This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow,…

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 present an original,…

The format is very similar to a BIG cheat sheet. This cookbook integrates a variety of topics in probability theory and statistics. It is based on literature and in-class material from courses of…

Understanding Data Roles

By Michael Burke

With the rise of…

These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vendor, or whether they design…

This article was written by Roopam Upadhyay. Roopam is a seasoned professional of advanced analytics with more than a decade of experience in statistical modeling, data science, predictive…

Big Data is not just the ability to store…

Model evaluation metrics are used to assess goodness of fit between model and data, to compare different models, in the context of model selection, and to predict how predictions (associated with…

We are now at 91 questions. We've also added 50 new ones …

Author: Gaurav Jha.

The way a message is communicated…

From querying your data and visualizing it all in one place, to documenting your work and building interactive charts and dashboards, to running machine learning…

Contributed Article from MIT Professor Devavrat Shah.…

Should you use linear or logistic regression? In what contexts? There are hundreds of types of regressions. Here is an overview for data scientists and other analytic practitioners, to help you…