Here’s how I Learned Just Enough Programming for Data Science

Here’s how I Learned Just Enough Programming for Data Science

How to approach learning programming and best books I recommend. There’s no doubt that data science requires decent programming skills, but how much is enough?

There’s no doubt that data science requires decent programming skills, but how much is enough? Should you know just as much as an average software engineer? This article aims to answer this question, and much more.

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As a one-sentence summary — no, knowing to program on the level of a mid/senior backend developer is not required. Aim to know more than average statistician and you’ll be fine. There’s always time to learn more as you progress in your career.

The article is divided into three sections:

  1. How much programming is needed in data science?
  2. Which programming language to pick?
  3. Resources I recommend to get started

Please keep in mind — in the article, you’ll find affiliate links to the recommended resources to get started. That doesn’t mean anything to you, as the price is identical, but I’ll get a small commission if you decide to make a purchase. Also, I only show materials I’ve gone through myself and can guarantee 100% for the quality.

Without much ado, let’s get started with the first section.

How much programming is needed in data science?

Well, a lot — but that depends on the role and the company you work for. Small companies don’t necessarily have structured teams for both development and data science, so it’s required to be comfortable with both.

In a nutshell, you won’t be the best in programming nor in data science. That’s not necessarily a bad thing, as you’ll get a better grasp of the product/service the company offers.

Larger companies will treat you differently, due to a more formal structure. You’ll handle data science problems only (as a data scientist), and more often than not won’t see the big picture. You’re there to do the job — not to ask too many questions.

Keep in mind that this is just a rule of thumb — drawn from my experience and from many others.

Which programming language to pick?

It’s not an easy question, to be honest. Most websites mention Python and R as a go-to languages, but those aren’t the only options.

Some companies need a data science solution, but don’t have any data scientists onboard — software development companies centered around the web/mobile development.

While Python and R are great, I find more and more resources on solving machine learning tasks with Java, or even with Go(lang). Heck, I’ve even written a whole article on this topic:

Go for Data Science? Let’s try.

Can Google’s Golang handle data science? Let’s find out.

I’m not saying languages like Java and Go are great for prototyping, but they are still a viable option for a software developer that doesn’t know Python or just doesn’t want to use it. As I’m diving deeper into software development, or developing applications that use machine learning, I can get why someone wants to stay away from Python.

To summarize:

  • Learn Python/R if you only care about data science and machine learning
  • If you are a software developer and don’t want to switch languages, you can try Java and Go (among other languages)

Resources I recommend to get started

My guess is that you’ve chosen the Python route, and that’s great for several reasons:

  • The language is simple to learn — more beginner-friendly than Java/Go
  • It’s the most widely used language for data science
  • It’s a general-purpose language — not limited to statistical tasks

As an aspiring data scientist, Python will suit you just fine. There’s no need for you to explore other, more difficult languages as coding shouldn't be your primary concern.

But how to get started? I’ve got two amazing books for you which helped me to learn Python well, both pure programming-wise and for the data analysis tasks. Let’s start with the basics.

towards-data-science software-development data-science python programming data analysis

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