What is Python?

Python is the most famous programming language in the 21st century. Here are some of the reasons this language has reached this level of popularity:

Python’s readability and syntax is, or the characters and words used while programming language is easier to read because it is like English language.

Although Python can support multiple paradigms, however, most people describe Python as an Object-Oriented language.

It is open source and free to install and use.

Python has hundreds of libraries and frameworks which is great additions to development projects. It saves a lot of time to use those specific libraries than manually develop operations.

Libraries and Frameworks

As this is a Data Science blog, listed below are the most common libraries and frameworks being used to develop data science projects:


It is great to data analysis and data cleaning process.


NumPy is the most famous library for numerical computing. It provides high level math functions with data manipulations.


This library is specific to scientific and technical computing. SciPy can also be used for data modification, algebra, etc.

Scikit – learn

Scikit-learn is a free machine learning related library for the Python programming language. It features numerous algorithms like regression, clustering and recommendation.


Matplotlib is a comprehensive library for creating numerous data visualizations in Python.


Seaborn is another Python library for creating data visualization. It provides a high-level interface for drawing attractive and informative statistical graphics.

Other Fields where Python is used immensely

As Python is a very flexible programming language, it is also being used extensively in the following fields. Some of the fields on top of my head where it is being used are below:

  • Game development
  • Web Development
  • Image processing and computer vision
  • NLP (Natural Language Processing)
  • Medicine and Pharmacology
  • Astrophysics and Astronomy
  • Particle Physics
  • Neuroscience
  • GUI (Graphical User Interface) development

As you can easily observe that learning Python and reaching a specific level of proficiency will open doors to programming for different fields as well.

Since Python is an open-source programming language, fortunately there is no lack of good resources to learn it on the internet. I have collected some resources which has helped me in getting a grasp on it.


Sololearn teaches you Python language in an interactive way. This learning experience is divided into bite-sized modules, each with quizzes at its end to test your learning. These modules are divided into topics that helps you in any every other way. Sololearn can also be downloaded via Google’s Play Store and Apple’s Appstore to help you learn on the go.


In addition to Kaggle being home to a learn Data Science community, it also offers different python learning certification courses as well. Though the content may not be as deep as expected, but Kaggle offer a nice practice for Kaggle’s interface and kernel, which will be useful as you will be needing some experience as you will be spending some time on Kaggle’s interface as a Data Science enthusiast.


Whenever you want to learn programming, always keep one thing in mind: getting skills in a particular programming language is like getting proficient in mathematics. What I mean by that is that you cannot learn any programming language by just going through the theory behind it, you must practice it religiously by solving problems.

HackRank is not about learning to code, but it exists to help one to practice coding by solving numerous problems by coding. One can gain more skills in writing programs in Python, from HackerRank than any other course or study material found online. 

Even after all this, if you cannot make out what the problem demands, you can check out the Discussions tab, where a lot of the community engage and converse to help others with any type of problems they are having.


Whenever you start your Data Science journey, it can be a over whelming process at first because there is a lot of information available on the internet and it makes it hard for one to decide from where to start it. My advice for you would be to select a program or a part where you think you can be a good at it and expands from there.

Learning a programming language demands that the person should be consistent in learning it daily. Some concepts might be hard for you, specially for the non-technical background students, but the moment you understand and apply the techniques by solving a problem, it will be a piece of cake for you.

So, the above-mentioned websites and courses are the ones I feel helped me a lot while I was starting, and I hope they do the same to you. Cheers!!


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