While so many people are trying to enter the field of data science, it’s easy to get misguided and focus on the wrong stuff. Peter Scobas shares his experience on how he made the jump from economics to data science: https://peterscobas.com/2019/04/26/how-i-became-a-data-scientist/.

As a follow-up, you can read about new roles of analytics that have emerged recently: data product owner and analytics translator.

I stumbled upon this great post on the evolution of data engineering, which led me to the Medium of Maxime Beauchemin, the creator of Apache Airflow: all very good posts, like this one on functional data engineering.

Google just released their People + AI Guidebook about the multidisciplinary and human-centered approach to designing with machine learning and AI

It’s not entirely clear what level of mathematics is necessary to get started in machine learning. If you need to brush up your math skills, this course by Microsoft on EdX provides a hands-on approach on topics like equations, functions, vectors, matrices, statistics and probability.

Microsoft Research just release a free book that covers the theory for data science they expect to be useful in the next 40 years. The book is available for free as a pdf here, you can also watch the presentation video.

Andreas Ketz, the host of Plumbers of Data Science just released a first draft of his Data Engineering Cookbook.

You can download the PDF for free here.

DigitalOcean just announced the launch of their Managed Kubernetes service (https://blog.digitalocean.com/doks-in-ga/) during KubeCon. As part of their effort to push Kubernetes, they’ve also made available all their resources and guide about Kubernetes in one place.

I haven’t watched it yet but that sounds super interesting, this talk about strategies for testing Async code from the last PyCon.

See you next week! 🙂