Skip to content
Switch branches/tags

Latest commit


Git stats


Failed to load latest commit information.
Latest commit message
Commit time

Useful Resources

Data Analysis

Data Wrangling with Dplyr

A useful overview of less used dplyr functions. The first post in a series of four.

Hadley Wickham Showing Off

A video of Hadley Wickham showing how he does exploratory data analysis.

Pandas tutorials

The offical pandas docs sometimes leave a bit to be desired, but the tutorials and exercises here are very useful.

R for Data Science

The excellent book by Hadley Wickham about all things R related.

Data Visualization


This package allows you to create graphs declaratively, similar to ggplot. However, it is made for python and sits ontop of D3.js, so interactive plots can be made easily.

Data Illustrator

I haven't tried this, but it looks like an interesting tool for interactively building plots without code.

Data Visualization Checklist

A great resource not only for learning about what makes a good visualization, but allows you to check your own figures.

Design for an Audience

This is a transcript of an excellent talk from Jonathan Corum, the science graphics editor at the New York Times.


An interactive ggplot creator, useful for ggplot beginners

Storytelling With Data

Storytelling With Data is a blog all about improving your visualizations. There's often figure fix ups and reader competitions.

Learning to Program

Automate the Boring Stuff with Python

In my opinion, it's the best intro to python book that I've come across.


Codecademy is sometimes criticized as it leaves beginners without an understanding of how to actually write a program. However, as a bare bones intro or syntax refresher I think it does a great job. Just pair it with a book afterwards.

Mode SQL Tutorial

SQL is great if you are working with larger datasets, or even just for understanding the philosophy underneath many data analysis toosl. Mode's tutorial is all interactive so you get to actually practice with real data. If you have experience analyzing data already, SQL is relatively quick to pick up for how valuable it is.


Sentdex has amazing video tutorials for almost everything you could want to do with data in python.

Machine Learning has both a deep learning course and a basic machine learning course. It's unique from other resources as it builds top down and explains concepts through code rather than math.

Jeremy Howard's ML Course

A great, practical introduction to machine learning. Large focus on immediately useful tools like regression, random forest and xgboost.


Kaggle hosts machine learning competitions. The titanic dataset is a good place to start.



These are a series of excellent math tutorials. 3Blue1Brown uses beautiful visualizations to try to make complex topics clear, and focuses on teaching at a conceptual level. I found the linear algebra course to be particularly useful.

New and Improved Flask Mega-Tutorial

The old version of this tutorial was incredibly helpful for making my website. It's comprehensive and now up-to-date.

R Markdown: The Definitive Guide

A thorough description of R Markdown and everything you can do with it.

Speeding up R Package Installation

Really useful for slower computers.


A Hands-On Example of Bayesian Mixed Models with brms

This is a really useful intro to brms, a package that makes working with bayesian hierarchical mixed models in stan really easy.

An Introduction to Hierarchical Modeling

A great visual demo of what is happening in a hierarchical linear model (AKA a random or mixed effects model).

Calculating Effect Sizes

A useful article describing how to calculate effect sizes in a number of different situations, including spreadsheets to do the calculations.

Distill Article on Gaussian Processes

Gaussian Processes are really cool, but they never clicked for me until I saw this paper. Distill is also just an awesome model for what scientific publishing could like like in the future.

An Intro to GAMs by Michael Clark

If you wish your GLMs were wigglier you should start here.

MCMC Sampling Demo

A great introduction about how MCMCs sample distriubtions and why NUTS is useful.

PBR Special Issue

Interested in Bayes stats for psychology? This special issue in PBR has a ton of excellent papers on the topic.

Stan Intro

This is a great intro to stan using a practical example workflow.

Bayesian inference for psychologists using R & Stan

Bayesian stats specifically for psychologists

Statistical Rethinking

Richard McElreath's excellent course on bayesian statistics. Useful even for people who are feel like they need some help understanding the foundation of frequentist analyses.


A place to keep the things I've stumbled upon







No releases published


No packages published