This is a simple overview of the k-NN process. Perhaps the most challenging step is finding a k that’s “just right”. The square root of n can put you i...
Summary: Based on a McKinsey study we reported that 47% of companies had at least one AI/ML implementation in place. Looking back at the data and the dominance of RPA...
Determining sample sizes is a challenging undertaking. For simplicity, I’ve limited this picture to the one of the most common testing situation: testing for differ...
This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlati...
Key topics of this blog: Economies of scalehave historically given large enterprises unsurmountable market advantages through the exploitation of mass production, distrib...
This is part 2 of a 3 part series: “How to make your mark on the world as a talented, socially conscious data scientist.” You can find part 1 here: “Choose a dom...
This article was written by Krishna Kumar Mahto. So, three days into SVM, I was 40% frustrated, 30% restless, 20% irritated and 100% inefficient in terms of getting my ...
Witnessing the data science field’s meteoric rise in demand across pretty much all industries and areas of scientific research, it’s easy to anticipate efforts to cre...
Every dataset contains a “Where” component. For organizations that need to use location to drive their business, it is critical that they can unlock the Where in thei...
If you’ve read your fair share of tech press, you’ve certainly been exposed to breathless forecasts about the promise and power of artificial intelligence. The thing ...