This is a repo with links to everything you'd ever want to learn about data engineering

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…

Automatic extraction of relevant features from time series:

Recipes for using Python's pandas library

An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code

A concise, beginner-friendly introduction to the core ideas of linear algebra.

Data and methodology for the Big Mac index

IPython kernel for Torch with visualization and plotting

Data Hacking Project

Statistics Tutorial for IT Operations Engineers

Presentations from H2O meetups & conferences by the H2O.ai team

IPython notebooks and slides for talks I've given

Perceived UX of Web Apps in the Wild : Benchmark data, exploratory analyses and insights from large-scale crowd sourcing

DEPRECATED - A Framework for testing web performance between different browser

Elasticearch IPython magic

DataScience for Effective Operations

Material from my presentation to the Cheltenham Geek Night on 8th December 2015.