[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)

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 models
• my channel on YouTube
• the data science channel