Why Learn Python for Data Science?

Before starting to understand the concepts of python for data science, it’s necessary to know why to use only python and not another language for data science and machine learning. Nowadays, learning python has become one of the crucial skills for building your career in data science.

Here are some facts that would prove why python is best for data science.

  • Python overtook R (a data science language) in 2016 on Kaggle, the best platform for data science competitions
  • It also overtook R on KDNugeets’s annual poll of data scientist’s most used tools
  • In 2018, python was marked as the number one language for analytics professionals

This trend is expected to continue considering the increase in development in the python ecosystem. The language is fun to learn, and there are abundant employment opportunities too. The best part is that demand for data scientists is increasing manifolds day by day. In 2020, the job openings for data scientists were three times that in 2019.

There is a bright future for those who choose to pursue a career in python for data science. Luckily, learning python and other languages is almost as easy as a cakewalk. Read the five steps given below, and you will get to know how. Although the steps are simple, you have to put ample time and effort to learn. If you dedicate enough time to learn python, your skills would rapidly improve and boost your career.

Step 1: Python Fundamentals

Learning python basics is the first step before getting into deep. An introduction to python for data science courses is also a must. The best IDE to start your journey with is Jupyter Notebook. It comes with some prepackaged libraries of python, which will help you in the future.

You should also join a python or data science community. By doing this, you will get to know about each update, and you will be surrounded by like-minded people who will increase your employment opportunities.

Try to learn the Command Line Interface. It will help you test your programs at a good pace and work with more data.

Step 2: Python Mini Projects

Project-based learning has always been interactive and efficient learning. Try making python mini-projects. They will help you understand the concept better and know its practical applications.

Some of the basic data science projects are given below.

  • Fake News Detection – In this project, a Python model would be created to accurately detect whether a piece of information is fake or real.
  • Road Line Detection – You will build an application that can track the road lines from input images or video frames.
  • Color Detection – In this project, you will build an app to detect the color from any selected image.

Step 3: Python Data Science Libraries

Python has this great feature that distinguishes it from any other language. Python libraries are pre-written lines of codes that eliminate the need to write them from scratch. These libraries play a crucial role in data science. TensorFlow, PyTorch, Theano, OpenCV are some of the famous Python libraries.

Pandas is the most famous library, which is specifically created to ease working with data. It is mostly used in all python projects. Several python data science courses available on the internet will help you understand all these libraries.

Step 4: Building Data Science Portfolio

To showcase your skills and knowledge, building a portfolio is a must. It should all of your projects that you have completed till now. Data Cleaning project, Data Visualization project, Machine Learning project are some of the projects you can mention in your portfolio.

Your code should be presentable and easy to read. This will help the technicians understand your code, and even non-technicians will follow along. Adding a theme to your portfolio will give you extra benefits, but it is not necessary. If you have worked for a company, mention all the projects you have done for the company. This will boost up your portfolio.

Step 5: Advanced Data Science Techniques

You have to come out of your comfort zone and start working on the advanced techniques. There are many advanced courses available on the internet, but you have completed all of your basics before that. Try to make difficult and advance projects after completing the course.


There is an increasing demand for data scientists in the upcoming days, and it is the best time to nurture your skills. These steps are simple, and if you put in your whole effort, you will be improving your skills and chances of being selected by reputable companies.

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