I came across another great set of curated resources by one of the teams at Google:
. People + AI Guidebook
The People + AI Guidebook was written to help user experience (UX) professionals and product managers follow a
human-centered approach to AI.
The Guidebook’s recommendations are based on data and insights from over a hundred individuals across Google product teams, industry experts, and academic research.
These six chapters follow the
product development flow, and each one has a related worksheet to help turn guidance into action.
The People & AI guidebook is one of the products of the major
(People & AI Research). PAIR project team
Here are the direct links to the six guidebook chapters:
Links to the related worksheets you can find
The Institute for Ethical Machine Learning compiled this
amazing that will help you curated list of open source libraries deploy, monitor, version, scale, and secure your production machine learning.
Direct links to the sections of the Github repo
is a think-tank that brings together with technology leaders, policymakers & academics to develop standards for ML. Institute for Ethical Machine Learning
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.
this short tutorial, Chris shows how to the
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
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 pbpython.com/pandas_dtypes.html
on Schwabisch recently proposed ten guidelines for better table design.
Next to the
academic paper, Jon shared his recommendations in a Twitter thread.
Let me summarize them for you:
Right-align your numbers Left-align your texts Use decimals appropriately ( one or two is often enough) Display units (e.g., $, %) sparsely (e.g., only on first row) Highlight outliers Highlight column headers Use subtle highlights and dividers Use white space between rows and columns Use white space (or dividers) to highlight groups Use visualizations for large tables
Highlights in a table. Via twitter.com/jschwabish/status/1290324966190338049/photo/2
Visualizations in a table. Via twitter.com/jschwabish/status/1290325409570197509/photo/3
Example of a well-organized table. Via twitter.com/jschwabish/status/1290325663543627784/photo/2
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:
Another pearl of a resource on Twitter is
For your convience, I copied the links below. Just click them to browse to the resource and learn more about the concept
Click to learn more about each concept
Variables & Scoping Data types Objects, Funtions & Arrays Document Object Model (DOM) Prototypes & this. Events Flow Control (specifically, for-loops) Security & (web) Accesibility (to which I’ve linked Good coding practices before) Async
This 10-step list was compiled as apart of this
interesting podcast, which I recommend you listen to as well. Want to learn more?
There’s a (now classic) conference talk that comes with this book, which I can also recommend you watch: