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Academics

Generation Data

"You have to think about the generation of data as a strategic imperative. Results will only be useful if your data collection is done with a purpose."

— Florian Zettelmeyer, Faculty Director of PDAK

The Kellogg Approach to Data Analytics

Our data analytics curriculum is built around our experience that managers may not always understand how analytics can help them solve tough business problems. Similarly, data scientists may not understand the business problem well enough to provide managers with actionable data.


Businesses need analytics-savvy MBAs who are natural problem solvers and fluent in analytics so they can manage a team of data scientists and effectively use data. As a result, our teaching philosophy is designed to be relentlessly problem-driven while taking a deep dive into methods and applications.

To determine which data analytics courses to take, students should start by determining the level of expertise they鈥檇 like to achieve.

Foundational

To prepare for analytics, students should take Foundational courses, which provide the statistical and methodological foundations for data analytics. All students — regardless of their interest in data analytics — are required to take these courses.

Competitive Advantage

To obtain a working knowledge of analytics, students should also take Competitive Advantage courses. They will learn new methods to solve different business problems, and then apply these methods to large, real-world datasets.

Deep Dive

To become fluent in analytics, students should also take Deep Dive courses, which provide greater levels of methodological sophistication. Students should also consider taking an Experiential course where they can apply their analytics skills to real business problems.

The Data Analytics Pathway

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.

Foundational Courses: These courses provide the statistical and methodological foundations for data analytics.

Competitive Advantage Courses: 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.

Deep Dive Courses: These courses provide depth in selected areas. In contrast to “Competitive Advantage” courses, they can be methods as opposed to problem-focused.

Experiential Courses:
These courses allow students to apply their skills from “Competitive Advantage” and Deep Dive” courses to real company situations.

Faculty sponsors: Florian Zettelmeyer (Marketing), Brian Uzzi (MORS), Eric Anderson (Marketing)

data analytics
Pathway
1 Foundational
Business Analytics I
Business Analytics II
Marketing Research
2 Competitive Advantage
Analytics for Strategy
Retail Analytics and Pricing
Strategy Implementation
People Analytics and Strategy


Critical Thinking in Digital & Social Media Marketing


Applied Advanced Analytics


Customer Analytics and AI


Human and Machine Intelligence


Social Dynamics & Network Analytics


Decision Models and Prescriptive Analytics



3 Deep Dive
Visualization for Persuasion


Data Exploration


Technology for Analytics: What a CMO Needs to Know


Decision Making and Modeling


Introduction to Software Development
Experiential
Analytical Consulting Lab
 
Apply your analytics skills to live business problems.

 


Last edited Sept 7, 2022. For any questions regarding this page, please email kellogg-registrar@kellogg.northwestern.edu