All Hands on Data #16
The NFL regular season starts tomorrow. Hopefully, these articles from our team can hold back your excitement for one more day.
Three Essential Soft Skills for Practical Data Scientists
People often underestimate the importance of soft skills in the STEM fields. The end goal of all that data processing and interpreting is often to share with people and to influence decisions…and that’s one of the place soft skills shine. The skills that de Harder mentions in this post are some of the most essential skills I would look for in a new team member. - Katt Baum
Data Science Lessons from Top Gun
As a lover of the data space and movies, this article really hit home with the title alone. Bill discusses the OODA loop which is interesting how we apply it in our jobs almost everyday without even thinking about it. - Steven Johnson
Why every SaaS company will offer native data pipelines
Sort of bucking the general data trend is the idea of “native data pipelines” - instead of one of the major ETL/ELT/Reverse ETL players offering this service, the article argues that it’s better to build them yourself as a SaaS provider. The article rightly notes that this does take considerable engineering effort and I would additionally note it moves you, the SaaS provider, away from your domain expertise and into data pipeline management. It might work for some, but I expect the data pipeline players are here to stay (for the time being). - John Forstmeier
Image Processing and Pixel Manipulation: Photo Filters
With all of the hype around AI generated images, it’s cool to see the math behind what exactly a digital image is and how to manipulate it. The article gives a foundation for how those text to image services could build on top of basic image manipulation. My favorite examples are the brightness and gamma corrections to better see the green in the trees. - Eric Elsken
How to Use Python to Loop Through HTML Tables and Scrape Tabular Data
I would assume almost every beginning Python developer has hit a point in their learning cycle where they see HTML code and feel lost. This article helps you get past that point where you can use data in HTML code in your analysis. - Steven Johnson