
Course Modules
[1] Introduction
- introduction to innovation
- the role of innovation in the history of capitalism
- setting expectations and agreements
- installing R and RStudio
[2] Regressions and Machine Learning
- linear regressions
- generalised linear models
- non-linear regressions
- decision trees
- random forests
- SVN
- neural networks
[3] case studies and models
- recap of theory (and continuation)
- case studies and practice
[4] Business Plans and MCDA
- Elements of a Business plan
- MCDA (Multi Criteria Decision Analytics)
- case studies MCDA
[5] from Accounting to Creating Value
A selection from:
- financial accounting
- management accounting
- valuation of cash, bonds, equities
- other financial instruments
[6] selected innovation axis
A selection from:
- automated reporting
- preparation for the end-project
- crypto ledgers
- quantum computing
- Big Data
Guest lectures:
- the use of data in warehousing (Frigo Logistics)
- the value of data (Colgate)
- calculating the value of a company (bank)
[7] Exam: presentations of projects by students
- presentations of the projects (counts as exam)