Blog ยป Python ยป 10 Compelling Reasons to Learn Python for Data Science

10 Compelling Reasons to Learn Python for Data Science

By Zulie Rane on Aug 19, 2021

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Read this if you don’t know where to start learning data science

Data science is a vast field with tons of entry points, depending on where and how you want to start. I started learning basic data science with a language called R, until I ran into one of its many limitations. Python has definitely won the battle of R vs Python for data science, as I learned. When I wanted to take the next step in my data science journey, I leaned on Python. Learning Python for data science is one of the fastest, easiest, and most fun ways to get into data science.

Data science is a very valuable skill, with both high average salary and job satisfaction, yet there are still more companies posting job listings for data scientists than current data scientists actually exist.

I dipped my toe in the data science world using Python for a lot of reasons - it’s used by lots of FAANG-adjacent companies to do their data science, it’s a versatile language, it’s easy for beginner coders to pick up and for expert coders to learn.

Even though Python is a general-purpose language, this article gives you 10 solid reasons you should learn Python for data science, and explains what Python is in data science.

  1. Python is easy to learn for data science.
  2. Python is easy to read.
  3. Python is a popular language among data scientists.
  4. There’s a vibrant community of Pythonistas in the Data Science world.
  5. The Python data science libraries are comprehensive.
  6. Learning basic Python teaches you basic data science.
  7. Python makes data cleaning easy.
  8. It’s simple to communicate data science results with Python.
  9. Python helps you build quick data science prototypes.
  10. Python gives you job security beyond data science.

1. Python is easy to learn ๐Ÿ”—

Coding can be intimidating, especially for a beginner. But Python is the exception. It has a remarkably simple syntax and vocabulary so you can pick it up relatively quickly, especially compared to more complex languages like C, C++, and Java. Python, for data scientists, is an obvious choice of a language to learn.

It’s so simple that Next Academy actually recommends it as a great choice for children to learn coding. And for non-coders, there are plenty of cheap or free resources to start learning Python.

If you want to get into data science, Python is a great choice of coding language because you can add it to your tool belt pretty quickly and with a minimum of pain. Learning data science with Python for beginners can be a simple solution.

2. It’s easy to read ๐Ÿ”—

Python has a clean and simple syntax that mirrors English, so whatever you build will be understood by you and many people, even if they’re not Pythonistas themselves.

When I started learning Python, part of the reason it was so easy to learn was that I could read Python code examples and understand what they were trying to do. If you want to get into data science, you should definitely think about readability as a key component of any language you choose.

You’ll be reading lots of code as well as sharing it with your coworkers (or strangers on the internet as you try to debug something on StackOverflow). Python makes that easy to do.

If you learn Python, you’ll be one of many. It’s one of the most widely used languages in data science (and elsewhere). It’s the third-most widely used language in the world according to TIOBE’s 2020 index. And in data science specifically, it’s emerged as the leader, outstripping my old favorite language R.

As I alluded to above, many companies are using Python to build frameworks and projects. Google, for example, created Tensorflow, which is based on Python; Facebook and Netflix are also relying on it more and more in their data science projects.

If you want to get into data science, you won’t get far without knowing at least some Python. Luckily it’s a joy to learn!

4. Huge Community of Pythonistas ๐Ÿ”—

I remember when I learned the name of someone who codes in Python: a Pythonista. I loved it. And one of the prime benefits of learning Python for data science is that you’ll get access to an incredible community of Pythonistas and become one yourself. (There are more benefits than just the cool name.)

Because it’s been around for three decades, because it is easy to learn and easy to build with, because it’s remained relevant to so many people and companies for long, there is a huge and enthusiastic community of Pythonistas out there who are more than happy to share their tips, answer your questions, correct your code, and discuss new ideas. You can find them anywhere - Reddit has a particularly active community, but you even have Discord groups popping up to chat about Python.

The reason this makes learning python such a great choice for data science is because learning any kind of language is hard, especially if you’re feeling professional pressure. Communities like the ones that have sprung up around Python make that easier.

5. Comprehensive set of data science libraries ๐Ÿ”—

Python as a language for data science rocks on its own. But on top of the simple syntax, easy vocab, readability, community, and every other benefit I’ve already listed, there are the libraries. Libraries like Pandas, statsmodels, NumPy, SciPy, and Scikit-Learn are very popular in the data science communities.

Ecosystems like SciPy make data science tasks much easier. (SciPy is pronounced sigh-pie, not skippy as I’d originally assumed.) SciPy addresses lots of common data science needs, like handling data structures, analyzing complex networks, algorithms and toolkits for machine learning. Python libraries for data science are popular and constantly evolving.

The really exciting thing is that new Python packages for data science are being released all the time as more Pythonistas join the community and make their own contributions. Python libraries for data science are popular and constantly evolving. For instance, Keras is a minimalist library used for deep learning that was released in 2015. Since then, it’s become a critical component of the Python library ecosystem.

6. Teaches the basics ๐Ÿ”—

Even though Python has a practically unlimited number of applications, there’s actually a lot of overlap between learning Python and data science. You can easily learn data science basics with Python just by running through some basic tutorials. Data scientists use Python for retrieving, cleaning, visualizing and building models - so if you want to use Python to learn data science, that’s where you can start.

As you go through the standard track of learning how to code in Python, you’ll cross over with some data science basics by default. For example, you’ll start by learning how to set up your environment, importing data, cleaning it, running some statistical analyses on it, creating some nice visualizations, and sharing your findings. And look at that - you’ve done some data science with Python.

As long as you keep typical data science tasks in mind as you look for Python tutorials, you’ll easily be able to find plenty of resources that teach you Python - and simultaneously teach you Python for data science, specifically. Learning basic Python for data science is a natural learning path.

7. Data cleaning is a breeze ๐Ÿ”—

The phrase “data science,” for me, conjures up images of Neo in the Matrix wearing a cool coat and doing cool stuff. A lot of people don’t realize data science is a LOT of much-less-glamorous data cleaning. Conservative estimates place data cleaning at 80% of a data scientist’s typical workload. But good news: Python is great at that!

If you want to get into data science, you need to come to terms with the fact that you’ll be doing a lot of data scrubbing, cleaning, massaging, wrangling, etc before you even make a single cool viz. That need is what makes learning Python for data science a great choice: it’s built to clean.

Two of the libraries I mentioned early, NumPy and Pandas, are really great at cleaning data.

8. Communication ๐Ÿ”—

After you’re done cleaning your data, the next biggest component is communicating your findings. Data science is not just lines of code - it means communicating results of key stakeholders. A good viz is crucial for that.

“Data visualization gives us a clear idea of what the information means by giving it visual context through maps or graphs. This makes the data more natural for the human mind to comprehend and therefore makes it easier to identify trends, patterns, and outliers within large data sets,” writes an unnamed writer in the Analytiks blog.

A lot of people assume data science stops at the analysis, but like everything else in the professional world, what you do after you build that really cool thing is what matters.

Python has a lot of great tools to make easy visualizations, like the very basic matplotlib and its two children Pandas and seaborn (both built on matplotlib). If you can easily make a good viz to communicate or illustrate the data, the battle is half won. Python makes that easy.

9. Quick prototypes ๐Ÿ”—

A little-known fact is that data scientist projects are expensive. In fact, Chris Chapo, the SVP of data and analytics at Gap once said that “87% data projects will fail.” It takes time, energy, resources and a lot of patience to build something that works.

To get around this, most data scientists use prototypes to do a dry run of their idea and stress-test it to make sure it’s worth properly building out. If you’ve been following along with the theme of this article, it shouldn’t surprise you to hear that Python is great for building good prototypes to test out concepts, ideas, and products.

The authors of Fuzzing Book wrote:

“Python made us amazingly productive. Most techniques in this book took 2-3 days to implement. This is about 10-20 times faster than for “classic” languages like C or Java.”

Python makes it easy to run dynamic analysis (analyzing program execution as it runs) and static analysis (analyzing code without running it), both of which make prototyping a dream.

10. Job security ๐Ÿ”—

This article is about why it makes sense to learn python for data science. Butโ€ฆ many occupations and career paths that once seemed as stable as mountains faded away, or were replaced with algorithms.

There’s no sign of this kind of atrophy for data science, but since companies are paying through the nose to find data scientists and struggling to find enough, you can bet they’re motivated to look for alternatives to spending yet more time and resources to scrounge for another data scientist.

If you learn Python for data science, those skills will be more than enough to help you find jobs elsewhere in the computer science realm. Python itself is more stable than any career path - it’s been around and relevant for thirty years, it constantly reinvents itself to be useful for new jobs and careers. The future of data science might be in question, or you might feel your career goals change. Either way, knowing Python will give you a leg up.

Start learning python for data science, but rest secure in the knowledge that no matter what happens to the data science field Python will be a valuable language to know.

If you want to know what to learn in Python for data science, this article should have covered that comprehensively. Learning data science basics with Python is a natural solution for people wondering where to get started with learning data science, which can be an overwhelming prospect! When I started learning Python, I found it was a perfect fit to getting exposure to basic data science concepts and tasks.

If you’re looking to learn Python for an eventual data science role, we developed our Learn Python course to teach you the skills you need.

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