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 such 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.

MENT 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.

OPIM 5110.  Operations Management.  (3 Credits)

An operations manager is concerned with designing, operating and controlling a system for producing goods and services. Design decisions include selecting a process technology, organizing jobs, selecting vendors, and developing the location and layout of facilities. Operating the system involves planning and scheduling work and material flow, controlling quality, and managing inventories. General systems concepts and models are developed and applied. Topics include process flow analysis, inventory systems, waiting line analysis, quality design, capacity resource planning, project management, and integrating operations with the firm's strategic plans.

Enrollment Requirements: Open to MBA and MSBAPM students, others with consent. Not open to students who have passed or are currently enrolled in OPIM 5184.

OPIM 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.

OPIM 5112.  Strategic Sourcing.  (3 Credits) 

Sourcing (or purchasing) has evolved as a strategic function that affects firms' ability to meet customer needs and their competitive advantages in today's global business environment. It refers to the collaborative and structured process of acquiring goods and services from suppliers, along with the function of managing suppliers, to achieve desired supply chain performance. This course introduces the framework and fundamental concepts in sourcing, as well as the tools to effectively manage the strategic sourcing process.

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

Economic globalization has increased the criticality of distribution, transportation, and logistics operations for the global supply chain. A calamity in any part of a distribution system, including transportation of raw materials, warehousing, delivery of finished goods, etc., can lead to costly repercussions such as supply shortages, revenue losses and customer dissatisfaction. An efficient and effective distribution and logistics system is vital to the success of businesses as it bridges temporal and geographical gaps between production and consumption. The recent development of e-commerce and customers' increased awareness of sustainability have posed new challenges in distribution and logistics strategies. Introduces concepts related to the global supply chain and distribution strategies, transportation and logistics planning, and warehouse operations. Emphasis on quantitative methods and analytics tools for the design of distribution network, transportation planning, and logistics operations.

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

OPIM 5114.  Sustainable Supply Chain Management: Strategies for Environmental and Social Responsibility.  (3 Credits) 

Supply chain sustainability has gained significant importance in today's globalized and interconnected world. This course provides a comprehensive understanding of sustainable supply chain management. It equips students with the knowledge and tools to develop strategies for achieving environmental and social responsibility in supply chain operations. Students will explore the critical role of supply chains in addressing global sustainability challenges, such as climate change, resource scarcity, human rights, and fair labor practices. This course aims to empower students to create positive change in supply chains, drive innovation, and promote long-term economic, environmental, and social value through a combination of theoretical concepts, practical case studies, and interactive discussions.

Enrollment Requirements: Open to all School of Business graduate students. 

OPIM 5115.  Supply Chain Finance.  (3 Credits)

Supply Chain Finance (SCF) involves the use of financing and risk mitigation practices and techniques to optimize the management of the working capital and liquidity invested in supply chain processes and transactions. Supply chain finance is an increasingly important subject that brings new opportunities and challenges to businesses, as companies extend their supply chains in an environment of restricted supply of credit in different geographies and adopt various innovative SCF approaches. To provide a comprehensive understanding of the theories and practices of SCF, this course will offer a mixed pool of theory concepts, application tools, simulation games, and case studies. Among the topics explored are working capital management, inventory financing, financial hedging, trade credit, factoring and reverse factoring, SCF instruments, accounting regulation, fintech innovation, blockchain technology, and sustainable SCF.

Enrollment Requirements: Open to all School of Business Graduate Students.

OPIM 5500.  Field Study Internship.  (3 Credits)

Gives students real-world experiences in applications of analytics and/or project management through an internship or industry project undertaken individually with a company under the joint supervision of a faculty member and the student's field supervisor. Student performance will be evaluated on the basis of an appraisal by the field supervisor and a detailed written report submitted by the student.

Enrollment Requirements: 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.  May be repeated for a total of 6 credits

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, it 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.

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.

OPIM 5505. Analytical Consulting for Financial Services. (3 Credits)

Exposes students to a wide array of real consulting situations in business analytics operations and financial services and will teach students methods of addressing these problems using spreadsheets, simulation, and optimization methods. While consulting encompasses many specific tasks and requires broad functional knowledge, there is an increased need and appreciation of the usefulness of analytical consulting.

Enrollment Requirements: OPIM 5641 or BADM 5181.

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.

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.

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.

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.

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, it 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.

Data-driven businesses are dominating today. Learn how to explore, visualize, model, and derive custom insights from data using state-of-the-art methods & software.

Choose three (3) from the following recommended courses:
OPIM 5501 – Visual Analytics
OPIM 5502 – Big Data Analytics with Cloud Computing
OPIM 5504 – Adaptive Business Intelligence
OPIM 5509 – Introduction to Deep Learning
OPIM 5511 – Survival Analysis using SAS
OPIM 5512 – Data Science using Python

Marketing is more than creativity. Gain insights on how businesses build awareness, engage consumers, and stand out among fierce competition in a digital world.

Required Course: MKTG 5115 – Marketing Management
Choose two (2) from the following recommended courses:
MKTG 5220 – Big Data and Strategic Marketing
MKTG 5250 – Marketing Research and Intelligence
MKTG 5251 – Marketing and Digital Analytics
MKTG 5665 – Digital Marketing
OPIM 5510 – Web Analytics