AIgents Career Blog

Every Data Scientist Should Know This Design Principle

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

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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Introduction to Convolutional Neural Networks CNNs

Take the first steps in understanding how computer vision works with CNN networks

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Data Science Career Summit: ‘Building a Data Science Career’ — Day 1

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Iteratively Finding a Good Machine Learning Model

There is a theorem telling us there is no single machine learning method that performs best in all problems. So how do we find the best one that fits our needs? This post suggests that before going into complex methods and spending time on fine-tuning your deep learning model, try simple ones. As you gear up towards more complex methods, you may find that a simple one is sufficient for your needs.

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