Guest blog post by Michael Li, Head of Analytics and Data Science at LinkedIn. I’m sure everyone who has been following tech industry news knows about “big data” an...
Written exclusively for Data Science Central, by Vincent Granville. These articles are intended for non-experts, written in simple English, and particularly suited for pr...
Cambridge Analytica was caught tampering with elections by exploiting Facebook, but chances are that this is the tip of the iceberg, and that many others, including scamm...
The answer to this question is not black and white, and also depends on where you live, what you did during your PhD program, how much time and money you spent on it, wha...
Ajit’s research is focused on Data Science for IoT. He teaches the same at Oxford University and UPM in Madrid (@forumoxford + @citysciences). Ajit is also launchin...
What do experienced data scientists know that beginner data scientists don’t know? Here is a quick overview. Automating tasks. Writing code that writes code. Outsou...
This is a quote by George E Box. Share if you like it. In short, all models are approximations. All models are wrong, but some are useful. George E Box (18 October 1919 ...
Summary: Automated Machine Learning has only been around for a little over two years and already there are over 20 providers in this space. However, a new European AM...
This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, c...
This article is intended for practitioners who might not necessarily be statisticians or statistically-savvy. The mathematical level is kept as simple as possible, yet I ...