Python is one of the most sought after programming languages in the 21st century. Learning Python can give you an upper edge in your career as a software engineer.
But, as a beginner, we find difficulty in deciding the right learning path and we usually ended-up wasting a good amount of time in deciding the better resource to learn.
It is observed in most of the programmers — when they start learning new stuff they usually ended up stacking different courses in their channel or hard drive, which is not a wise practice to follow.
In this article, I will talk about how you can learn python in the most effective way even as a beginner. I have tried to give a brief overview of the learning path depending on your field of interest and also a few tips and tricks to make the learning process more interesting.
Know about the language first
Before learning the language we must first understand how and where the language is used. Python has its application in various fields like web development, data science, machine learning, network engineering etc. It is impossible to learn about all the fields at the same time so we must focus on what are the key concepts we need to learn and proceed accordingly.
e.g. for web development, we need to learn Object-Oriented Programming and Django/ Flask while for data science we need to learn Numpy, Pandas, Matplotlib etc along with basic Python syntax.
Learn the Basics
The next step is to get acquainted with the basic Python syntax, data types, conditional statements, loops and various other Python operations.
Thanks to the easy and almost English syntax of python it can be learnt in a week or two even by a beginner. Although some of these concepts might seem very easy at first you must understand them properly for a seamless programming experience.
Here is a book on Python programming that I would definitely recommend for all beginners. And for advanced learners have a look at this book.
Implement as you learn
The best way to learn anything is to implement it and Python is not an exception. Whether you’re learning it from an online course or a book you should get your hands dirty with it.
Just open your computer, set up your coding environment and start coding. For example, if you have learnt about conditional loops try to make a number guessing game using it.
You can also customise it using if-else statements for a better experience. If you are learning a new Python library you can make a small project out of it.
This will improve the understanding of the concept tenfolds.
Build well-structured projects
This point is only applicable after you have learnt the basics of python pretty well.
Building a well-structured project gives you a good idea about complex concepts like OOP, file handling database, concurrency and multithreading. Try to build a project related to the field of your interest.
e.g. If you are interested in web development then build a web application using Django or Flask.
Data science and machine learning enthusiasts can make projects like Handwriting recognition, future stock value prediction etc.
These Keystone projects not only help you gain in-depth knowledge about the field, but they also add some extra points to your CV.
Learn domain-specific concepts and libraries:
When working on a real-world project you cannot proceed with the understanding of python only. We need to learn some concepts and Python libraries specific to that domain to develop the application.
This step is only applicable for developers who are well versed with the concepts of Python.
For example, if you are interested in the field of data science then other than core Python you need to learn concepts and libraries related to them also:
- Data manipulation and cleaning: the most important part of data science is to organise the data and clean it(getting rid of unwanted data). Python libraries you should learn for this are Numpy and Pandas.
- Data visualisation: another important aspect of data science is data visualisation that is representing the data in form of charts, bars, histograms etc. The Python library you need to learn for this is Matplotlib.
- Analysis and ML: to find a pattern from the data using machine learning we need to learn Python frameworks like Scikit-learn, Tensorflow etc.
Here is a book on python programming for data science that I would definitely recommend for all data science enthusiasts.
Conclusion
Although Python is a simple language to learn even for beginners, it might take a good amount of time to get well acquainted with all the basic concepts of python.
Using the above-mentioned approach you can learn the core concepts and the field-specific contents of python easily.
It is advised to refer this book or an online course(plenty of which are available on YouTube) to get all the contents in an organised manner.
The official Python documentation can also be a good place to consult. The main focus should be on implementing whatever you learn and remember, there is no shortcut to learning.