A look at why the ‘sexiest job of the 21st century’ has lost its appeal

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Driven to tears — when dream jobs turn bad (photo by Andrea Bertozzini on Unsplash)

Dream job?

We’re often bombarded with how popular data science is as a career. It’s all too common to read things like data science being the “Sexiest Job of the 21st Century” or yearly comparisons of high salary expectations.

Improving the #1 skill for data professionals

More than anything else, how you communicate will be the dominant factor in your success as a data scientist. Hear why and what you can do to turn communication into a strength.

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Get comfortable drawing a crowd, what you have to say and how you say it matters (photo by Wan San Yip on Unsplash)

I get asked a lot by aspiring data scientist questions about how to improve and what they should be focusing on. Things like:

  • What framework should I learn next?
  • What models should I be using?
  • Should I learn Julia or Spark next?

While these are all valid things to be considering (in the right context) for the vast majority of people they’re not where you should be focusing…

Books that’ll give you a wider perspective when working in data science and applying machine learning at scale

Reading a book
Reading a book
Photo by Lars Poeck on Reshot.

I first started coding during an extended hospital stay back in 2010. I told myself, “If I’m stuck here, I want to learn something useful.” Like most people who take the plunge, I soon got carried away with this newfound power! Even the incredibly simple things I was able to do in C++ opened my eyes to the possibilities and wonder of it all.

Tips to help you stand out from the pack and get you hired from an experienced data science leader

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Get hired! (photo by Sincerely Media on Unsplash)


Data science is a highly attractive career for many reasons — so the competition can be tough. Some of the tips below that make a great candidate stand out might surprise you!

  • You might just be starting out and trying to land your first role
  • You could be in a tangential technical field and want to make the switch
  • Or you might already be an experienced data scientist looking to brush up on your skillset.

Why is it so hard?

This is a question I often get at user groups and community events for aspiring or…

Get started with persisting data and state when working with data science containers. A guide to data storage and persistence in Docker.

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Photo by Fredy Jacob on Unsplash


In this post, we’re going to cover data persistence in Docker and how to get your images to interact with data outside of the container. If you’re familiar with Docker or have already walked through the previous posts in this series skip to the next heading!

Machine Learning Engineer essentials — How to set up private container repositories, secure them, deploy and share in the cloud with Azure

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Photo by Ian Taylor on Unsplash


If you’re already using Docker for your data science projects, then great —you might now want to learn how to manage and share your containers privately. This post will show you how to get a private repository up and running in Azure.


For this tutorial you’ll need to the following installed:

  • Docker — can be found here
  • Azure CLI — can be found here

If you’re not sure where to start with Docker I’ve written two-part series covering getting up and running, then deploying an end-to-end machine learning service in these two posts:

Part one walks through how to get…

How Deepfakes and AI could start to become more common in the media we consume.

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Channel 4’s Deepfake Queen’s Speech might be the start of a new way of making media (image courtesy of Channel 4).

As a brit, on Christmas Day I was quite happy to hear the call out for diversity and the nod to all those who have struggled through this year — especially those on the front line in key worker roles and in the NHS.

A complete guide to develop a machine learning model, write an API, package it in Docker, run it anywhere, and share it with ease.

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Docker for data scientists — an end-to-end guide (photo by Rémi Boudousquié on Unsplash).


In Docker for data scientists — Part 1 we covered some of the basics around using Docker and got a simple Hello World! example up and running. If you missed that post and want to run through the code, you can find it here:

Our aim in this post is to move on to an example of how a data scientist or machine learning engineer might want to use Docker to share, scale, and productionise their projects.

  • Model serving — develop and deploy an API for a simple model
  • Serialising models —…

The basics. A quick guide for data scientists and machine learning engineers to get started with Docker.

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Docker for data scientists — the argument for containers (Photo by Dale Staton on Unsplash)


We’re going to jog through the following topics in order:

  • It works on my machine
  • Virtual Machines (VMs) and containerisation
  • Docker — A Hello World! example
  • Next steps — Putting models into production, Kubernetes, Helm, MLOps, and DataOps

It works on my machine

Over my time working in data science I’ve seen many people struggle with (or simply ignore!!) the concept of environments.

Adam Sroka

Dr Adam Sroka, Head of Machine Learning Engineering at Origami Energy, is an experienced data and AI leader helping organisations unlock value from data.

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