In this article, we’ll focus on how to look for data science jobs. You’ll first learn all the places where you can find jobs, making sure you won’t unknowingly narrow your options. We’ll cover how to decode these descriptions to find out what skills you need (spoiler: it’s not all of them) and what the job might be like. Finally, you’ll learn how to choose which ones are best suited for you, using your knowledge about data science skills and company archetypes.
From Code like a Pro in C# by Jort Rodenburg
This article gives an overview of exactly how C# code is compiled.
From Street Coder by Sedat Kapanoǧlu
This article discusses the importance of using data types in programming for writing and maintaining code bases.
From The Art of Network Penetration Testing by Royce Davis
From High-Performance Python for Data Analytics by Tiago Rodrigues Antao
In this article we will start to explore Python’s framework for concurrency – the first step in developing parallel applications.
From Chaos Engineering by Mikolaj Pawlikowski
This article explores how you can apply Chaos engineering principles to make your team better.
From Operations Anti-Patterns, DevOps Solutions by Jeffery Smith
This article covers
Longer release cycles and their impact to the team’s deployment confidence
Automation techniques for deployments
The value of code deployment artifacts
Feature flags for releasing incomplete code
From Unified Logging with Fluentd by Phil Wilkins
This article describes how and when to deploy Fluentd.
From Learn Kubernetes in a Month of Lunches by Elton Stoneman
This article delves into getting started running pods with controllers in Kubernetes.
From Practices of the Python Pro by Dane Hillard
This article covers
• Recognizing the signs of tightly coupled code
• Strategies for reducing coupling