Preparing Yourself for a Job in Data Science, Part 3: finding a job

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.

How is C# Compiled?

From Code like a Pro in C# by Jort Rodenburg

This article gives an overview of exactly how C# code is compiled.

Making Types Work for You

From Street Coder by Sedat Kapanoǧlu

This article discusses the importance of using data types in programming for writing and maintaining code bases.

Compromising a Microsoft SQL Server

From The Art of Network Penetration Testing by Royce Davis

Implementing a MapReduce Framework Using Python Threads

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.

Chaos Engineering (for) People

From Chaos Engineering by Mikolaj Pawlikowski

This article explores how you can apply Chaos engineering principles to make your team better.

Fear of Deployments

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

Deployment of Fluentd

From Unified Logging with Fluentd by Phil Wilkins

This article describes how and when to deploy Fluentd.

Running Containers in Kubernetes with Pods and Deployments

From Learn Kubernetes in a Month of Lunches by Elton Stoneman

This article delves into getting started running pods with controllers in Kubernetes.

Achieving Loose Coupling

From Practices of the Python Pro by Dane Hillard

This article covers

•  Recognizing the signs of tightly coupled code

•  Strategies for reducing coupling

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