Acquiring a new skill for your current profession or switching to a new career path is easier than ever today, especially when you focus on learning thought-after tech skills like programming. Countless boot camps, communities, and free resources build a robust ecosystem around the most popular programming languages. This means whatever language you choose, you'll have all the cards in your hand to become proficient in it.
Today, we will explore how to learn Python, a fantastic starting point for newbies. Python is user-friendly, universal, and widely used in web development, data analysis, and automation. It's a smart choice regardless of whether you learn it for a career breakthrough or wish to gain some coding knowledge for your independent projects.
As experts in retraining people from different professions into programmers, we have prepared a comprehensive step-by-step guide to help you master Python programming most efficiently. Follow the outlined steps to reach your goal and master programming without pitfalls.
Why is learning Python so beneficial?
Proficiency in Python programming may benefit you in many ways. We’ll get to them gradually, don’t worry.
Let's start with what's on the surface. Python is one of the easiest (among the most popular) languages you can try, even if you have no prior experience with coding:
Smooth Learning: Python's simple syntax mimics natural language, making learning more accessible for beginners.
Multifaceted: You can use Python in various fields, such as web development, data science, automation, and more.
Large Community: A vast community of Python developers means that plenty of resources, tutorials, and support are available.
Of course, let's highlight the career perspectives of those with Python knowledge. This language can lift you in almost anything, from marketing and product management to scientific research and hardcore programming.
Careers Available with Python
Web Developer: Builds and maintains websites — average Salary in the U.S. is $80,000 per year.
Data Analyst: Interprets complex data to help companies make decisions — average Salary in the U.S. is $108,140 per year.
Software Engineer: Designs and creates software solutions — average Salary in the U.S. is $166,370 per year.
Machine Learning Engineer: Develops algorithms that allow computers to learn and make decisions— average Salary in the U.S. is $160,663 per year.
DevOps Engineer: Manages and streamlines software development and IT operations — average Salary in the U.S. is $130,000 per year.
Impact on your everyday life
Python is a great language to learn and improve your everyday life. Here are just a few examples of how you can get rid of routine with a bit of knowledge of this language:
Write scripts to automate repetitive tasks like renaming files, sending emails, or organizing data,
Develop your games, apps, or websites — for fun or practical use,
Build adjusted tools to manage your time, track habits, or keep notes organized.
You can further learn and explore other areas of interest, such as artificial intelligence, robotics, or data visualization. These technologies rely on Python, so Python might become your key to the future in tech.
How to Learn Python: 9 Simple Steps To Success
We hope that by now, you're excited about learning Python—as you should be! But please retake your seats, and let's discover the best route from rookie to proficient Python coder together.
Programming can be overwhelming; that's what we know from the experience of CodeGym graduates. So, let's outline the steps of the learning experience that will bring you to your goal — start programming in Python.
Step 1: Identify Your Motivation and Goals
Your learning plan depends on the ultimate goal of your coding training. When it comes to Python learners, here are some common motives:
Career Advancement: Many people start learning Python to improve their job prospects and qualify for data analyst, software engineer, or machine learning engineer roles. Python is highly valued in different fields, so it's a popular choice for career changers and those looking to advance in their current field.
Data Analysis and Visualization: Python's libraries, such as pandas, NumPy, and Matplotlib, make data analysis and visualization convenient. Learners often aim to use Python to analyze large datasets, create visual reports, and derive insights that inform decision-making.
Automation and Scripting: Python is commonly used to automate repetitive tasks like file management, web scraping, and data entry. Learners seek to streamline their workflows and save time by writing scripts that handle these tasks.
Web Development: With frameworks like Django and Flask, Python is a popular choice for building websites and web applications. Many learners aim to develop websites, create web services, or contribute to open-source projects.
Scientific Computing: Researchers and scientists use Python to perform simulations, model, and solve complex mathematical problems. Learning Python helps them perform scientific computing tasks.
Hobby Projects: Python enthusiasts often pursue personal projects such as game development, building bots, or creating apps. These projects allow learners to apply their skills creatively and enjoyably.
Learning Programming Fundamentals: Many beginners see Python as an easy introduction to programming. Its simple syntax and readability make it an excellent starting point for understanding basic coding concepts.
The depth and time of learning will differ depending on the knowledge base you need for your goal. However, in any of those cases, you should stay on track with your training without losing motivation. Try to make programming your everyday habit to succeed.
Step 2: Learn the Basics Thoroughly
What is considered to be the basics of Python? Here are the fundamentals you should dig deeper from the beginning:
Variables and Data Types: Learn how to store and manipulate data using variables. Get acquainted with the standard data types, including integers, floats, strings, and booleans.
Control Structures: Learn how to use conditional statements (if, elif, else) to make decisions in your code.
Loops: Understand how the loops (for and while) work to repeat code execution until a condition is met.
Functions: Try to define reusable blocks of code using def to create functions. Learn parameters and return values.
Lists and Tuples: Try working with collections of items. Lists are mutable, whereas tuples are immutable.
Dictionaries: Understand how to use key-value pairs to store and retrieve data efficiently.
String Manipulation: Lear methods to manipulate and format strings.
File Handling: Try reading from and writing to files to manage data persistence.
Exception Handling: Learn how to use try, except, finally to handle errors in code.
Basic Input/Output: Study how to take user input and display output.
List Comprehensions: Try using concise syntax to create lists based on existing lists.
This seems like a little. However, even with basic knowledge, you can solve some cool tasks, such as writing programs that read a text from a file and analyze it, sort the digits, or even perform simple arithmetic operations.
Remember that the ideal formula for learning to code is 20% / 80% practice, so don't skip the coding tasks.
Step 3: Master Advanced Python concepts
When you become confident in your knowledge foundation, it’s time to move to advanced concepts:
Object-oriented programming (OOP): The principles of OOP include classes, objects, inheritance, encapsulation, and polymorphism. If you know OOP, you’re able to create and manage complex data structures and behaviors.
Decorators: Functions that modify the behavior of other functions or methods. You will later use them for logging, access control, instrumentation, and other cross-cutting concerns.
Generators and Iterators: Learn how to create iterators and generators using the yield keyword. It can help handle large data streams and implement custom iteration behavior.
Context Managers: with statements and context manager protocols (__enter__ and __exit__ methods) are utiliized to manage resources such as file streams or network connections efficiently and safely.
Metaprogramming: Master writing code that manipulates code, such as creating decorators, context managers, and using metaclasses. It involves using the type function, the inspect module, and understanding the Python object model.
Concurrency and Parallelism: Learn how to use threading, multiprocessing, and asynchronous programming (asyncio) to perform tasks concurrently or in parallel. It helps improve performance and handle I/O-bound and CPU-bound operations.
Testing and Debugging: Master advanced techniques for writing and running tests using frameworks like unittest, pytest, and mock. Get hands-on with debugging tools and practices such as using pdb, logging, and profiling to find and fix bugs efficiently.
Functional Programming: Understand functional programming paradigms such as higher-order functions, pure functions, and immutability. Use tools like map, filter, reduce, and comprehensions.
Network Programming: Learn about building and working with network applications using libraries such as socket, asyncio, and frameworks like Flask and Django for web development.
Advanced Data Structures: Figure out how to implement and use complex data structures such as heaps, balanced binary trees, graphs, and tries. Also, understand the performance implications of various data structures.
Package and Dependency Management: Learn more about managing project dependencies and environments using tools like pip, virtualenv, and poetry. Figure out the creation and distribution of packages using setuptools and publishing to the Python Package Index (PyPI).
Reflection and Introspection: These techniques are essential for examining and modifying the properties of objects, functions, and modules at runtime. Try using the inspect module and understand the use of getattr, setattr, and other introspection functions.
Data Serialization: Master techniques for serializing and deserializing data using JSON, XML, and binary formats with libraries such as pickle and JSON.
Regular Expressions: Try using the re module to perform complex pattern matching and text manipulation tasks.
Memory Management and Performance Tuning: Get a comprehensive understanding of Python's memory model, garbage collection, and techniques for optimizing performance, such as profiling, caching, and using efficient data structures.
These advanced concepts will help you build more complex, efficient, and scalable applications.
Step 4: Practice with Simple Projects
Remember to enhance your training by solving various coding tasks and getting hands-on coding. Use these ideas for simple projects to boost your knowledge and confidence:
Create a basic calculator for addition, subtraction, multiplication, and division. This project will help you practice basic arithmetic operations and user input handling.
Develop a simple command-line to-do list application where users can add, view, and delete tasks. This will help you understand list manipulation and basic file handling for saving tasks.
Write a game in which the program randomly selects a number, and the user has to guess it. This will give you experience with loops, conditionals, and random number generation.
Create a program that generates random passwords. This project will enhance your understanding of string manipulation and using libraries like random.
Build a unit conversion tool (e.g., converting kilometers to miles, Celsius to Fahrenheit). This will give you practice with user input, conditionals, and basic mathematical operations.
Step 5: Build Independent Projects
Moving to advanced concepts in your training, enhance the complexity of your pet projects:
Build an application to track income and expenses. Users can add, edit, and delete transactions, and the app calculates the balance. This project will involve file handling, user input, and data manipulation.
Create a weather application that fetches data from a weather API and displays the current weather conditions for a given location. You'll learn about working with APIs and JSON data.
Develop a simple blog where users can create, edit, and delete posts. This project will introduce you to basic CRUD (Create, Read, Update, Delete) operations and working with a file-based database.
Create a flashcard app for learning new vocabulary or concepts. Users can add new cards, review them, and mark them as learned. This project helps with practicing data structures and user interface design.
Build a simple recommendation system that suggests movies based on user preferences. This project will help you understand basic machine learning concepts and data handling.
Finally, you can create a few grand and complex projects when you complete your Python core learning and grasp other programming technologies. For example:
Develop a full-fledged web application using the Django framework. Include user authentication, database management, and dynamic content rendering. This project will help you understand web development and backend programming.
Create a real-time chat application using Python and WebSockets. This project involves understanding networking concepts, real-time data transfer, and frontend-backend communication.
Build a data analysis dashboard using Python libraries like pandas and Plotly, integrated with a web framework like Flask. This project involves data manipulation, visualization, and web development.
Train a machine learning model using libraries like scikit-learn or TensorFlow and deploy it as a web service using Flask or FastAPI. This project covers data science, model training, and API development.
Create a home automation system using Python and Raspberry Pi. Control lights, temperature, and other devices remotely. This project involves hardware interaction, networking, and Python programming.
Step 7: Engage with the Python Community
Being in the community with peers and more experienced programmers is excellent not only because you'll need help with grasping some programming concepts. It's also the easiest way to get valuable insight into your level of competence, knowledge gaps, and areas for improvement. Try to engage in communities from the beginning. Don't just ask questions — help other newbies with answers when you can provide a solution. It's the best way to solidify your knowledge.
The Most Popular Python Online Communities
Python.org: The official website that hosts a comprehensive list of Python resources, documentation, and community links.
Real Python: A website offering tutorials, articles, and a community forum focused on Python programming.
Reddit (r/Python): A popular subreddit where Python enthusiasts share news, projects, and discussions.
Talk Python to Me: A podcast and community focused on Python developers and their stories.
Python Discord: A large and active community on Discord where Python developers can chat, share resources, and help each other.
Biggest General Programming Communities
Stack Overflow: The largest and most well-known Q&A site for developers, covering all programming languages and topics.
GitHub: A platform for hosting and collaborating on code repositories. It includes a vast community of developers contributing to open-source projects.
Reddit (r/programming): A subreddit for general programming news, discussions, and questions.
Dev.to: A community of developers sharing articles, tutorials, and discussions on various programming topics.
Step 8: Build a portfolio of projects
Building a portfolio as a novice programmer is a smart way to showcase your skills and progress, especially if you've followed our hints and created a few projects while mastering the programming.
If you pursue a developer career, these projects will help you prove your qualifications to future employers.
That's why we advise documenting your code well, explaining the purpose of each project and the problem it solves. Use GitHub to host your code so potential employers can easily access it. As you complete each project, focus on improving code quality and readability. It's okay if your early projects aren't perfect. The goal is to show growth over time.
Remember to add a brief description of each project in your portfolio. This will highlight your journey from novice to more skilled programmer and give you an edge in job applications.
Finally, you can even create a resume on your GitHub profile if you pursue a developer's career.
If you need help figuring out where to start while organizing your portfolio, here's a tip: take inspiration from others. Search for the best portfolios of Python developers on GitHub and take notes.
Step 9: Continuously Learn and Improve
If you've already mastered the basics and advanced concepts of Python, here are several strategies to continuously learn and improve:
Contribute to the Open-Source community and work on personal projects. Hands-on coding practice and collaboration with other programmers will skyrocket your skills and bring new ideas for self-improvement as a programmer.
Stay Updated with the Latest Trends. Follow Python Enhancement Proposals (PEPs) to understand new features and changes in the language. Subscribe to Python newsletters, blogs, and YouTube channels like Real Python, Talk Python to Me, and PyBites. And, of course, subscribe to the CodeGym newsletter to get valuable insights for novice programmers.
Improve Your Coding Practices. Participate in code reviews, try pair programming, and master code refactoring to improve its structure, readability, and performance.
Learn New Paradigms and Languages. This will broaden your understanding of different approaches and paradigms, which can enhance your Python skills.
Deepen Your Theoretical Knowledge. Study computer science concepts such as algorithms, data structures, complexity theory, and system design.
Teach and Share Knowledge. Teaching is a great way to reinforce your own understanding. Mentor beginners in Python programming. This can give you new perspectives and solidify your knowledge.
Why Many Fail in Python Learning: A List of Blunders To Avoid
Learning Python can be a rewarding experience, but many learners face challenges that can hinder their progress. Here are some common mistakes that are well-known but are still surprisingly common:
Lack of Practice: One of the biggest reasons learners struggle is not practicing enough. Coding is a skill that improves with consistent practice and hands-on experience.
Skipping Fundamentals: Beginners need a solid understanding of the basics before jumping into advanced topics. This can lead to confusion and frustration.
Unrealistic Expectations: Expecting to become an expert overnight can be discouraging. Learning to code takes time and patience.
Fear of Making Mistakes: Many learners fear making mistakes or encountering errors. However, mistakes are a crucial part of the learning process.
Not Seeking Help: Trying to learn in isolation without asking for help when stuck can slow down progress. Engaging with the community or finding a mentor can be very beneficial.
Overwhelmed by Resources: With so many tutorials, books, and courses available, beginners can feel overwhelmed and need help figuring out where to start. We recommend starting with one primary source (for example, a course with a mentor) and 2-3 additional ones (a platform with coding challenges, a good video tutorial, etc.).
Lack of Clear Goals: Without clear objectives or projects to work on, learners may lose motivation and direction.
Inconsistent Learning: Learning with a "flexible" schedule will inevitably lead to the acquired knowledge fading from memory, and progress will halt.
In Conclusion: Why You Should Start Learning Python As Soon As Possible
Learning Python in 2024 is a great idea for those who want to be in demand in the job market or simply expand their horizons. By taking the right steps on this path, anyone can succeed. We hope our guide will help you structure your experience in learning Python programming and reach your ultimate goal.
All you need is your desire, time, and the right resource for learning.
How long does it take to master Python programming? Let's look at the experience of CodeGym graduates. Over the past 12 years, more than 30,000 of our students have started their IT careers after completing their studies. More than 20% of them learned programming and found their first job in just 3-6 months, and another 30% did so within 6-12 months.
However, if you want to learn Python programming from scratch, without rushing and at a comfortable pace, plan on spending about a year on regular studies — with mentoring, lectures, problem-solving, and time for coding projects. This is exactly how the Python Software Fullstack Engineer course is structured, preparing students from zero to employment at middle-level positions within a year.
No matter what level you are at now. What matters is who you trust with your education. Try writing your first code and watch your skills improve day by day. Ready to take the first step? Then go for it!
