Learning data science with John Oliver

You know this guy, right? In case you don’t, he is John Oliver, an english comedian with a perspective on the modern world that can only be matched by his distinctive voice!

I saw the video below sometime ago. In it, John Oliver presents in his usual style what is wrong with how science is used and presented. I won’t discuss the large amount of pet peeves I have with what I see on mainstream media or shared on Facebook regarding science or the lack of it. It would be out of context, too long and, to be intellectually honest, incredibly boring especially after John’s tremendous piece.

Instead I want to invite to watch the video in case you haven’t and I’ll tell you why I believe his views are important in the context of data science also. 

You might be a data redneck…

I like comedy a lot and stand up in particular. Some years ago I saw a video of The Blue Collar Comedy Tour. While I am not a fan and was only mildly entertained, there was a piece of it by Jeff Foxworthy that, I learned later, it’s sort of his stand up business card. That piece is widely known as “You might be a redneck”.

To Jeff, the definition of redneck is and I quote “The glorious absence of sophistication”. Let’s save this bit for later…

The reason why I’m writing this post is because in this day and age every knowledge worker claims to be data driven(*)… and many aren’t. This is a very touchy subject. The reason is simple. If everyone around me says they are data driven, it is very hard for me to admit that I’m not. It is even harder to say “I don’t know” when everyone seems to know.

Trust me on this, most don’t know! It is ok to not know. It is the prerequisite to start learning. The problem is that with so many people “knowing” there is a vast widespread glorious absence of data sophistication… See what I did there? 😉

How to come up with the important business questions?

Sometime ago I wrote a blog post on why questions are better than data. Zach Riegler from Upsight commented:

Very insightful post. Now for the most important questions – what is the best way to come up with the most important business questions to ask…?

This post is about that: the important questions. What they are and how to get them. It’s about the thought process and a sneak peak on how analysts, scientists and statisticians translate business questions to quantitative questions that can be answered with data.

Let’s go! 

Planning Game Analytics from 0 to data science

Game analytics can be very simple or go wide, far and deep. The trick is to define what it is that you will want on a given timeframe. The length of the timeframe depends on how sophisticated and complex are your objectives.

This post will go through the role that sophistication and complexity take in defining both your objectives and the analytics stack to support them.

So you want to have game analytics, huh?

I’ve asked game development communities on Facebook and Reddit what was it that they were interested in the context of this blog. I expected a large number of interests, but the truth is that most requests were in the lines of “how do I start?” The Setting Up Game Analytics category of posts that I start today is about that. How to setup game analytics in your studio. From planning and choosing the technology to defining events and integration with external services. I expect many posts in this category.