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Tips to Learn Python
Python is a popular, interactive, interpreted, advanced, and object-oriented scripting language. Designed to be highly readable, Python often uses English keywords as opposed to the use of punctuation by other similar languages. It also has fewer syntactic constructions, when compared with other scripting languages.
Python programming language is extensively used in web development, game development, data analytics, and machine learning. In this article on ‘The Best Tips for Learning Python,’ you will learn about Python in the right way. We will also discuss the tips that you should follow while learning it.
Let’s first go through a few pointers that help establish that Python is so immensely popular in the tech industry.
Python is Easy to Learn
It requires considerably lesser resources and time for a beginner to learn and start implementing Python when compared with programming languages like Java or C++.
Python is Cross-Platform
Cross-platform or platform-independent is a programming language feature that allows a developer to run intermediate code on different OS.
Python Has Hundreds of Libraries
Python’s library collection is enormous and allows developers to use pre-written code, saving time when developing a new program.
Python Has Excellent Community Support
Python comes with an abundance of documentation and support material for troubleshooting and learning. A Python developer not able to crack a piece of code needn’t be very concerned and can easily find documentation, guides, and video tutorials to help them move forward, unlike many other programming languages.
Python Supports Scalable Development
If a Python developer is stuck on a piece of code, they shouldn’t find it difficult to get relevant support. From the documentation, guides, and video tutorials, Python community provides plenty of assistance, unlike some of the other languages out there today.
Tips for Learning Python
Now that you are familiar with Python, let’s discuss all the tips you need to learn Python the right way.
Cover Python Fundamentals
At a bare minimum, you must cover the Python fundamentals. You can quickly work through complex Python problems, projects, or use cases if you understand the fundamentals. Some important fundamentals are as follows:
- Variables and Types
- Lists, Dictionaries, and Sets
- Basic Operators
- String Formatting
- Basic String Operations
- List Comprehensions
- Classes and Objects
Establish a Goal for Your Study
After covering all the fundamentals, you should establish a goal for your study.
- Before you start learning Python, establish a goal for your study.
- You’ll know what learning material to focus on or skim through as it pertains to your goals.
- For example, if you are interested in becoming a machine learning engineer, you should refer to only that relevant material.
Several tech industries use Python for development:
Machine Learning and AI
Python is extensively used for the backend development of any web application.
Python also has the capabilities for game development. So if you are into that, learning Python is still an option.
Internet of Things
Internet of Things is a comparatively new field, but it has become largely popular recently, and we also use Python in this field of technology.
Learn Relevant Python Libraries
Once you’ve established your goal, you should also look at the relevant python libraries that help you reach your goal.
- There are hundreds of libraries that are used by Python developers in different domains.
- These libraries make your life easier since they reduce manual workload and wrap code for specialized tasks.
- There are many popular libraries out there; for example, Django for web development, Pandas for data analysis, etc.
Some of the most popular Python libraries are:
- TensorFlow for Machine Learning
- Flask for Web Development
- Scrapy for Web Scraping
- Pygame for Game Development
Do Projects Based on Whatever you Learn
After doing the above, now you are ready to make and work on python projects on your own.
- It is crucial to apply what you learn while learning each Python concept.
- Doing projects as you progress is highly recommended since it will make you a good coder and expand your portfolio.
- Don’t expect to make industry-standard projects from the start, but improve upon them as you learn more.
Some of the exciting ideas that you can base your projects on are:
- Number Guessing
- Web Scraper
- Searching and Sorting
- Tic – Tac – Toe
Consistency is Important
Consistency is really important, no matter what you learn or do.
- It is not realistic for anyone to expect to become a developer within a week.
- Except, be consistent, and work towards short-term goals and reach those goals within a set timeframe.
- Remember, two hours daily for a month are always going to be more productive than cramming everything within a week.
Let’s look at the Python industry trends, and ultimately at the scope of a python developer.
- In recent years, Python has witnessed tremendous growth in demand and is showing no signs of slowing down.
- PYPL, the popular programming language ranking website ranked Python as the number one programming language with a substantial spike in popularity in 2019 and 2020.
Also, Python has surpassed Java and become the 2nd most popular language according to GitHub repository contributions.
- Machine Learning is probably the most popular field in the tech industry right now.
- Python, because of its variety of libraries, is the preferred language for performing machine learning tasks.
- Libraries like NumPy, Pandas, TensorFlow are widely used in machine learning and artificial intelligence.
Software engineers with Python skills are offered better salaries across the globe.
- The average Python developer salary in India is ₹75k and more.
- The average Python developer salary in the US is $79,395.
There you go! We hope you enjoyed the article ‘The Best Tips for Learning Python’ and are now motivated enough to start your learning journey. Be ready to keep working on getting better at Python and landing the job of your dreams
Top python libraries data science
- Scikit Learn