Tutorials, case studies & career guides for the inquisitive Data Scientist & Machine Learning Engineer.

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Bridge Libraries and How They Can Aid Your Data Projects

In need to port code from a different programming language to the one with which you are more comfortable? This nifty set of libraries may be able to help you with the task at hand.

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Mentoring Best Practices

Some ideas to help cultivate and refine a mentoring relationship in practice

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Data Science vs Computer Science degree? What you should know before choosing a study program

Choosing a degree is one of the most important decisions you will make in your career or even in your entire life. Are you considering a Computer Science or Data Science study and don't know which one to choose? This career guide will definitely help you to make up your mind. This is part 1/3 of the Ultimate Career Guide for Data Scientists & Developers.

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Data labelling: The Dirty Job of Machine Learning

What it is, how to do it, and the best tools out there

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Pro Python Tips for Data Analysts

Complex analysis requires complex code. How do you keep this tidy, ready to evolve and improve? Learn to create sleek code, which clearly expresses the steps between the problem and the solution with these top tips for data analysts.

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Every Data Scientist Should Know This Design Principle

Let's talk about the one design principle that every Data Scientist should be aware of.

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Human-Centric AI Manifesto: Or How To Try and DO No Harm 

Recently I’ve said farewell to my academic career and switched back to industry. While I haven’t regretted that move for one moment, I started having some concerns on if I will be able to keep on reflecting on the ethical aspects of my work now that my work has become more pragmatic and my autonomy has decreased.  In general I think we as a data science community are not doing enough to guarantee our work does not harm or disadvantage those that interact with whatever we create. In this piece I tried to consolidate my observations and ideas on how to do better than we are currently doing, by adopting a more human-centric and value-sensitive approach.

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How to write amazing code as a Data Scientist

And why you should learn how to do it

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"Must-Have Skills" is Silly

Let's talk about marketing techniques without skill anxiety.

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Machine learning explainability: a hands-on introduction

Machine learning/AI explainability (also called XAI in short) is becoming increasingly popular. As algorithms become more and more prevalent in high-stakes decisions in industries such as finance, healthcare and insurance, the demand for explainability will only grow.

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How I would redo my Data Science journey if I had to start from Scratch

The things I would learn, projects I would do, and skills I would prioritise if I had to learn from scratch

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How I Hire for Data Science?

As a former CTO in a data science business, I have been asked this question many times. This post outlines the exact method and procedure that I have used. I can not promise you that preparing for this will get you hired anywhere, but this method and procedure did create one of the stronger data science teams that exists. I hope it helps…

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Complexity and Big O Notation: The Ultimate Guide

"In almost every computation, a variety of arrangements for the process is possible. It is essential to choose that arrangement which shall tend to minimise the time necessary for the calculation".

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Data Science Career Summit  — Day 2

On November 18-19th AIgents and the European Leadership University organized the first Data Science Career Summit. The title of this first edition was ‘Building a Data Science Career’. In two days, 14 different talks were presented by Data Science consultants, team leads, entrepreneurs, recruiters and headhunters.

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The Whys of Transparent Artificial Intelligence (TAI)

"He who has a why to live for can bear almost any how." ― Friedrich Nietzsche

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