Why Python?

The name of this section already suggests that we have chosen Python for this data science course. In this reading, we will show you some of our reasons.

Why Python for data science?

The truth is we can do data science in almost any language. To name a few:

  • Python
  • R
  • SAS
  • Scala
  • Julia
  • Closure
  • But even some more traditional languages like Java or C#
  • ...

In the picture below, we can see the popularity Python has gained in the last couple of years.

 Source: Google Trends

Let's take a look at why it is so.

Instruction

Read the article Is Python the most popular language for data science? to understand the importance of Python among data scientists.

There are a couple more reasons why we think Python has going for it:

1) Python is a friendly language for beginners (and data scientists)

Python is unusually human-readable. Even other "beginner-friendly" languages (like Javascript) expect you to wrap your brain around unusual patterns and complicated syntax, like in this snippet of Javascript:

for (let i=0; i<contacts.length; i++) {
  sendEmailTo(contact[i]);
}

The same thing can be communicated in Python with a much more readable syntax:

for contact in contacts:
    sendEmailTo(contact)

That, combined with descriptive error messages (that's a good thing!), great documentation, and a supportive online community, make Python highly accessible for beginners.

2) Python is a friendly language for experts, too

The fact that Python is great for beginners does not mean that it's a "beginner language", to be left behind for something more powerful once you get the hang of it. You very well may move on to other languages (and it's a good idea to try new ones), but if you like Python, it is powerful and expressive enough to be useful for your entire data science career. Many of the virtues that make a language friendly for beginners also benefit professional programmers, too.

3) Python is extremely versatile

Python has a terrific ecosystem of libraries, frameworks, and tools that lower the barrier of entry to do really powerful things. Here are just a few examples:

  • Understand, analyze, and express enormous sets of data with data science libraries like Pandas
  • Build a machine learning model with Sklearn
  • Create an AI superintelligence that can seamlessly blend pop culture references into iconic works of art with Google's machine learning framework, TensorFlow
  • Build a sophisticated web app with Django, or something simpler with Flask
  • Build a game with Pygame
  • Program your own Internet of Things devices with a Raspberry Pi

Of course, most people won't become proficient at all of these things... But by choosing Python, you are making it easy to dabble in all of these things, and eventually, to mix them together in interesting ways.

4) Python is popular

Partly for the reasons already described, Python is astoundingly popular. Depending on who you ask, it may be the most popular of all programming languages. It has broad appeal, great versatility, and there may or may not be hundreds of job openings in your area for folks proficient with it.

Of course, "popular" doesn't always mean "good," or "the right choice for me", but there are some benefits to working with something popular. More people learning your language means that there are more resources to help you learn, more of other peoples' code for you to use, more beginners asking questions and getting answers for your perusal on forums like StackOverflow, and more people to answer questions for you when you get stuck. If you take it far enough to go pro, it also means more job opportunities :)

Conclusion

Now you see why we chose Python for this course, even though there are many other options. It is true that, nowadays, Python is a must for data science and it will bring you the best opportunities in your career. Let's see how long it will take you to fall for it, too.