ISSUE 292 · June 30, 2020

Insight

The Recession’s Impact on Analytics and Data Science

There has been a huge demand for data scientists, analysts, and engineers in the past decade. Is that about to change?
MIT Sloan Management Review

Why Statistics Don’t Fully Capture Systemic Bias in Policing

Because of a statistical quirk called “collider bias,” the criminal justice system may be even more racially biased than studies suggest. Here's how collider bias works, including charts that clearly show the problem.
FiveThirtyEight | Laura Bronner

Tools and Techniques

STUMPY: a powerful and scalable time series library

STUMPY is a powerful and scalable Python library that efficiently computes something called the matrix profile, which can be used for a variety of time series analysis tasks. This series of posts explores how it works and how you can leverage it for modern time series data mining.
Sean Law

Mean Squared Terror

Nice thought experiment that shows how machine learning can amplify bias in a dataset and the hazards that could result.
Vincent D. Warmerdam

Google Colab Tips for Power Users

If you use Google Colab, this is a great collection of lesser-known features that will help boost your productivity.
Amit Chaudhary

Building AI Trading Systems

Lessons learned building a profitable algorithmic trading system using Reinforcement Learning techniques.
Denny Britz

Learn Data Analysis and Visualization with Python

You’ll explore the four crucial steps for any data analysis project: reading, describing, cleaning, and visualizing data. In each step, you will work with the most common and popular tools. By the end of the course, you will be able to confidently extract knowledge and answers from data using Python.
// sponsored

Resources

Comprehensive Python Cheatsheet

This exhaustive Python cheatsheet has been making its way around the web and has recently been updated with sections for Pandas and Plotly.
GitHub | Jure Šorn

Data Viz

Crowd-Testing Data Viz with ObservableHQ and Mechanical Turk

Technical guide for developing data visualizations in Observable Notebooks and then user-testing them on Mechanical Turk.
Eli Holder

Learning data viz with D3

In this companion article to her Getting Started with Data Viz Journal, Diana MacDonald shares her learning process, including an extensive collection of curated resources. This is well organized and thorough.
Diana MacDonald

Data Elixir is curated and maintained by Lon Riesberg. If you need help on a data project or have a suggestion for the newsletter, reply back to this email or grab a spot on my calendar >>