Day 57: Don’t read Data Science Central

Couldn’t disagree with this more.

Academics should write more code, not less. But we also need to be encouraged to do it right. More RSEs (e.g. @walkingrandomly), better tools.

Some very important tools are academic-written. E.g. Pandoc is by a philosopher. @johnlmacfarlane https://t.co/DMlyH2UOKa

— Neil Lawrence (@lawrennd) July 15, 2018

Real risk is incorporating other’s code without understanding or testing it.

It doesn’t matter who wrote it, bad code is bad code.

But let’s not point at a group and say “don’t write code”, let’s help everyone to write better code.

— Neil Lawrence (@lawrennd) July 15, 2018

Kaggle just released a new Python course based on the wildly successful 7-day Learn Python Challenge.

Check it out: https://t.co/dwMs9fnvEt

Great explanations, and a range of exercises that will be fun for both new and experienced Python programmers.

— Dan Becker (@dan_s_becker) July 16, 2018

Foundations of Machine Learning by Bloomberg, a training course that was initially delivered internally to the company's software engineers (30 videos):https://t.co/zJMyROIxEM

— fastml extra (@fastml_extra) July 15, 2018

ICYMI, 👍 weekend read…
📄 "Using Typography to Expand the Design Space of Data Visualization" by @rkbrath & Ebad Banissihttps://t.co/P6cB0Cz3co #dataviz #infovis pic.twitter.com/M6dgV2DFyK

— Mara Averick (@dataandme) July 15, 2018

A Visual Debugging Tool for Sequence-to-sequence Models #ieeevis
By @harvardnlp @IBMResearch @henddkn @S_Gehrmann

Githubhttps://t.co/9ubmBVQNqn
ArXivhttps://t.co/HHlNdnYxGP pic.twitter.com/goyDJiHPYb

— ML Review (@ml_review) July 16, 2018

Data Scientists: “I don’t have any production code just these Python Jupyter Notebook analyses that inform a multi-billion dollar company’s strategy.” 👏 PRODUCTION 👏 CODE 👏

— Justin Bozonier (@databozo) July 15, 2018

Don’t read Data Science Central. The man who runs it wrote this about another data scientist. pic.twitter.com/l1kqknVDpr

— Chris Albon (@chrisalbon) July 15, 2018

A+ for this Investigative data-rich journalism. Worthy of best of NateSilver and 538. Careful documentation of research methods as well. Toronto Star stars! https://t.co/JQgBKasCKp via @torontostar

— Edward Tufte (@EdwardTufte) July 15, 2018