We provide more details about the scientific approach and we introduce probabilities to understand how and why certain decisions lead to some outcomes instead of others and how to make better decisions. We also focus on how to formulate and test hypotheses in practice, and how to interpret these tests. We finally discuss how to design and run experiments. NB: some videos may contain a downloadable database; please, download it and follow the in-video instructions

We cover the basics of data analysis, beginning with the distinction between correlation and causality in the analysis of data. We also teach how to make predictions using regression analysis and link these methods to the scientific approach, showing what role these analyses play, how they help to make scientific decisions and why. We complement this with real examples of companies using data to make innovation decisions. We close by discussing how to interpret these analyses and results critically to make sure we understand what we really learn from the analyses and when, how and why we should interpret our results cautiously and critically.

This is s a more advanced part in which we discuss causality and provide the students with some broad exposure to big data and machine learning, and we discuss what they can do for managerial decisions.We provide a general wrap-up and conclusion of the course, including a discussion of when the scientific approach is most appropriate or has limitations. This helps to see when to apply it, or when to apply other approaches, including our own gut feelings. NB: some videos may contain a downloadable database; please, download it and follow the in-video instructions