Category: python

Become a Data Science Professional

Become a Data Science Professional

Amit Ness gathered an impressive list of learning resources for becoming a data scientist.

It’s great to see that he shares them publicly on his github so that others may follow along.

But beware, this learning guideline covers a multi-year process.

Amit’s personal motto seems to be “Becoming better at data science every day“.

Completing the hyperlinked list below will take you several hundreds days at the least!

Learning Philosophy:


How a File Format Exposed a Crossword Scandal

Vincent Warmerdam shared this Youtube video which I thoroughly enjoyed watched. It’s about Saul Pwanson, a software engineer whose hobby project got a little out of hand.

In 2016, Saul Pwanson designed a plain-text file format for crossword puzzle data, and then spent a couple of months building a micro-data-pipeline, scraping tens of thousands of crosswords from various sources.

After putting all these crosswords in a simple uniform format, Saul used some simple command line commands to check for common patterns and irregularities.

Surprisingly enough, after visualizing the results, Saul discovered egregious plagiarism by a major crossword editor that had gone on for years.

Ultimately, 538 even covered the scandal:

I thoroughly enjoyed watching this talk on Youtube.

Saul covers the file format, data pipeline, and the design choices that aided rapid exploration; the evidence for the scandal, from the initial anomalies to the final damning visualization; and what it’s like for a data project to get 15 minutes of fame.

I tried to localize the dataset online, but it seems Saul’s website has since gone offline. If you do happen to find it, please do share it in the comments!

Bayesian Statistics using R, Python, and Stan

Bayesian Statistics using R, Python, and Stan

For a year now, this course on Bayesian statistics has been on my to-do list. So without further ado, I decided to share it with you already.

Richard McElreath is an evolutionary ecologist who is famous in the stats community for his work on Bayesian statistics.

At the Max Planck Institute for Evolutionary Anthropology, Richard teaches Bayesian statistics, and he was kind enough to put his whole course on Statistical Rethinking: Bayesian statistics using R & Stan open access online.

You can find the video lectures here on Youtube, and the slides are linked to here:

Richard also wrote a book that accompanies this course:

For more information abou the book, click here.

For the Python version of the code examples, click here.

Handling and Converting Data Types in Python Pandas

Handling and Converting Data Types in Python Pandas

Data types are one of those things that you don’t tend to care about until you get an error or some unexpected results. It is also one of the first things you should check once you load a new data into pandas for further analysis.

Chris Moffit

In this short tutorial, Chris shows how to the pandas dtypes map to the numpy and base Python data types.

A screenshot of the data type mapping.

Moreover, Chris demonstrates how to handle and convert data types so you can speed up your data analysis. Both using custom functions and anonymous lambda functions.

A snapshot from the original blog.

A very handy guide indeed, after which you will be able to read in your datasets into Python in the right format from the get-go!

Using data type casting, lambda functions, and functional programming to read in data in Python. Via

How most statistical tests are linear models

How most statistical tests are linear models

Jonas Kristoffer Lindeløv wrote a great visual explanation of how the most common statistical tests (t-test, ANOVA, ANCOVA, etc) are all linear models in the back-end.

Jonas’ original blog uses R programming to visually show how the tests work, what the linear models look like, and how different approaches result in the same statistics.

George Ho later remade a Python programming version of the same visual explanation.

If I was thought statistics and methodology this way, I sure would have struggled less! Have a look yourself:

100 Python pandas tips and tricks

100 Python pandas tips and tricks

Working with Python’s pandas library often?

This resource will be worth its length in gold!

Kevin Markham shares his tips and tricks for the most common data handling tasks on twitter. He compiled the top 100 in this one amazing overview page. Find the hyperlinks to specific sections below!

Quicklinks to categories

Kevin even made a video demonstrating his 25 most useful tricks: