Python is the primary programming language for data scientists. Although it wasn't the first major programming language, its use has increased with time.

What is Data Science?


The tools used to undertake data exploration, machine learning, visualisation, statistical analysis, NLP approaches, or deep learning are constantly evolving due to the size of the area of data science. There are numerous tools available now to meet all needs.


Data science is the practise of taking an iterative approach to glean insights from unstructured data and transforming them into useful information. Data scientists are interested in improving the effectiveness of this process, thus they must be familiar with all the necessary technologies.


Why Python for Data Science?


Data scientists frequently use the computer language Python. It enables you to quickly prototype statistical models and tools for quantitative analysis.


One of the factors contributing to Python's current status as one of the most well-known languages is the substantial user base it has in the data science field. You may execute data analysis and machine learning on large datasets with the aid of several libraries. Python is a great environment for data science because of the variety of its libraries, which allows you to choose the best tool for the job.


Five steps to learning Python for Data Science

Step 1: Learn Python fundamentals


Everyone has a beginning.Data science is something else you should familiarise yourself with if you haven't before.


This can be accomplished through online courses (like the ones that Dataquest provides), data science bootcamps, independent study, or academic courses. The fundamentals of Python can be learned in any order. 


Find a community online

Join an online group for support in maintaining motivation. In most communities, you can learn by asking the group questions or asking questions yourself.


Additionally, you can establish ties with professionals in the field and connect with other community members. Additionally, since 30% of all hires come from employee referrals, this boosts your chances of finding work.


Step 2: Practice with hands-on learning


Hands-on learning is one of the best ways to advance your education.


Work on Python projects for practise


You might be surprised by how quickly you pick things up when you create simple Python programmes. Some of them are as follows:


  • Enjoy some fun while using Python and Jupyter Notebook to analyse a dataset of helicopter jail escapes.


  • Profitable App Profiles for Google Play and the App Store — You'll perform data analysis work for a company that creates mobile apps in this supervised project. Python will be used to add value through useful data analysis.


  • Exploring eBay Car Sales Data - Work with a scraped dataset of used vehicles from the German eBay website's classifieds area, eBay Kleinanzeigen, using Python.

There are a huge amount of other beginner Python project ideas in this page as well:


  • Create a game of rock, paper, scissors.
  • Make a text-based adventure game.
  • Construct a guessing game.
  • Create engaging Mad Libs


Step 3: Learn Python data science libraries


The four most significant Python libraries are Scikit-learn, Pandas, Matplotlib, and NumPy.


  • The NumPy library, on which many pandas library features are built, simplifies a number of mathematical and statistical procedures.
  • Pandas is a Python package designed to make working with data easier. A visualisation library called Matplotlib makes it quick and simple to create charts from your data.
  • The most widely used Python library for machine learning tasks is scikit-learn.
  • For examining and experimenting with data, NumPy and Pandas are fantastic tools. A data visualisation package called Matplotlib creates graphs similar to those in Google Sheets or Excel.

Step 4: Build a data science portfolio as you learn Python.


A portfolio is a requirement for aspirant data scientists because it's one of the key qualities hiring managers look for in a prospect.


These projects should involve working with a variety of datasets, and each one should present intriguing insights you found. Consider the following project categories:


  • Projects using unclean or "unstructured" data that you clean up and analyse can impress potential employers because most real-world data needs to be cleaned before use.


  • Project for Data Visualization — The ability to create appealing, simple-to-read visualisations is a programming and design challenge, but if you succeed, your analysis will be much more beneficial. 


  • Machine Learning Project – If you want to become a data scientist, you must have a project that demonstrates your proficiency in ML. Several machine learning initiatives, each centred on a different algorithm, can be what you need.

Step 5: Apply advanced data science techniques


Finally, develop your abilities. Although learning new things will be a continuous in your data science journey, there are advanced Python courses you can take to make sure you've covered everything.


By learning about bootstrapping models and building neural networks with Scikit-learn, you may also get started with machine learning.