Python is occupying a larger and larger footprint in the world of data science. It is increasingly the language chosen for all parts of the data analysis process—from prototyping to visualisation to execution.

Whether you’re a student who has vague inclinations towards a job in data science or a working adult who’s looking for a career switch, as long as you are aspiring to go into the field of data science, you should learn Python.

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So why Python?

The Python (programming) logo
The Python logo. Image: Python.org.

I. It is easy for non-programmers to learn.

We’re going to go out on a limb here and say that if you’re already proficient at another coding language, you’re probably not going to be reading an article on why you should learn Python for data science—because you already know the answer to that question.

So we’re going to direct our answer at those who aren’t yet proficient: Python is easy to learn. If you consider yourself more of a statistician or an analyst than a programmer, Python is the language you want to consider.

With its clear, readable syntax, any English language user should have little trouble picking up the basic concepts. Below is an example of how you would print a “Hello, World!” message using Python 3.

Screen Shot 2018-09-26 at 3.10.50 PM

Simple enough right?

While Python isn’t the only programming language that is useful for data scientists (R and Java are some others), it’s definitely the one with the most gradual learning curve.

It also helps that Python is one of the most popular programming languages today, meaning that there are plenty of learning resources available (both online and offline) as well as a large community of developers and fellow students who will support you in your data science journey.

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II. It has multiple tools that help with data analysis.

If your purpose is to get into data science, the coding language you learn should directly assist you to this end. So the ease of learning Python is perhaps less important to you than how useful it is.

If that’s the case, we have some good news for you: not only is Python easy to learn, it also has a variety of tools that help with data analysis.

The language has a number of data science libraries available, such as NumPyPandas and Statsmodels, which provide data scientists with tools to create statistical models and to conduct a variety of data analysis operations with ease.

Pandas logo
Use pandas for data analysis. Image: pandas.pydata.org

III. It also has tools that help with data visualisation.

Other than analysing data, data scientists also help the companies they work for understand the significance of said data. This is often done through data visualisation, where the data is contextualised and visually (rather than just numerically) represented.

Python has a number of libraries, like Matplotlib and Pygal, available that make data visualisation a much simpler process.

Screenshot of the Matplotlib website
Use Matplotlib for data visualisation. Photo: via matplotlib.org.

If you’re familiar with visualisation software like Tableau that is often used by large organisations to present big data, extensions like TabPy allow you to use Python to display and visualise your data on Tableau.

Screenshot of Tableau homepage
Integrate Tableau using TabPy. Photo: tableau.com.

IV. It is a multi-purpose language that can be applied to other industries too.

If you’ve done your research on the programming languages a data scientist might want to learn, you’ve probably realised that another popular language used by those who deal with large quantities of data is R.

Logo for R programming language
R logo. Image: r-project.org.

The language has been created and modified over the years by statisticians and academics for statisticians and academics. This means that if you can think of an statistical analysis to perform, R is likely to have a library for it. In addition, R also provides better tools for data visualisation.

So why are we encouraging you to learn Python instead of R?

R is typically used only when dealing with data, unlike Python, which is used in the machine learning, artificial intelligence and the gaming industries among others. Even marketers can find use for Python knowledge, especially in dealing with data from their marketing campaigns.

This means that if your dreams of working in big data don’t work out, your Python knowledge can definitely still be put to good use in other fields.

Is Python the only programming language you should learn if you want to become a data scientist? Probably not. But we can safely say that it’s the one you should start with if you don’t know any other.


Interested in picking up Python as an aspiring data scientist? Take a look at our Python Development course (for complete beginners) here.

Already have some experience in Python and want to start applying your knowledge to data science? View our Intro to Data Science course now.