Comprehensive course analysis
Who should attend
- Senior management who want to get a practical understanding of the application of analytics and identify the types of strategic business problems where analytics can add value
- Mid-level functional managers who aim to use analytics to improve performance in their functional area and drive successful business outcomes
- Senior or top-level executives who want to build an intuition for data science and be more effective in leading a culture that values analytics in decision making
About the course
Big data and analytics are more than technology and data science problems to be relegated to specialists. In fact, the hardest part of engaging analytics is not the data science or the technology. The major challenge is first identifying the right business problem to solve, and then determine if analytics can contribute to a solution. Direct leadership involvement in analytics is critical to reaching optimal business outcomes. This is largely possible when decision-makers get a working knowledge of data science that is grounded in practical application and equipped with leadership-focused insight.
This program delivers material in an accessible, easy-to-understand format that is immediately applicable to your organization. Whether this is your first introduction to analytics, or you have some experience in related fields, you can start here. The frameworks in this program will build your working knowledge of data science and improve your data literacy. Additionally, you will understand the intuition behind machine learning algorithms and what artificial intelligence (AI) can accomplish for your business.
This program equips you with:
- The tools required to put analytics to practical use and solve specific business problems
- The language and intuition to work effectively with data scientists
- The necessary insights for leveraging analytics to accelerate growth and increase efficiency and productivity
- Build a working knowledge of data science
- Identify where analytics adds value
- Build the confidence required to operate in a data-driven environment
- Develop the ability and intuition to judge “good analytics” from “bad analytics”
- Understand the importance of experimentation platforms to drive business growth
- Learn how to tell a persuasive story with data visualization tools
Leading with Analytics
Learn why analytics is every leader’s problem. Use the Kellogg Analytics Framework — a process for developing analytics-driven business initiatives to help you achieve your business goals.
- Why analytics must be driven by business problems
- The importance of planning for analytics
- What kinds of organizational changes are needed to leverage analytics to solve business problems
- The three ways that analytics creates value (enabling, ideating and evaluating business initiatives)
- The Kellogg Analytics Framework to support the use of exploratory, predictive and casual analytics
Exploratory Analytics with Visualization
What makes you a good consumer of analytics? In this module, you will explore how the human visual system works, how to see beyond simple patterns in data, and learn how to tell persuasive stories using visualization tools.
- Explore how visualization allow analysts to see more complex patterns in their data
- Create thoughtful visualizations that make it easier to see comparisons
- Learn how to tell a persuasive story with data visualization tools
Distinguish Good from Bad Analytics
What questions should you ask to help distinguish good analytics from bad analytics? This module will help you make analytics-based decisions on real-causal relationships. You will learn to recognize analytics design flaws and identify errors in reasoning.
- Understand data which was not generated as a part of an experiment is often presented or interpreted as if they were — which is problematic
- Apply the causality checklist to diagnose the quality of analytics
- Determine whether analytics that is presented as evidence of a causal effect is “good” or “bad”
- Understand if you are drawing the right conclusions from the data presented
Analyze the importance of experimentation platforms in driving growth and in analytics
- Discover why it is important to invest in experimentation platforms
- Learn why analytics requires an experimental mindset
- Discuss why true experiments are not always possible
- Explore the main techniques one can use when true experiments are not possible
Causal Analytics in Action: The CPE Case
Apply the concepts of the program to a real-world problem. Consider the two objectives of the case and analyze the best ways to achieve the objectives.
- Gain an understanding of why planning your analytics is critical
- Discuss how analytics can effectively be used to evaluate a business initiative
- Build confidence to operate in a data-driven environment
- Practice what to do when true experiments are not possible
Gain an understanding of predictive analytics, how profitable it can be, and how it can enable business initiatives.
- Identify when causal relationships are necessary when using predictive models
- Describe how and when to cross over from predictive to causal analytics
- Evaluate the performance of a predictive model, both from a data science and a financial model
Linking Analytics with Actions
Learn how good decisions are the result of careful planning, anticipating the complexity of real-world optimization problems, and integrating your domain expertise with analytics.
- Learn why long-term success in analytics requires investment in opportunistic and designed data
- Understand variability and how it relates to the causality checklist
- Learn why intuition and analytics are both integral to solving complex business problems
Machine Learning and Artificial Intelligence
Understand the logic behind machine learning models and learn how these systems automatically uncover complex data relationships and offer predictions.
- Identify business problems that AI can help resolve
- Learn the three basic types of machine learning
- Explore the types of data that are used by AI systems
- Discuss the several types of machine learning model and applicability to business problems
Florian Zettelmeyer is the Nancy L. Ertle Professor of Marketing at the Kellogg School of Management at Northwestern University. He also founded and directs the Program on Data Analytics at Kellogg, the school's Big Data and Analytics initiative. Prior to his appointment at Kellogg he was an As...
Eric T. Anderson is the Hartmarx Professor and former Chair of the Marketing Department at Northwestern University, Kellogg School of Management and Director of the Center for Global Marketing Practice. He holds a Ph.D. in Management Science from MIT Sloan School of Management and previously hel...
Steven Franconeri is leading scientist, teacher, and speaker on visual thinking, visual communication, and the psychology of data visualization. He is a Professor of Psychology in the Weinberg College of Arts & Sciences at Northwestern, Director of the Northwestern Cognitive Science Program, ...
Education Ph.D. Computer Science, Yale University, New Haven, CT M.S. Computer Science, Yale University, New Haven, CT B.A. Philosophy, Yale University, New Haven, CT TedX Chicago: Humanizing the Machine with Language TedX Northwestern: Machine Consciousness AI at Northwestern: Partnerships ...
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