PTMBA2023 Data Science for Managers

With the dramatically increased use of data science in business there comes an even higher increased need for managers with knowledge of the fundamentals of data science to make effective decisions: McKinsey estimated that about 10 managers with these skills will be needed for every data scientist (because leverage from a data science team can be gotten in multiple areas of the business).

This course seeks to impart this knowledge. Specifically, the objective is to convey an understanding of data science sufficient to become a critical consumer of data science solutions. You will acquire the skills needed to ask the right questions when consultants are proposing data science projects, and you will be able to communicate better with internal data science teams as you will have an understanding of how data scientists work. The aim is not to train you to become a data scientist, but to work with them as a manager.

The following concepts are covered (taught in a hands-on, case-based manner):

• Introduction to the Cross-Industry Standard Process for Data Mining: from business understanding over data understanding, data preparation, modelling, evaluation to deployment.
• Data types and why this matters
• Data sampling and partitioning
• Conceptual understanding of key machine learning models for predictive analytics (decision trees, linear classifiers, …)
• What is a good model? Evaluation and visualisation of model performance
• Data Science and business strategy: assessing data science project proposals, working with data scientists
• Visualization concepts, interactive maps and dashboards: theory and practice using Tableau

The course uses R to illustrate a data science project, but acquiring programming skills is no learning objective and, therefore, learning about R programming is entirely voluntary. 

F. Provost and T. Fawcett. Data Science for Business. O'Reilly, 2013

The course consists of a combination of lectures, discussions, case studies, and group work.

Each session contains short exerciseson a comprehensive data science case studyto provide a hands-on experienceon running data science projects and what interactions with the business problem owner they typically require. Students should bring their own laptops along.

There is one intermediate group assignment which counts 40%.

The final project is to be done on an individual basis and will make up 60% of the grade.

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