open to MBA and BAPM students, others with consent.

Grading Basis: Graded

The Agile revolution has crossed over from manufacturing to software, product design, startups, and innovation. Dissect the types of organizations where Agile will work and where hybrid or Kanban approaches are utilized. Examine leadership qualities required at the transformation level for organizations adopting Agile, as well as the roles of the product owner, scrum master, and sprint team. Evaluate the impact of personas, backlog grooming, and estimation and their effect on development and product design. Test Driven Development and Extreme Programming theories underscore the evolution from traditional project management. Introduction to SAFe, Agile metrics and principles, and understanding the management decisions required when risk threatens an Agile effort. Leverages Jira, one of the most popular Agile project management software packages used in companies today.

5111. Supply Chain Analytics
3.00 credits

Prerequisites: Open only to MBA, MSBAPM, and Advanced Business Certificate in Supply Chain Analytics students, others with consent.

Grading Basis: Graded

Managing supply chains is a complex and challenging task, given the current business trends of expanding product variety, globalization and digitalization of business, and ever changing customer expectations for fast and on-time delivery. To make right and timely decisions in the era of big data, an increasing number of companies have started to apply data analytics in supply chain management. A recent Accenture survey reveals that the use of data analytics has successfully helped companies improve customer service, reduce reaction time to supply chain issues, increase supply chain efficiency, and drive greater integration across the supply chain. This course will introduce the concepts and methods related to the design, planning, control, and coordination of supply chains with a focus on the applications of data analytics in supply chain management. The course consists of various components: lectures, case studies and a simulation game. In lectures, we introduce theoretical frameworks and useful analytical models. In case studies, we analyze supply chain issues under real-world business scenarios. In the simulation game, you will (virtually) manage a supply chain of fruit juice.

5620. Project Leadership and Communication
3.00 credits

Prerequisites: Not open to MBA students.

Grading Basis: Graded

Comprehensive and in-depth coverage of project leadership and communication designed to increase the student's ability to be a successful project manager. It covers critical competencies for leadership, critical components of communication, key roles involved in taking charge of an organization, building and using networks, motivation and influence, and authority and non-authority bases for power. Students will identify ways to further develop their own leadership potential and their own communication style. Formerly offered as MGMT 5620.

5501. Visual Analytics
3.00 credits

Prerequisites: None.

Grading Basis: Graded

Explores techniques and best practices in visualizing data. From simple cross tabs to more complex multi-dimensional analysis, explores why particular data visualizations can better illustrate patterns and correlations inherent in the data itself. Examines cognitive function and its role in data visualization designs; showing that data visualization can reveal answers and questions alike. Utilizing state of the art software, the use of parameters, filters, calculated variables, color, space and motion to visually articulate the data are surveyed. The use of dashboards to quickly reveal data-driven information that has daily relevance to executives, managers, supervisors and line personnel are investigated. Common pitfalls in visualization design and why less is often more are considered.

5502. Big Data Analytics with Cloud Computing
3.00 credits

Prerequisites: OPIM 5604 or BADM 5604; and OPIM 5272.

Grading Basis: Graded

In-depth, hands-on exploration of various cutting-edge information technologies used for big data analytics. The first half focuses on using big data management techniques for ETL (extract-transform-load) operations. The second half focuses on using big data analytics tools for data mining algorithms such as classification, clustering, and collaborative filtering. Extremely hands-on, requiring students to spend significant time working with large datasets. Students are expected to have taken at least one course in data modeling and one course in data mining (please see pre-requisites) or have significant related work experience. Students should expect to become familiar with the Unix operating system, as well as data programming concepts. Students may be required to install some software on their computers on their own, with very little support, if any, from the instructor or anyone else. Students should be willing to troubleshoot any issues during installation, drawing help from Google searches.

5509. Introduction to Deep Learning
3.00 credits

Prerequisites: OPIM 5512 and 5604; open only to MBA, MSBAPM, and MS FinTech students, others with consent. Not open to students who have passed OPIM 5894 when offered as Introduction to Deep Learning.

Grading Basis: Graded

Introduction to topics related to deep learning and will build on your previous experience in predictive analytics. Use of neural networks for a host of data and applications - including time series data, text data, geospatial data, and image data.

5510. Web Analytics
3.00 credits

Prerequisites: OPIM 5604; open only to MBA and MSBAPM students, others with consent. Not open for credits who have passed OPIM 5894 when offered as Web Analytics.

Grading Basis: Graded

Introduction to key concepts, techniques, and tools for analyzing web data to derive actionable customer intelligence, develop digital marketing strategies and evaluate their impacts. Clickstream tracking, search engine analytics, digital experiments, and social analytics.

5512. Data Science using Python
3.00 credits

Prerequisites: OPIM 5604; MBA, MSBAPM, and MS FinTech students, others with consent. Recommended preparation: Students are expected to know the fundamentals of Python programming language (or another language) through self-study, previous coursework or previous work experience, including topics such as loops, functions, and data structures. Not open to students who have passed OPIM 5894 when offered as Data Science with Python.

Grading Basis: Graded

Data science concepts using the Python programming language. Data wrangling and management using Pandas; visualization using MatPlotLib; fundamentals of matrix algebra and regression, with illustrations using Numpy; machine learning, focusing on fundamental concepts, classification, and information extraction.

5504. Adaptive Business Intelligence
3.00 credits

Prerequisites: OPIM 5603; open only to MBA and MSBAPM students, others with consent.

Grading Basis: Graded

The use of techniques from statistics and optimization to implement adaptive business intelligence (ABI) decision support systems. The course will introduce students to the different components of ABI systems as well as to the fundamentals of adaptive statistical methods, simulation adaptive methods, and evolutionary algorithms. Applications to diverse management contexts evolving in time will also be discussed.

5508. Healthcare Analytics and Research Methods
3.00 credits

Prerequisites: BADM 5103 or BADM 5180 or OPIM 5103 or OPIM 5603; open only to MBA and MSBAPM students, others with consent. Not open for credit to students who have passed OPIM 5894 when offered as Healthcare Analytics.

Grading Basis: Graded

Evidence-based practice, research techniques, health data collection devices, legislation and regulation of health data, ethical use of health data, and reporting tools. Prepares students for employment opportunities within a clinical or medical research environment.

5511. Survival Analysis with SAS
3.00 credits

Prerequisites: OPIM 5604; open only to MBA and MSBAPM students, others with consent. Not open for credits who have passed OPIM 5894 when offered as Survival Analysis using SAS.

Grading Basis: Graded

Describes the various methods used for modeling and evaluating survival data, also called time-to-event data. General statistical concepts and techniques, including survival and hazard functions, Kaplan-Meier graphs, log-rank, and related tests, Cox proportional hazards model, and the extended Cox model for time-varying covariates and non-proportional hazards.

5894. Special Topics
1.00 - 6.00 credits | May be repeated for a total of 18 credits.

Prerequisites: None.

Grading Basis: Graded

Introduces many of the most exciting and timely topics and advanced tools emerging in the field of data analytics and project management as announced in advance for each semester. With a change in content, may be repeated for a total of 18 credits.

Currently Offering:

Generative AI for Business

3.00 credits

Prerequisites: OPIM 5512 and 5604; open only to MBA, MSBAPM, and MS FinTech students, others with consent.

Grading Basis: Graded

A focused course blending foundational knowledge and practical application of generative AI in business. It begins with an overview of generative AI architecture and its business applications, including essential skills in prompt engineering for effective AI model interaction. The course then advances to cover Retrieval-Augmented Generation (RAG) and fine-tuning techniques, tailoring AI to specific industry needs while emphasizing ethical and responsible AI usage. Designed for participants with a basic understanding of AI or relevant business technology experience, this course offers a hands-on approach, preparing students to skillfully integrate generative AI into their business strategies and operations.

5894. Special Topics
1.00 - 6.00 credits | May be repeated for a total of 18 credits.

Prerequisites: None.

Grading Basis: Graded

Introduces many of the most exciting and timely topics and advanced tools emerging in the field of data analytics and project management as announced in advance for each semester. With a change in content, may be repeated for a total of 18 credits.

Currently Offering:

Global Technology Management

3.00 credits

Prerequisites: None.

Grading Basis: Graded

The Global Technology Management course will cover aspects of insurtech, fintech, risk management, and international business. Topics covered include regulations such as Right to be Forgotten and General Data Protection Regulation (GDPR), Brexit, global sustainable development, and innovation. Students may also make a separate proposal for a topic related to insurtech, fintech, risk management, or international business. The course includes a trip to London to visit a number of companies including start-ups and well established companies. Students will learn how to network with senior leaders, engage in discussions with leadership, and will have the opportunity to be immersed in business cultures from another country.

5500. Field Study Internship
3.00 credits | May be repeated for a total of 6 credits.

Prerequisites: Open to all MSBAPM and MS FinTech students. International students must have completed both a spring term and a fall term prior to taking this course. Departmental consent required.

Grading Basis: Graded