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There are many things you can learn as a data scientist. I bet the first things you learned were about statistics, data analysis, visualization, programming, databases, machine learning and deep learning. When you are familiar with these topics, you continue with more specific ones, like GANs, NLP, the cloud, containerization…

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When working on an ordinary classification problem, one of the most important tasks is feature engineering: creating new features from the data. This guide shows different ways to create those new features from existing columns or dictionaries, so you don’t have to check Stack Overflow ever again for column creation!

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Ever since I watched the Netflix documentary AlphaGo, I have been fascinated by Reinforcement Learning. Reinforcement Learning is comparable with learning in real life: you see something, you do something, and your act has positive or negative consequences. You learn from the consequences and adjust your actions accordingly. Reinforcement Learning…

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Finally, the moment is here, you are moving to San Francisco to become a well-known data scientist! You will work for a big company and be famous soon! But, first things first. You will live in a small apartment, way smaller than your current house in the valley. So you…

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More and more companies are using complex machine learning models, like neural networks and gradient boosting machines. The reason they use complex models is because they outperform traditional models, like decision trees or logistic regression. The negative side effect of using complex models is that you can’t interpret those models…

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A machine learning model from Amazon selected only males from a pile of resumes¹. Another model fired teachers who were underperforming, according to the model². Such models are discriminatory and can be bad for society. They can make wrong decisions and affect people’s lives in a negative way. To solve…

Hennie de Harder

Data Scientist

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