Explaining negative R-squared by Tan Nian Wei via @TDataScience

Why and when does R-squared, the coefficient of determination, go below zero?

Interesting and good to be able to confirm it is how I thought it was.

A Step-by-Step Guide to Calculating Autocorrelation and Partial Autocorrelation by Eryk Lewinson via @TDataScience

How to calculate the ACF and PACF values from scratch in Python.

This was very clear and really helped me to understand how to calculate them in Python as I'm really not good at that language no matter how hard I try.

How Netflix uses A/B tests to inform decisions and continuously innovate by/via @NetflixEng

Here are the first four parts in the multi-part series from the Netflix blog on how they use A/B tests to innovate their products.

#1 Decision Making at Netflix

#2 What is an A/B Test?

#3 Interpreting A/B test results: false positives and statistical significance

#4 Interpreting A/B test results: false negatives and power

I strongly recommend that you follow the Netflix blog as you will find a lot of really great educational information that are not just dry lessons but are based on real-life knowledge and experience.

Interpreting Correlations - an interactive visualization by/via @krstoffr

 This is a great tool that needs to be used. Just change all of the settings via the cog at the top right hand.

Similarity and Distance Metrics for Data Science and Machine Learning by Gonzalo Ferreiro Volpi via @Medium

As applied in Recommendation Systems.

I found this really interesting and it includes the mathematical formulae and some code so you can relate it to your own world.

How Companies Can Use the Data They Collect to Further the Public Good by Edward L. Glaeser,Hyunjin Kim and Michael Luca via @HarvardBiz

The potential value of the large data sets being amassed by private companies raises new opportunities and challenges for managers making strategic data decisions.

I like the steps in the article but just like using any other data be careful of correlation and causation as it is very easy to mistakenly interpret one for the other.

Spurious Corrrelations by @TylerVigen

I just LOVE these and they are a great lesson to any aspiring data scientist or data analyst.

If Correlation Doesn’t Imply Causation, Then What Does? by @akelleh via Medium

Adam Kelleher’s interesting post looks at when and why you might want to use causality.

He has a second post here which discusses all about understanding bias.

Please read these at least twice as it's worth understanding these articles and the points within them well.