MDS Option

The ability to move data along the continuum from information to insight to action requires a strong foundation of skills in various quantitative methods. This course begins with a systematic and integrated overview of concepts from probability theory, statistics, and mathematical modeling such as probability distributions, cumulative probability distributions, descriptive statistics, hypothesis testing, correlation analysis, linear regression, multivariate regression, and mathematical model design. The course then proceeds to examine modern tools for conducting analyses using these quantitative methods on both small scale and large-scale datasets. Case studies from a variety of settings are used to develop students’ abilities to successfully apply the techniques learned in this course to practical circumstances that often, because of the ambiguities involved, present limitations to the power of these mathematical tools. Topics from this course also provide the foundation for some subjects covered in the analytical methods course and the data mining and business intelligence course.

Unlock the power of organizational data with this foundational course in enterprise data management. Designed for future data architects and analysts, this course explores how modern businesses store, secure, and access data. Learn to compare relational, hierarchical, and network database systems, and master key concepts in distributed database design, physical storage, and AI-powered querying. Topics also include compression, encryption, and enterprise-level data security.

Explore the core techniques of predictive modeling in this foundational course, designed for future data scientists, business analysts, and AI practitioners. You’ll learn how to transform raw data into valuable insights that support organizational decision-making. Key topics include data analytic thinking, decision trees, classification models, unsupervised machine learning, and text analytics. You’ll understand how predictive models intersect with business strategy to drive results, while gaining experience in applying these models in practical, real-world scenarios.

This course provides a business framework for the data scientist to identify, prioritize and perform data analytical projects for the purpose of driving business value and attaining competitive advantage. The course examines the data ecosystem, both external and internal to the enterprise, as well as business processes and networks upon which analytical projects can be used to reduce organizational risk and drive the creation of economic value. Topics covered include: marketing, sales and customer data exploration, supply chain data exploration, operations data exploration, financial data exploration and project management methods to convert information gain into business value. A variety of business process data sets will be examined using analytical tools including Tableau, SAP Predictive Analytics, SAS, and IBM Watson. Students will conduct the steps of an analytical workflow on a selected business process and create a project plan to drive value.

This course focuses on combining data science with a student’s prior project management training and experience to raise project management maturity in an organization. The course covers approaches for using data science tool and techniques to optimize quality, minimize risk and ensure vigilant decision making across projects in an enterprise portfolio. Topics covered include Monte Carlo methods for methodology evaluation, earned value analysis and dashboards, and data-mining techniques for establishing measured, repeatable and optimized project management processes. The project management maturity roadmap and the role of the PMO are also discussed in depth. Prerequisites: MDS 546, MDS 534, MPM 501, MPM 502. Students who are certified project management professionals can waive the project management course requirements.

This course provides a comprehensive introduction to generative and applied AI from a practical perspective. No background, technical or otherwise, will be assumed or needed. We will cover several basic perspectives on generative AI: concepts, tools, ethical issues, use cases, interactions with AI systems, creation of AI products, and strategies for AI implementation. One common thread will be prompt engineering, the primary approach for creating effective generative AI interactions.