7 Reasons to Learn Python

In this article I will share a few exciting reasons why I recommend people to learn Python. It is a resourceful tool that can be relevant for many careers, but the focus today will be for Data Scientists, Data Engineers and Data Analysts.  There are tons of solutions available, but if someone interested to start on those careers asked me today where they should start, my answer would be to learn Python. 

Let’s check what I believe are compelling reasons to start and keep learning Python.

1.     Open source

Python is an open source object-oriented programming language that can be applied to countless number of different problems and requirements. It is free and very well supported by its large community.

The Python Software Foundation or PSF is a non-profit organization that holds the copyright for Python versions starting on 2.1. The organization foments and promotes the advance of open source technology related to Python. You can learn more about its creation, previous versions and licensing here.

2.     Libraries

Python comes with a very good standard library, but also counts with thousands of amazing third-party libraries that most of the time will reduce the volume of code needed to write a solution. There are many companies and people using Python, so frequently new resources are available to help the user solve complex tasks without large code challenges.

Focusing on the Big Data and Data Science universe, some of the most common libraries you will be dealing are NumPy, Pandas, Matplotlib, SciPy, Seaborn, Tensorflow, Scikit-learnk, Pydoop, Dask and PySpark.

3.     Friendly and easy to learn

Python is very simple and easy to learn. It is intuitive and looks like a lot with spoken English. It does not require complex syntax, making the code clean and easy to understand. It is an interpreted language, not demanding previously compilation to be executed, helping develop solutions quickly. Python codes are dependent of proper indentation, but after getting used to it, you will see how it helps the code to be neat and more structured.

My tip #1: If you are brand new with Python, I recommend using the “4 spaces” instead of tab only or tab combined with spaces when putting your indentation in place; some editors are tricky and could turn your debug process into a small nightmare.

4.     Massive community

In the last few years Python became a very popular programming language. According to the Stack Overflow Developer Survey 2019, and also 2018, Python has been one of the major fastest-growing programming languages for two years in a row. In the category of most popular technologies of programming, scripting and markup languages it occupies the 4th place and it stands as the 2nd most loved language.

With all that said, you could only imagine how large is the Python support community. It is a massive, engaged and passionate community with people from all around the world, so it is very likely that you will also find the support required in your native language.

Python has another great advantage, which is to be well used across many industries, like academic research, cloud configuration, automation, wed development, gaming, data analysis, artificial intelligence, machine learning, graphic computing, image processing, i.e. countless possibilities that you will find someone to exchange experience and learn together.

My tip #2: If you are willing to learn, a good way to avoid any excuse to start learning is to download SoloLearn or Datacamp from your app store, available for iOS or Android, and just get into it wherever you are. These apps combined with many other resources, like courses and coding practices will offer good support.

5.     Portable, powerful and light weighted

You will find Python available for a large variety of operational systems, from Windows, Linux/Unix, MacOS, AIX, iOS, to z/OS, Solaris, and so on. And also, in a wide range of alternative Python implementations like Python running on .NET or JVM, or a portable scientific distribution for Windows, a possibility to run Python using a browser not requiring any installation, among others.

The extensible characteristics of Python will allow you to perform integration and cross-language operations flawlessly, using SQL, Java, .NET, C and C++ for example.

6.     Scripting and automation 

One of the biggest challenges companies have been facing is to increase the usage of process automation and automated decisions. Python come in handy for these tasks, because it is also well used as a scripting language. 

Scripting languages enable to code instructions for a run time environment; provide both batch and interactive use; usually don’t require a compilation step; are frequently interpreted; are designed to communicate and integrate with other programming languages; and, are well known to be easy to write code.

Reading the above main characteristics of scripting languages, it is easy to identify common characteristics of one of the largest purposes of Python. As you can see, Python can be used to build simple scripts but also very complex applications. Most important, we can make it easily, organized, using less lines of code than other languages, and faster by not needing to struggle with compilers and complex syntax.

Coming from an IT infrastructure background, I initiated my journey with Python as a scripting language and in the last 2 years I moved my focus to use it as Data Science tool.

7.     Employability and salary 

And if you are not yet convinced about learning Python, do a quick research to find job posts where Python is listed as required or desired skill and you see that the job market offers a wide range of careers paths and good paid figures.

The possible careers go from System Administrators, DevOps, Front-end and Back-end Developers, and Games to Data Analysts, Data Engineers and Data Scientists, to list some of the opportunities. For each career, the salary will depend mainly on experience, location, education and industry. Doing a quick research on the glassdor.ca website for Data Scientists and Data Engineer positions in Toronto area the average figures are ranging from 55K to 110K yearly.

According to the Stack Overflow, based on their last developer survey, the higher salaries per country for developers are for the DevOps positions followed by Data Engineers and Data Scientists, as you can see in the below image from the analysis of the survey results.

The Stack Overflow has another nice tool to enable you to have a broad view of salaries for many careers around the word, the Stack Overflow Salary Calculator. The calculator is updated yearly with data from the most recent survey and reflects trends and estimates for potential earnings. You can check all the details about the calculator here.

My journey has been fun and enjoyable. How about yours? Are you ready to start?