Data Science in Business -M-

This course is dedicated to conveying a sense of how to structure analytic projects systematically, from understanding of the business problem over modelling up to model assessment and communication of the project's results (or a project proposal) to a client. The course introduces such a structure with an applied, step-by-step introduction that mixes theory and practical, hands-on implementation tasks (using programming in R). The course also comprises an introduction to visualisation techniques and creation of interative charts, maps and dashboards with Tableau.

Whilst several fundamental data science techniques are introduced, explained and worked with, we do mainly focus on the overall analytic project process. Ultimately, this will also help to evaluate project pitches from a client’s perspective.

F. Provost and T. Fawcett. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O’Reilly, 2013

Lectures interwoven with in-class exercises (students must bring their own laptops).

Group presentation and report: 40%

Exam: 60%

Basic familiarity with the R language and the R Studio environment is expected as the course starts from day 1 with some programming exercises.

150