Analytics – an online course


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. I currently teach QM at Cotrugli Business School, but only a few sessions. So I wanted to make the full course publicly available.

The aim of this website is to provide a (mostly) free guide to basic statistics. It 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. I say that it is mostly free because some of the cases are not available online and some articles require a subscription. If you have any difficulties accessing them, or would like me to recommend alternatives, let me know. I appreciate that Harvard cases and academic journals are expensive (regardless of whether you’re enrolled on a program or a member of the public). But they are high quality. Indeed if you are looking for a totally free course then this isn’t for you. The skills you develop in an Analytics course are valuable and important. If you are willing to invest your time in building them, you should also be willing to invest some money. But you can trust my recommendations. Everything below is worth it.

Before we begin

I hope you find this online course useful, but I am also a fan of the old fashioned way. These materials are intended to tie into the following textbook:


In fact, the materials are intended to complement this 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. 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. The University of Bristol Medical school has a lovely Research Methods & Statistics online course.

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.

Funnily enough, a few hours after I’d finished this page, a friend of me showed me this website:

It is full of some excellent tutorials that are presented with a unique style. You may prefer his site to mine.


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

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:

Finally, before you start this course you should read the following:

0. Pre-requisite

  • If you want to start at the very beginning then take my Numeracy Skills Bootcamp, which covers Fundamentals of Mathematics, some Practice Tests, and a discussion of Gender Differences & Mathematics.

1. Introduction

Prior to the lecture below, you may wish to ask the following question: “How expensive is crude oil?

Download the handouts here.

Why data needs theory:

Download the handouts here.

A good homework exercise is the following:

Find an example of a news report that sounds like a big number, but once some relevant calculations are made it is in fact unsubstantial.

Some good examples: here.

Bas pie chart: here.

Some other great resources:

 2. Descriptive statistics

Download the handouts here.

Download and complete Krupnik Estate Agents.xlsx

Additional readings:

3. Probability theory

The lecture below is very heavy. I suggest the following

  • Start off with a group activity: Freemark Abbey Winery (£)
  • Collect a Problem set on probability theory, Bayes theorem, permutations, and combinations
    • Socrative Quiz: Probability Theory
    • Socrative Quiz: Probability Theory: Krupnik Investment
    • Socrative Quiz: Probability Theory: Bayes Theorem
    • Socrative Quiz: Probability Theory: factorials, permutations and combinations

Download the handouts here.

Seeing Theory: Basic Probability and Compound Probability

Additional readings:

Download the handouts here.

Quiz on Distributions (original source here).

Seeing Theory: Compound Probability

  • Socrative Quiz: Probability Distributions

4. Inferential statistics

Download the handouts here.

Seeing Theory: Statistical Inference

Download the handouts here.

  • Socrative Quiz: Inferential Statistics

Additional reading:

Download the handouts here.

5. Non-parametric Statistics

Download the handouts here.

  • Socrative Quiz: Chi-square test

6. Correlation

Download the handouts here.

7. Regression analysis

Seeing Theory: Linear Regression

  • Socrative Quiz: Regression

Additional readings

8. Time series

Download the handouts here.

  • Socrative Quiz: Time Series

Further reading:

9. Data Presentation

10. Review

  • “Fermat’s Folly”, October 2018
    • Email me for the case and solutions

You can buy a hard copy of the complete slide deck here, or download a free PDF here.


A. Controversies

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.

B. Game Theory

Appendix: Handouts

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: October 2018

Leave a Reply