Of course, it would be nice to study all four. But if you`re just starting out, it`s worth starting with one or two. The most commonly recommended options are Python and SQL. With their help, it will be possible to cover 99% of the process of working with data and analytical problems.

Contents

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When it comes time to learn code for working with data, you should focus on these 4 languages: SQL, Python, R, Bash.

The Python Data Science Handbook is a comprehensive guide to a wide variety of computational and statistical methods, without which any data intensive data processing, scientific research and advanced development is unthinkable. Readers who already have programming experience and want to effectively use Python in the field of Data Science will find answers to all kinds of questions in this book, for example:

  • How do I read this data format into my script?
  • How to transform, cleanse and manipulate this data?

If you are studying Data Science, you will quickly come across Python. Why? Because it is one of the most used programming languages ​​for working with data. It is popular for many reasons.
This guide will walk you through everything there is to know, starting with the basics. Even if you've never worked with code, you are definitely in the right place. Only that part of the language that is related to data science will be affected - we will skip unnecessary and impractical nuances. And at the end, we will definitely create something interesting, for example, predictive analytics.

This is a practical guide!

Doing something by following the instructions is always more rewarding than just reading. If you repeat the parts that contain the code yourself, typing it on your PC, you will understand and remember everything 10 times better. And at the end of the article there will definitely be one or two exercises to test!

Why Learn Python for Data Science?

The theoretical part will be short. But you need to know two things about the language before you start using it.

Python is a general-purpose language and is used for more than just Data Science. This means you don't need to know him perfectly to be a data expert. At the same time, even the basics will be enough to understand other languages, which is very convenient for working in IT.
Python is a high-level language. This means that it is not the most efficient in terms of CPU time. On the other hand, it was designed to be very simple, "user-friendly" and understandable. So, even if you lose in CPU time, you can win back in the development process.

Conclusions

Did you realize that you were programming in Python 3? Wasn't it so easy and fun?

The good news is, otherwise, Python is just as simple. Complexity comes from combining simple things ... This is why it is so important to know the basics! In the next part, Python for Data Science, you will learn about the most important data structures in Python.