**Error Analysis to your Rescue – Lessons from Andrew Ng, part 3** - Jan 29, 2018.

The last entry in a series of posts about Andrew Ng's lessons on strategies to follow when fixing errors in your algorithm

Tags: Andrew Ng, Bias, Distribution, Machine Learning, Variance

**Data Science Primer: Basic Concepts for Beginners** - Aug 11, 2017.

This collection of concise introductory data science tutorials cover topics including the difference between data mining and statistics, supervised vs. unsupervised learning, and the types of patterns we can mine from data.

Tags: Bias, Data Mining, Data Science, Distribution, Ensemble methods, Statistics

**Stanford Webinar, Mar 9: When big data seems too small** - Feb 23, 2017.

On March 9, Stanford’s Dr. Gregory Valiant discusses the difficulties of and solutions for making accurate inferences in this challenging regime, in which the empirical distribution of the available data is misleading.

Tags: Big Data, Distribution, Small Data, Stanford

**Data Science Basics: Power Laws and Distributions** - Dec 21, 2016.

Power laws and other relationships between observable phenomena may not seem like they are of any interest to data science, at least not to newcomers to the field, but this post provides an overview and suggests how they may be.

Tags: Beginners, Data Science, Distribution

**Central Limit Theorem for Data Science – Part 2** - Aug 16, 2016.

This post continues an explanation of Central Limit Theorem started in a previous post, with additional details... and beer.

Tags: Beer, Centrality, Distribution, Statistics

**Central Limit Theorem for Data Science** - Aug 12, 2016.

This post is an introductory explanation of the Central Limit Theorem, and why it is (or should be) of importance to data scientists.

Tags: Centrality, Distribution, Statistics

**What Statistics Topics are Needed for Excelling at Data Science?** - Aug 2, 2016.

Here is a list of skills and statistical concepts suggested for excelling at data science, roughly in order of increasing complexity.

Tags: Bayesian, Distribution, Machine Learning, Markov Chains, Probability, Regression, Statistics

**Plausibility vs. probability, prior distributions, and the garden of forking paths** - Jan 14, 2016.

A discussion on plausibility vs. probability: while many given events may be plausible, but they can’t all be probable.

Tags: Andrew Gelman, Distribution, Probability, Statistics

**What is numbersense – test yours** - Mar 25, 2014.

Kaiser Fung, Marketing and Analytics expert, and author of "Numbersense" book, explains what is numbersense in the age of Big Data. Test yours.

Tags: Anomaly, Distribution, Kaiser Fung, Missing values, Numbersense, Outliers, US Census