Hello Devs, If you want to become a Data Scientist and are curious about which programming language should you learn then you have come to the right place. In the past, I have shared the best data science courses and the best Python courses, and today, I will tell why learning Python is the best choice for Data Science.

When it comes to learning Data Science and Machine learning, you have mainly had two programming languages to choose from, either use Python or R, but you will find that most Data Scientists and Machine Learning specialists use Python.

I was thinking about it for quite some time; why do Data scientists love Python so much? And what makes Python an absolute choice for Data Science and Machine learning exploration.

I set out to research this and read many articles, books, and joined Data Science courses with both Python and R to figure out myself and what found was nothing more than surprising.

I mean, it was the simple reason which makes Python than any mystery advantage over R or any other mainstream programming languages like Java, C++, Ruby, or JavaScript.

Python is loved by everyone from beginner to experienced programmers for its simplicity and powerful set of libraries and tools which makes working with data really easy.

For example, you can easily cleanse raw data acquired from a survey to create your Machine learning Model using the Pandas library. If you try to do the same thing in other programming languages like Java, you will have to write tons of code and it’s not as easy as it is in Python.

This combination of simplicity, easier learning curve, powerful toolset, and a library make Python the best programming language for Data Science and Machine learning.

By the way, if you already made up your mind to learn Data Science with Python and looking for resources where you can learn Data Science with Python programming language, tools, and practices then IBM Data Science Professional Certificate on Coursera is a great program to start with. This program is specially designed for people who want to become Data scientists by learning Python programming language and tools.

Now, let’s take a look at all of these reasons in detail before you choose Python to start your Machine Learning and Data Science journey with Python.

5 Reasons Why Python is Best Programming Langauge for Data Science

Anyway, here are the top 5 reasons why Python is so popular among Data Scientists and Machine Learning enthusiasts and why you should learn Python if you want to become a Data Scientist.

  1. Python’s readability and simplicity

One of the main advantages of Python is that it’s intuitive and straightforward, and that’s what makes it likable for anyone who wants to get a result rather than lost in code.

Python is also very readable and easy to learn, which means a shallow entry barrier as compared to other programming languages like R, Java, or C++, which requires a proper environment to be set up to do anything other than running a trivial HelloWorld program.

And, If you are already convinced that Python is the best programming language for Data Science and looking for an online course that teaches you Python from a Data Science point of view then I highly recommend you to join Kirill Erenemko and SuperDataScience Team’s Python A-Z: Python For Data Science With Real Exercises! course on Udemy. This hands-on course is the best course to learn Python for Data Science.

  1. Tools and Libraries

One of the main primary responsibilities of Data scientists is to analyze the Data, and in the real-world Data comes in all shapes. They are often raw and not suitable to run any kind of analytics; hence Data wrangling is applied to that.

It’s a process to clean and transform the data so that you can analyze and model it to create insights.

Python helps Data Scientists here; it comes with so many open-source Python libraries that can do all these tasks for them. These are the libraries that are regularly get updated, and all you need to do is use them in your Python scripts.

You don’t need to learn how NumPy works or how Pandas works, as long as you can get your Data clean, apply some mathematical formulas, run some statistical equations you are happy with.

Isn’t that a result-oriented person will like it? Well, I certainly do. All you need to learn is how to import a Python module, and you are done.

If you are curious about which Python module to use for which job, then just Google it, you will find your answers. You don’t need to remember which Python libraries I should use.

In reality, after working with few scripts, you will automatically get familiar with essential Python libraries for Data Scientists like NumPy, which stands for Numerical Python, Pandas, which is the most critical tool for Data cleanup and Analysis, and MatPlotLib for visualizing data, creating charts and generating insights.

You also have TensorFlow, Sci-kit, PyTorch, which provide some Scientific and Machine learning capability and are continuously being enhanced and updated by talented people around the world.

For example, Facebook has recently added a lot of machine learning capabilities on PyTorch.

As a Data Scientist and Machine learning enthusiast, you don’t need to worry about updating libraries, adding new functionalities, etc., as someone else is doing that job for you. You just need to use the library to do your job.

  1. Jupyter Notebook

Another reason why Data scientists love Python in Jupyter Notebook, which allows you to code and collaborate with other Data Scientists using a web browser.

Jupyter Notebook was born from IPython, an interactive command-line terminal for Python.

Since working on the command line is not easy for everyone, they created a powerful web interface to Python and named it Jupyter Notebook.

The Jupyter Notebook is an incredibly powerful tool for developing and presenting Data Science projects. IT allows you to integrate code and its output into a single document, combining Visualization, mathematical formulas, and explanations.

In fact, most of the online courses I have taken about Machine learning on Google Cloud on Coursera uses Jupyter Notebook for a hands-on example. Because of its impressive capabilities, Jupyter Notebook is very popular among Data Scientists, and it’s one of the must-have tools for them.

And if all these good things are not enough, you would be surprised to know that Jupiter Notebook can also handle R code, which means you can also collaborate with a fellow Data Scientist who is using the R programming language.

  1. Community Support

Another reason which I found behind the popularity of Python among people learning Data Science in the community.

Since Python has an active community, and many people are doing Data Science using Python, you already have an active community to call upon when you get stuck.

You also benefit from their work as most of the things are shared as open source.

Many big organizations like Google and Facebook have contributed to TensorFlow and PyTorch, some of the most popular Python libraries for Data Science and Machine Learning.

  1. Pandas

This is an extension of the second point, but Pandas is such an essential tool for Data Scientists that It warrants a special mention. Most of the Data Science project I have worked upon starts with Pandas and finishes with it.

It not only allows you to clean and massage your Data but also to analyze the data. You can load data from various data sources like CSV files, Excel, Databases, and many other sources.

Pandas contain a large variety of functions for data import, export, indexing, and data manipulation.

It also provides a handy data structure like DataFrames (a series of rows and columns) and Series (1-dimensional array)and efficient methods for handling them.

For example, you can use Pandas to reshape, merge, split, and aggregate data. In short, Pandas is an indispensable tool for Data Scientists along with the Jupyter Notebook.

If you want to learn Pandas better, I also recommend you to check out the Data Analysis with Python and Pandas course on Udemy.

Coming back to the topic, because of all these excellent tools, frameworks, libraries, and simplicity of the Python programming language, Data Scientists love Python and continue to love it.


In short, here are 5 main reasons why Python is the most popular and best programming language for Data Science and Machine Learning

  1. Python is Simple and Intuitive.
  2. Jupyter Notebook allows Data scientists to collaborate and combine cod and output.
  3. Python packages and libraries like NumPy and Pandas help with data cleanup and Analysis.
  4. Community support
  5. Pandas

If you still have doubts, here is a chart from IBM’s survey about the most popular programming language for Machine learning from the last couple of years.

It’s a bit old, but it shows a clear trend that Python is way ahead with mainstream programming language like Java, C++, JavaScript when it comes to Data Science and Machine learning

That’s all about why Python is the most popular programming language for Data Science and Machine learning. I am also from the same camp. I did try R but not more than a couple of days. Why? Because I wanted to spend my time on something which I can use in places other than Data Science, and on that parameter, Python is well ahead with R.

If you also think that Python is the best Programming language for Data Science, here are some courses you may want to checkout to learn Python from the Data Scientist point of view.

Original Source