Kellogg's data analytics curriculum is built around the observation that managers do not always have a sense of what analytics can do for them, and data scientists do not always understand enough about a manager's problem to be helpful. What is missing are analytics-savvy MBAs who have a passion for business problems and who are so fluent in data analytics that they can easily converse with and manage teams of data scientists.
As a result, our teaching philosophy in data analytics is to be relentlessly problem driven while taking a deep dive into methods and applications.
These courses teach students how to apply data analytics to different business problems. Students learn new methods as needed to solve the business problems at hand and are required to apply these methods to large real-world datasets.
- Analytics for Strategy: STRT 469
- Retail Analytics and Pricing: MKTG 462
- Strategy Implementation: MORS 455-5
- People Analytics and Strategy: STRT 440
- Critical Thinking in Digital and Social Media Marketing: MKTG 479
- Applied Advanced Analytics: OPNS 441
- Customer Analytics and AI: MKTG 482
- Human and Machine Intelligence: MORS 950
- Winning with Networks: MORS 457
- Data Analytics with Large Language Models: OPNS 451
- Decision Models & Prescriptive Analytics: OPNS 450
These courses provide depth in selected areas. In contrast to “competitive advantage” courses, they can be methods as opposed to problem-focused.
- Visualization for Persuasion: LDEV 458-5
- Data Exploration: DECS-461
- Technology for Analytics: What a CMO Needs to Know: MKTG 930-5
- Data, Models, and Decisions: MECN 451
- Introduction to Software Development: ENTR 451
These courses allow students to apply their skills from “Competitive advantage” and deep dive” courses to real company situations.
- Analytical Consulting Lab: MECN 615
Apply your analytics skills to live business problems.