PTMBA2027 Data Science and AI for Business

Participation Prerequisites

none

Course Content

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):

• Re-visiting 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
• Conceptual understanding of unsupervised learning (clustering, association rules)
• Conceptual understanding of text mining
• Generative AI - background and opportunities. Use Gen AI to conduct analytical tasks yourself without coding.
• 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.

Intended Learning Outcomes and Competencies

ability to effectively collaborate with data scientists and to assess data science projects

Instruction Type

In-person teaching

Form of Examination

There is one intermediate group assignment which counts 40%.

The final individual exam will make up 60% of the grade.

Literature

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

K. Dubovikov. Managing Data Science. Packt, 2019