This is the third part of the series of posts covering Anti-patterns in Data Science. Read Part1 and Part2. In this article, I cover a few points to be careful about, during experiment design and data collection. Again, as with the previous two articles, the block diagram describing the “process flow” for solving a problem is reproduced below.
This is the second part of a series of posts that should cover Anti-patterns in Data Science. Read the first part here. In this post, I’ll cover the first set of anti-patterns that on should avoid, or use with utmost care. The discussion will be split along the lines of the various blocks described in
It is imperative to understand what it means to go “Data-Driven” and why and how one should be careful about it. In a series of blogs, I’d like to summarize some learnings I had while navigating the “Data-driven” Quicksand: A wonderland where faulty analysis can lead you to misleading and dangerous interpretations of how world
It is not seldom that I encounter the following question while marketing a solution: “What are the hypothesis you are testing for and how will you prove or disprove it?”, thereby alluding to standard hypothesis testing techniques. In this context, I had a short conversation with a friend a couple of days back on the prevalent culture in many analytics organisations and academic