This is the fourth and final part of a series of posts on “Anti-patterns” in data science. View the previous posts here: 1 2 3 In this (final) post of the series, I will cover a few points to be careful about while building models and drawing inferences and interpretations based on such models. Model
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