Why Specialising in Data Science is Essential (and How to Choose the Right Path)
At some point in your data science career, you’ll be hit with the question: “What do you want to specialise in?” It’s intimidating, and finding the best path for yourself can feel like solving a riddle. That’s why in this article, I’m going to break down why you should specialise, which specialisations might suit you, and how to get started.
Why Should You Specialise? In my view, specialising is essential, but you shouldn’t rush into it. Spend your first couple of years — let’s say two to three — getting a solid grasp on data science fundamentals. Here’s what you should cover:
- Basic statistics
- Linear algebra
- Calculus
- Linear and logistic regression
- Random forests
- Gradient boosted trees
- Neural networks
- Support vector machines
- Best practices for modelling: cross-validation, hyperparameter tuning, etc.
- High-quality Python and SQL
This isn’t an exhaustive list, but it’s a good starting point. Expect to spend a couple of years truly mastering these concepts. It might sound like a long time, but consider this: if you begin your data…
