How can I use data to understand my organisations situation and performance?

From 2006 – 2010 I taught a Quantitative Methods course at ESCP Europe. I enjoyed it immensely and got great feedback from students. Since then I’ve continued to gather material but realise that no-one gets to see it. So I’ve created an online course.

The course should be of use to anyone with an interest in an MBA level education, and I have attempted to supplement my own presentations with links to some exceptional online resources.

The rest of this page contains some further resources and links.




I hope you find the online course useful, but I am also a fan of the old fashioned way. The course ties in to the following textbook:


It is a very good one: well written, full of examples, and plenty of opportunities to test yourself. You could do a lot worse than simply order it now and then work your way through it.

I’m also intrigued by “Calculus Made Easy“, by Silvanus Thompson. It’s antiquated in format but highly directed toward simplifying concepts and engaging with the reader. Statistics Done Wrong also looks fun.

Pop analytics

I believe that a good way to prepare for a subject is to read a book that is captivating. Something that stimulates your interest and encourages you to dig deeper. There are lots of bestsellers that have attempted to communicate mathematical ideas to the educated layperson. My favourite 6 are these:

Other online courses

The University of Bristol Medical school has a lovely Research Methods & Statistics online course. Introductory Statistics, an online course by Andy Field. It is full of some excellent tutorials that are presented with a unique style. The New York Times have made their course on data skills publicly available, there’s a link here. A thorough course designed for incoming PhD students is the Princeton Sociology Summer Methods Camp 2019. Here is the free course MITx Probability.

I also love this course: Calling Bullshit in the Age of Big Data.

There are also lots of proper online courses to choose from. The only one I have direct experience of is this:

If you really need to develop your QM skills then I would recommend you follow the HBS one instead of mine. However I found it pretty dull and failed to complete it. I’m hoping that by providing a mixture of content you will find mine more enjoyable.

Other resources

Instrumental variables: this tweet and these lecture notes

False positives and coronavirus.

Examples of bitemporal charts (especially good on economic forecasts)

Video on Bayes’ theorem.

Double y axis.

Software and misc.

Daniel Kunin has a wonderful website called “Seeing Theory“, which allows users to visualise basic concepts in statistics. I’ve integrated links into the course below.



APPENDIX: Contoversies

I believe that the best way to internalise the key concepts in this course is to conduct a replication exercise. These have become increasingly common as ways to apply the concepts covered, and test a students knowledge retention. To be honest though I am yet to find any really good examples of statistical tests that companies have utilised, and for which the underlying data set is available.

In their textbook, “Modern Principles: Macroeconomics”, Tyler Cowen and Alex Tabarrok present a good exercise to replicate a Solow Model. My (flawed) attempt to combine two of their problem sets is here:

Whilst I continue to look for potential replications, one option is to focus on some controversial statistical debates. These are also good ways to go deeper into the theory, and fully appreciate the link between theory and practice.

After party

You should now be a savvy consumer of statistical analysis and passionate about good data management. I recommend that you treat yourself to the following tome:


Thank you for visiting.

Last updated: June 2019