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.

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.

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.

3.00 credits

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

Grading Basis: Graded

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.

3.00 credits

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

Grading Basis: Graded

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

3.00 credits

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

Grading Basis: Graded

Discusses data mining, time series forecasting and text mining techniques that can be utilized to effectively sift through large volumes of operational data and extract actionable information and knowledge (meaningful patterns, trends, and anomalies) to help optimize organizational processes and significantly improve bottom lines. The course covers theoretical and practical elements of various data analytics techniques such as natural language processing and advanced time series forecasting, with a focus on hands-on application in different business domains.