At Stitch Fix, we’re transforming the way people find what they love. Our clients want the perfect clothes for their individual preferences—yet without the burden of search or having to keep up with current trends. Our merchandise is curated from the market and augmented with our own designs to fill in the gaps. It’s kept current and extremely vast and diverse—ensuring something for everyone. Rich data on both sides of this 'market' enables Stitch Fix to be a matchmaker, connecting clients with styles they love (and never would’ve found on their own).

Our business model enables unprecedented data science, not only in recommendation systems, but also in human computation, resource management, inventory management, algorithmic fashion design and many other areas. Experimentation and algorithm development is deeply engrained in everything that Stitch Fix does. We’ll describe a few examples in detail as you scroll along.

In this guide, we present a reading list to serve as a concise introduction to Bayesian data analysis. The introduction is geared toward reviewers, editors, and interested researchers who are new to Bayesian statistics. We provide commentary for eight recommended sources, which together cover the theoretical and practical cornerstones of Bayesian statistics in psychology and related sciences.

I was talking the other day with a former student at UW, Sarah Rich, who’s done degrees in both math and CS and then went off to Twitter. I asked her: so what would you say to a math Ph.D. student who was wondering whether they would like being a data scientist in the tech industry? How would you know whether you might find that kind of work enjoyable? And if you did decide to pursue it, what’s the strategy for making yourself a good job candidate?

Sarah exceeded my expectations by miles and wrote the following extremely informative and thorough tip sheet, which she’s given me permission to share.

A peek into the clickstream analysis and production pipeline for processing tens of millions of daily clicks, for thousands of articles.

Pulled from the web, here is a our collection of the best, free books on Data Science, Big Data, Data Mining, Machine Learning, Python, R, SQL, NoSQL and more.

Professors Elizabeth Matsui, Professor of Public/Population Health at Johns Hopkins University, and Roger Peng, Biostatistics Professor at Johns Hopkins University, talk about life in the academic trenches, telling it like it is. Topics typically cover data science, public health, medicine, and academia.

Partially Derivative is a podcast about data science in the world around us. Episodes are a mix of explorations into the techniques used in data science and discussions with the field's leading experts. The podcast is a personal project hosted by Jonathon Morgan, Vidya Spandana, and Chris Albon -- three experts in data science.

I was recently trying various outlier detection algorithms. For me, the best way to understand an algorithm is to tinker with it. I built a shiny app that allows you to play around with various outlier algorithms and wanted to share it with everyone.

The shiny app is available on my site, but even better, the code is on github for you to run locally or improve! Let me give you a quick tour of the app in this post. If you prefer, I have also posted a video that provides background on the app. Another tutorial how to build a interactive web apps with shiny is published at DataScience+.

A group blog on Statistical Modeling, Causal Inference, and Social Science led by Andrew Gelman, professor of statistics and political science and director of the Applied Statistics Center at Columbia University.

KDnuggets is a leading site on Business Analytics, Big Data, Data Mining, and Data Science, and is managed by Gregory Piatetsky-Shapiro, a leading expert in the field.

R-Bloggers is an aggregator website where you will find daily news and tutorials about the R statistical computing software for statistics, data science, and more, contributed by over 573 bloggers.

A blog on data visualization, data science, and biology by Thomas Lin Pedersen.

This repository contains the exercises for the EPFL master course EE-558 A Network Tour of Data Science (moodle). The Data Scientist toolkit, a set of tools, mostly in Python, to help during the Data Science process. Machine Learning (ML) & Graph Signal Processing (GSP) algorithms. These exercises are designed so as to familiarize yourself with the algorithms presented in class.

48 video lectures on Python for data analysis and visualization from the PyData SF 2016 conference held on August 24, 2016.