Data Driven Entrepreneurship-E-M-B
The course deals with activities along the entire new product development (NPD) process from the first idea to market launch. Students will learn about managing different stages of the NPD process and apply their new knowledge in case studies. Apart from the classic knowledge about the NPD process there will be further one or two special topics discussed which mirror actual developments regarding NPD. The course helps to deepen the students' knowledge in NPD and innovation management.
Kurs ID
EAI624, EAI603
Art des Kurses
MSc Kurs
Wochenstunden
2,5
ECTS
5.0
Semester
FS 2023
Vortragssprache
Englisch
Vortragende/r
Prof. Dr. Dries Faems
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
Data are an increasingly important source for founders and investors to make entrepreneurial decisions. Moreover, the introduction of novel digital technologies has facilitated actors to collect and analyze a wide variety of data. The core purpose of this course is to introduce students to multiple methodological approaches and tools that can help them in executing data-driven entrepreneurship. To do so, the following topics will be addressed:
- Automate data cleaning and data merging
- Setting up a dashboard to generate business intelligence for startups
- Leveraging tools to generate competitor intelligence
- Applying natural language processing algorithms to identify potential partners/competitors
Throughout the different modules, we will use several software packages (e.g. Power BI, Python) to execute assignments. Students will be expected to self-learn the basics of these software packages (relevant learning material will be provided). In the modules, we will focus on applying these software packages to execute specific group and individual assignments. For the assignments, real entrepreneurial data will be provided and analyzed.
Termine
| Date | Time |
|---|---|
| Monday, 06.03.2023 | 15:30 - 18:45 |
| Wednesday, 08.03.2023 | 15:30 - 18:45 |
| Friday, 17.03.2023 | 08:00 - 11:15 |
| Tuesday, 28.03.2023 | 15:30 - 18:45 |
| Friday, 14.04.2023 | 08:00 - 11:15 |
| Monday, 17.04.2023 | 08:00 - 11:15 |
Literatur
The course will be structured as follows: Week 1: Introduction to course + Introduction to data cleaning and merging with PythonWeek 2: Building a dashboard using Power BI + Introduction to individual assignmentWeek 3: Individual feedback on assignmentWeek 4: Identifying competitors via NLP and SimilarWebWeek 5: Analyzing competitors based on financial data and SimilarWebWeek 6: Feedback on group assignment
Lernmethoden
Lecture, group work, in-class workshop
Art der Prüfung
Group assignment: 50%
Individual assignment: 50%
Umfang
150