So the journey begins. And here is the first entry of my 5 year project to become a fully qualified and very employable Data Scientist.
I’ve been working on my Analytical piece which is set to come out next week on Thursday. I’ve been obsessing over Euros 2020, an international Football (soccer) competition currently being played out, ironically, in mid-2021. Due, perhaps obviously, to the fallout from COVID-19 and a year that has seen a whole lot of disruption to essentially the entire world. Given this, my Analytical piece for July will cover some of the many interesting stats that are related to modern sports. And speficially, those used in the Euros.
Something I have noticed, in my watching of the Euros is the awful misuse of statistics in football commentary. Commentators will have all sorts of different and interesting stats, but most of them begin to make a whole lot less sense and begin to look a lot less remarkable, once put under a little bit of scrutiny. For example, the commentator will remark that one of the teams playing has never beaten the other in competition. Neglecting to mention of course that they have only played each other two times before, one 30 years ago and the other 70 years ago. This lack of context really does highlight precisely how stats and data can be uniquely handled in order to create a particular impression. This is something that is very common and is on my list for an Analytical piece in the future to cover. It is a fascinating conversation that leads into concepts of political disconnect and political division, as well as how technology, and hence data, help to create tension and separation in the modern world.
I’ve also been looking at some financials, regarding in particular to my personal finance. And something that has struck me is how opaque a lot of managed funds are relating to their performance and the numbers that they publish. For example, they’ll mention their overall average growth over a 5 and 10 year period, without disclosing which 5 or 10 year period it refers to, which inevitably means you are comparing the best period for one fund over another best period of the competitor fund. All of which does not help an awful lot when looking at the life of an average investor who would only be a small part of many of the funds that they would be investing in. What would their returns look like? It’s just all rather unclear. That’s another little research project I’m working on.
As university is still on hold at the moment, to keep me sharp, I’m doing two courses on Coursera and two on Brilliant. They are: Coursera Data Science Math Skills, Coursera Foundations of Financial Markets, Brilliant Programming with Python, Brilliant Search engines.
All of which is to say. That there is a lot happening right now, and I look forward to giving you some more insights on this journey.