eMBA
Innovation and Data Science
Philippe J.S. De Brouwer
updated: 2023-11-06
Course Plan
Purple 30
| Date | Time | Mode | Content |
|---|---|---|---|
| 2023-11-17 | 13:30–17:00 | in classroom | Introduction |
| 2023-12-01 | 13:30–17:00 | online | starting with R |
| 2023-12-08 | 13:30–17:00 | in classroom(?) | Business plans and MCDA |
| 2024-02-07 | 09:00–12:30 | online | building models (regressions) |
| 2024-03-08 | 13:30–17:00 | online(?) | building models (machine learning) |
| 2024-03-14 | 13:30–17:00 | online(?) | Selected Topics (AI applications, Quantum Computing, Applications of Quantum Computing, Blockchain) |
| 2024-03-15 | 13:30–17:00 | in person | Exam (your presentations) |
Velvet 30
| Date | Time | Mode | Content | |
|---|---|---|---|---|
| 2023-11-17 | 9:00–12:30 | in classroom | Introduction | |
| 2023-12-07 | 13:30–17:00 | in classroom(?) | starting with R | |
| 2024-01-18 | 13:30–17:00 | in classroom | Business plans and MCDA | |
| 2024-01-19 | 13:30–17:00 | in classroom | building models (regressions) | |
| 2024-02-02 | 13:30–17:00 | online | building models (machine learning) | |
| 2023-04-18 | 13:30–17:00 | in person | Selected Topics (AI applications, Quantum Computing, Applications of Quantum Computing, Blockchain) | |
| 2023-04-19 | 9:00–12:30 | in person | Exam (your presentations) |
The Program
In this program we examine history to understand the impact of exponential growth, capitalism, welfare, and innovation. We understand how important the next wave of development is and how it is related to artificial intelligence (AI).
AI uses data and data-driven quantitative models and decision making are the cornerstone of this program. We start with an excursion in the vast world of multi-criteria decision analysis (MCDA) in order to appreciate that most decisions can be brought back to data-driven decisions.
From there we dig deeper to understand this artificial intelligence. We use the software R to make simulations, use data, and even build models.
Throughout the course, students work on a project that –hopefully– is of use elsewhere and we improve together the data driven recommendations in the paper.
Exam: presentations of projects by students
For this program students make
- a paper (min. 5 pages)
- a presentation
about a data-driven decision. This might be part of another project and/or end-work. The presentation of the projects counts as exam.
Background Material
- The Big R-Book: From Data Science to Learning Machines and Big Data
- examples and other material from my book
- 50 minutes summary of matematical models
- my channel on YouTube
- the data science channel
Back to www.de-brouwer.com