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PTMBA2024 Data Science for Managers

In this course, we shall explore techniques for descriptive, predictive and prescriptive analytics. This will enable better decision making in the presence of big data.
Course code
MBA SCM643, MBA SCM643 SI
Course type
PT MBA Lecture
Weekly Hours
2,0
ECTS
2.0
Term
HS 2023
Language
Englisch
Lecturers
Prof. Dr. Arne Karsten Strauss
Please note that exchange students obtain a higher number of credits in the BSc-program at WHU than listed here. For further information please contact directly the International Relations Office.

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
  • Generative AI, like ChatGPT, and its impact on data science project management
  • Visualization concepts, interactive maps and dashboards: theory and practice using Tableau

There is no need to acquire/use programming in this class. If you are interested in the implementation details of the various models that we look into, I am happy to provide you with the R code underpinning them.

Date Time
Saturday, 02.12.2023 09:00 - 16:30
Sunday, 03.12.2023 09:00 - 16:30
Saturday, 09.12.2023 09:00 - 16:30
Saturday, 30.12.2023 23:50 - 23:55
ability to effectively collaborate with data scientists and to assessdata science projects
F. Provost and T. Fawcett. Data Science for Business. O'Reilly, 2013 K. Dubovikov. Managing Data Science. Packt, 2019
The course consists of a combination of lectures, discussions, case studies, and group work.

Each session contains short exerciseson acomprehensivedata science case studyto provide a hands-on experience on running data science projects and what interactions withthe business problem ownerthey 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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