Modern Tools and Applications of Data Science -B-
Kurs ID
QUANT509
Art des Kurses
MSc Kurs
Wochenstunden
2,5
ECTS
5
Semester
HS 2021
Vortragssprache
Englisch
Vortragende/r
Prof. Dr. Arne Karsten Strauss
Bitte beachten Sie, dass AustauschstudentInnen im BSc-Programm der WHU eine höhere Anzahl an Credits erwerben als hier aufgeführt. Für weitere Informationen wenden Sie sich bitte direkt an das [International Relations Office].
Kursinhalt
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.
Termine
| Date | Time |
|---|---|
| Thursday, 28.10.2021 | 08:00 - 11:15 |
| Tuesday, 02.11.2021 | 15:30 - 18:45 |
| Thursday, 11.11.2021 | 11:30 - 15:15 |
| Tuesday, 16.11.2021 | 15:30 - 18:45 |
| Tuesday, 23.11.2021 | 08:00 - 11:15 |
| Thursday, 02.12.2021 | 11:30 - 15:15 |
| Monday, 06.12.2021 | 11:30 - 15:15 |
| Tuesday, 07.12.2021 | 09:45 - 11:15 |
| Wednesday, 15.12.2021 | 09:00 - 10:30 |
Lernerfolge
- Ability to structure analytic projects
- Ability to evaluate proposals for analytic projects
- Ability to deliver effective pitches for analytic projects
- Ability to effectively report analytic results to managers
- Ability to design interactive visualizations and dashboards
Literatur
F. Provost and T. Fawcett.Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O’Reilly, 2013
Lernmethoden
Lectures interwoven with in-class exercises (students must bring their own laptops).
Art der Prüfung
Weekly individual assignments based on real-life case study, assessed via Moodle quizzes: 15%
Group presentation: 25%
Exam: 60%
Umfang
150