Augmented Intelligence is the New Artificial Intelligence by Priya Dialani via @analyticsinme

Computers are getting more clever and increasingly inventive, offering terrific prospects to improve the human condition. There’s a call to rethink Artificial Intelligence as Augmented Intelligence, to stress the capability of people working with AI instead of being replaced by AI. 

I found this very interesting and well worth a read and think about it.

Why You Should Get Google’s New Machine Learning Certificate by Frederik Bussler via @kdnuggets

 Google is offering a new ML Engineer certificate, geared towards professionals who want to display their competency in topics like distributed model training and scaling to production. Is it worth it?

This was really interesting and definitely made the case as to when you should or should not take the certification exam. I like the idea that you might be better off getting lots of experience and publish some results to get known for your skill and knowledge in machine learning instead.

The 7 Programming Languages & Frameworks to Learn in 2020 by Kent Sia via @thestartup_

 Curated recommendations for passionate coders

Great advice and some good tips on what to learn next from Kent.

How to Understand COVID-19 Numbers by @CarolineYLChen via @propublica

Between case counts, deaths, hospitalizations, positivity rates, rolling averages, etc, it's hard to gauge how the pandemic is progressing. Here's a detailed guide to the numbers and, ultimately, insights into what's worth paying attention to.

I found this incredibly interesting both from a person POV to make sure I understood all the numbers politicians throw around and from a data science perspective because it makes you think a bit more about if you need to explain all the numbers and statistics at people to make sure they really understand what you are giving them.

How Much Math do you need in Data Science? by Benjamin Obi Tayo via @kdnuggets

There exist so many great computational tools available for Data Scientists to perform their work. However, mathematical skills are still essential in data science and machine learning because these tools will only be black-boxes for which you will not be able to ask core analytical questions without a theoretical foundation. #DataScienceHub

My advice is to do a series of mathematical and statistics courses online - many are completely free especially via MOOCs - and bring your skills up to scratch. I certainly had to do that as my own skills were not good enough (and probably still aren't if I am honest with myself). 

Labelling Data Using Snorkel by Alister D’Costa and others at NLP4H via @kdnuggets

In this tutorial, we walk through the process of using Snorkel to generate labels for an unlabelled dataset. We will provide you with examples of basic Snorkel components by guiding you through a real clinical application of Snorkel.

This was really interesting and could be a way to save time in the long run.

Do You Know Python Has A Built-In Database? by Christopher Tao via @TDataScience

An introduction of the Python built-in library - sqlite3.

This is very useful and something that I never really understood until I read this.  Includes some code examples and a link to the documentation.