Python is actually a snake! Let's agree on that before diving into our main topic of today. 😉😉

During our technical Bootcamp for tech recruiters, we play a game called "Categorize them all". We give attendees 50 cards representing technologies used in software development. On the wall, we display a list of 10 categories (Frontend, Programming languages or Data Science). Attendees have to associate each technology with the right category.

90% of the attendees drag the Python card under Data Science.

Python is a programming language.

The attendees aren't completely wrong when placing Python under Data Science.

Indeed, Python is one of the most used languages in the Data Science world. So is the R programming language.

Here is why having Python under the Data Science category is misleading.

There are a lot of developers that use Python to build web applications with Django and other frameworks. Some use Python to automate repetitive work. Python is used practically everywhere even in Data Science.

Therefore, these developers have Python on their LinkedIn or resume and they get requests for jobs in Data Science just by having Python on their profile.

You can imagine how this doesn't help improve the relationship between the two parties.

Python is the most popular language used in Data Science. The story!

Travis Oliphant, a data scientist, is the creator of the famous library NumPy. He started writing code in Python while doing his PhD. Through his work, NumPy (Numeric Python) became popular in the Data Science community. It became a must for all Data Science students to learn Python in order to leverage the work done by Travis.

Travis could have written that library in Perl or Java if he had the knowledge at that time. The story would have been completely different today.

NumPy made Python popular in the Data Science community.

Takeaways and tips

Python, just like the snake It is, can enter any industry. It takes someone with knowledge of the industry and some Python skills to make it a reality. You can apply this logic to any programming language.

Here are a few tips you can use on daily basis:

Tip #1: A programming language can be used to build any type of application.

Tip #2: Don't hire for Python roles. That's not specific enough. Seek the underlying need then search for developers. For example, "I'm looking for Data Scientist with Python skills" is better than "I'm searching for a Python developer"

Tip #3: Don't single out a language to a certain industry. Languages are fluid.

This was "Python is not Data Science".

If you found this useful, please, check out our Bootcamp and learn how to understand the technologies around you.

Until next time!