There is no shortcut: Data scientists need hard training πŸ‹οΈ

In recent years, data science has become one of the most sought after career paths, with online courses and blogs (like this one) popping up left and right.

While free online courses and programming guides are a fantastic start, they cannot replace rigorous training over multiple years. Following a programming cookbook is not the same as working out the linear algebra yourself.

You do not need to be a mathematician to become a data scientist. However, you do need to learn from mathematicians, as math is a necessary training for every data scientist.

I tend to think about a data scientist like an athlete. While athletes have very specialized skills which make them exceptional, they all need to have a baseline of conditioning. Even though an olympic gymnast never needs to squat a barbell in competiton, you will still find them in the weightroom every day.

Linear alrebra, calculus, or econometrics. These hard skills are the difference between an average and an exceptional data scientist. As they are hard to acquire, they are rare. This hard training is an opportunity to set yourself apart.

You don’t become a doctor by reading a medical book. You become a doctor by a decade of training. And that is a good thing. You wouldn’t want an amateur to perform surgery on you.

The same holds for data science: It takes years of training, to become excellent. There is no shortcut.


What training do you seek out? What do you want to learn next? Let me know in the comments below!