Taking the leap from learning to application can be tough. Here are some things to look out for to smooth the transition.

Don’t spend all of your time in the classroom (photo by Shubham Sharan on Unsplash)

Beware the Bookworm

It shouldn’t surprise you to know that I — like many data scientists — am a bit of a bookworm. I love to read and try to pick up new skills. There’s a special feeling when you hear something that just clicks and makes you think about something in a completely different way.

The most common reasons strong candidates get stuck in an interview are often easy to fix with the right focus

Images of a computer
Images of a computer
Photo by Boitumelo Phetla on Unsplash


The tech world seems to have gone mad for data scientists in the last few years: many people want to get into the role and plenty of organisations are looking to hire them. As someone that’s interviewed and hired scores of talented, capable individuals for the ‘Sexiest Job of the 21st Century,’ I can tell you, it can be a painful and difficult process for both sides. Whether you’re a candidate trying to find the perfect role or an organisation seeking the right fit — there are plenty of pitfalls to watch out for.

The soft skills that most developers struggle with and some guidance on how to improve them

Frustrated man using phone
Frustrated man using phone
Are soft skills dragging you down? (Photo courtesy of Reshot)

It’s Not All About the Tech

As techies, we can get really excited about learning the newest tools and approaches. There’s always some big discussion in the community, like R vs. Python, Power BI vs. Tableau, TensorFlow vs. PyTorch, etc. It’s easy to get drawn in. You’re passionate about tech. Good.

1. Selling


A lack of available data is stifling the adoption of machine learning solutions. Federated Data Sharing might be the answer.

Many organisations are out in the cold when it comes to high-quality data (photo by Harrison Haines from Pexels)

From the earliest themes of artificial intelligence in Greek mythology, people have long thought about AI and the possibilities it may hold. With the advances in computation and mathematics, Alan Turing’s 1950 paper on thinking machines sparked the first real developments of this in practice. The first proof of concept was initialised through Allen Newell, Cliff Shaw, and Herbert Simon’s, Logic Theorist, a program designed to mimic the problem-solving skills of humans — considered by many to be the first artificial intelligence program presented in 1956.

AI is 60 years young, we’re only at the very beginning.

Despite its 60…

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

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.

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

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.

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

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…

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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