Python is one of the easiest programming languages to pick up. What's really nice is that learning Python doesn't pigeonhole you into one domain; Python is so versatile it has applications in software development, data science, artificial intelligence, and almost any role that has programming involved with it!
Richie Cotton, Data Evangelist at DataCamp
For a full explanation of getting set up, check out our guide to how to install Python.
Write your first Python program
Start by writing a simple Python program, such as a classic "Hello, World!" script. This process will help you understand the syntax and structure of Python code. Our Python tutorial for beginners will take you through some of these basics.
Python data structures
Python offers several built-in data structures like lists, tuples, sets, and dictionaries. These data structures are used to store and manipulate data in your programs. We have a course dedicated to data structures and algorithms in Python, which covers a wide range of these aspects.
Control flow in Python
Control flow statements, like if-statements, for-loops, and while-loops, allow your program to make decisions and repeat actions. We have a tutorial on if statements, as well as ones on while-loops and for-loops.
Functions in Python
Functions in Python are blocks of reusable code that perform a specific task. You can define your own functions and use built-in Python functions. We have a course on writing functions in Python which covers the best practices for writing maintainable, reusable, complex functions.
3. Master intermediate Python concepts
Once you’re familiar with the basics, you can start moving on to some more advanced topics. Again, these are essential for building your understanding of Python and will help you tackle an array of problems and situations you may encounter when using the programming language.
Error handling and exceptions
Python provides tools for handling errors and exceptions in your code. Understanding how to use try/except blocks and raise exceptions is crucial for writing robust Python programs. We’ve got a dedicated guide on exception and error handling in Python which can help you troubleshoot your code.
Working with libraries in Python
Python's power comes from its vast ecosystem of libraries. Learn how to import and use common libraries like NumPy for numerical computing, pandas for data manipulation, and matplotlib for data visualization. In a separate article, we cover the top Python libraries for data science, which can provide more context for these tools.
Object-oriented programming in Python
Python supports object-oriented programming (OOP), a paradigm that allows you to structure your code around objects and classes. Understanding OOP concepts like classes, objects, inheritance, and polymorphism can help you write more organized and efficient code.
To learn more about object-oriented programming in Python, check out our online course, which covers how to create classes and leverage techniques such as inheritance and polymorphism to reuse and optimize your code. You might also find our tutorials on list comprehensions and dictionary comprehensions useful for writing more Pythonic code.
4. Learn by doing
One of the most effective ways to learn Python is by actively using it. You want to minimize the amount of time you spend on learning syntax and work on projects as soon as possible. This learn-by-doing approach involves applying the concepts you've learned through your studies to real-world projects and exercises.
Thankfully, many DataCamp resources use this learn-by-doing method, but here are some other ways to practice your skills:
- Take on projects that challenge you. Work on projects that interest you. This could be anything from a simple script to automate a task, a data analysis project, or even a web application.
- Attend webinars and code-alongs. You’ll find plenty of DataCamp webinars and online events where you can code along with the instructor. This method can be a great way to learn new concepts and see how they're applied in real-time.
- Apply what you've learned to your own ideas and projects. Try to recreate existing projects or tools that you find useful. This can be a great learning experience as it forces you to figure out how something works and how you can implement it yourself.
A range of Python projects on DataCamp Projects
5. Build a portfolio of projects
As you complete projects, compile them into a portfolio. This portfolio should reflect your skills and interests and be tailored to the career or industry you're interested in. Try to make your projects original and showcase your problem-solving skills.
We’ve got a list of 60+ Python projects for all levels in a separate article, but here are a few suggested project ideas for different levels:
- Beginners. Simple projects like a number guessing game, a to-do list application, or a basic data analysis using a dataset of your interest.
- Intermediate. More complex projects like a web scraper, a blog website using Django, or a machine learning model using Scikit-learn.
- Advanced. Large-scale projects like a full-stack web application, a complex data analysis project, or a deep learning model using TensorFlow or PyTorch.
We’ve got a full guide on how to build a great data science portfolio, which covers a variety of different examples. And don’t forget; you can build your portfolio with DataCamp to show off your skills.
6. Keep challenging yourself
Never stop learning. Once you've mastered the basics, look for more challenging tasks and projects. Specialize in areas that are relevant to your career goals or personal interests. Whether it's data science, web development, or machine learning, there's always more to learn in the world of Python. Remember, the journey of learning Python is a marathon, not a sprint. Keep practicing, stay curious, and don't be afraid to make mistakes.
An Example Python Learning Plan
Below, we’ve created a potential learning plan outlining where to focus your time and efforts if you’re just starting out with Python. Remember, the timescales, subject areas, and progress all depend on a wide range of variables. We want to make this plan as hands-on and practical as possible, which is why we’ve recommended projects you can work on as you progress.
Month 1-3: Basics of Python and data manipulation
Master basic and intermediate programming concepts. Start doing basic projects in your specialized field. For example, if you're interested in data science, you might start by analyzing a dataset using pandas and visualizing the data with matplotlib.
- Python basics. Start with the fundamentals of Python. This includes understanding the syntax, data types, control structures, functions, and more.
- Data manipulation. Learn how to handle and manipulate data using Python libraries like pandas and NumPy. This is a crucial skill for any Python-related job, especially in data science and machine learning.
Recommended resources & projects
- Python Fundamentals
- Investigating Netflix Movies and Guest Stars in The Office Data Science Project
- Python Cheat Sheet for Beginners
Month 4-6: Intermediate Python
Now that you have a solid foundation, you can start learning more advanced topics.
- Intermediate Python. Once you're comfortable with the basics, move on to more advanced Python topics. This includes understanding object-oriented programming, error handling, and more complex data structures. Explore more advanced topics like decorators, context managers, metaclasses, and more.
- More specific topics. If you're interested in machine learning, for example, you might start the Machine Learning Fundamentals with Python Track. Continue to work on projects, but make them more complex. For example, you might build a machine learning model to predict house prices or classify images.
Recommended resources & projects
- Python Programmer Career Track
- A New Era of Data Analysis in Baseball Project
- Object-Oriented Programming in Python (OOP): Tutorial
Month 7 onwards: Advanced Python and specialization
At this point, you should have a good understanding of Python and its applications in your field of interest. Now is the time to specialize.
- Specialization. Based on your interests and career aspirations, specialize in one area. This could be data science, machine learning, web development, automation, or any other field. For instance, If you're interested in natural language processing, you might start learning about libraries like NLTK and SpaCy. Keep working on projects and reading about new developments in your field.
Recommended resources & projects
- Machine Learning Scientist with Python Career Track
- Naïve Bees: Image Loading and Processing Project
- Mastering Natural Language Processing (NLP) with PyTorch: Comprehensive Guide
Learn Python roadmap
Below, we've compiled a basic visual roadmap based on the Python learning path. This can help you visualize your progress as your aim for Python mastery:
6 Top Tips for Learning Python
If you’re eager to start your Python learning journey, it’s worth bearing these tips in mind; they’ll help you maximize your progress and keep focused.
1. Choose Your Focus
Python's versatility spans web development, data analysis, machine learning, and more. To streamline your learning, consider focusing on a specific area aligned with your career goals or interests. For instance, aspiring data scientists can prioritize libraries like pandas and NumPy, while those targeting web development may explore frameworks like Django or Flask.
Focusing doesn't limit you; Python's skills are transferable across domains. Once you're comfortable, you can broaden your expertise to other areas.
2. Practice regularly
Consistency is essential for learning Python—or any new language. Aim to code daily, even if it's just for a few minutes, to reinforce your knowledge and improve retention.
Daily practice doesn't require tackling complex projects. It can involve reviewing concepts, refining previous code, or solving simple challenges to build confidence and maintain momentum.
3. Work on real projects
The best way to learn Python is by using it. Working on real projects gives you the opportunity to apply the concepts you've learned and gain hands-on experience. Start with simple projects that reinforce the basics, and gradually take on more complex ones as your skills improve. This could be anything from automating a simple task, building a small game, or even creating a data analysis project.
4. Join a community
Learning Python is easier and more rewarding when shared with others. Communities provide support, motivation, and valuable opportunities to learn from peers.
Consider joining local Python meetups for in-person connections or participating in online forums to ask questions, share knowledge, and gain insights from others' experiences.
5. Don't rush
Learning to code takes time, and Python is no exception. Don't rush through the material in an attempt to learn everything quickly. Take the time to understand each concept before moving on to the next. Remember, it's more important to fully understand a concept than to move through the material quickly.
6. Keep iterating
Learning Python is an iterative process. As you gain more experience, revisit old projects or exercises and try to improve them or do them in a different way. This could mean optimizing your code, implementing a new feature, or even just making your code more readable. This process of iteration will help reinforce what you've learned and show you how much you've improved over time.
The Best Ways to Learn Python in 2026
There are many ways that you can learn Python, and the best way for you will depend on how you like to learn and how flexible your learning schedule is. Here are some of the best ways you can start learning Python from scratch today:
Online courses
Online courses are a great way to learn Python at your own pace. We offer over 150 Python courses for all levels, from beginners to advanced learners. These courses often include video lectures, quizzes, and hands-on projects, providing a well-rounded learning experience.
If you're totally new to Python, you might want to start with our Introduction to Python course. For those looking to grasp all the essentials, our Python Fundamentals skill track covers everything you need to start programming. Once you're comfortable with the basics, our Intermediate Python course will help you level up your skills.
Top Python courses for beginners
- Python Fundamentals Skill Track
- Python Programmer Career Track
- Introduction to Python
- Python Data Science Toolbox
- Writing Efficient Python Code
Tutorials
Tutorials are a great way to learn Python, especially for beginners. They provide step-by-step instructions on how to perform specific tasks or understand certain concepts in Python.
We have a wide range of tutorials available related to Python and associated libraries. So whether you’re just getting started or hoping to improve your existing knowledge, you’re sure to find topics of interest.
Top Python tutorials
- Python Tutorial for Beginners
- How to install Python
- 30 Cool Tricks for Better Python Code
- 21 Essential Python Tools
Cheat sheets
If you’re looking for a fast way to brush up on specific Python principles, cheat sheets are a handy way to have a lot of knowledge in one resource. For example, our Python Cheat Sheet for Beginners covers many of the core concepts you’ll need to get started.
We also have cheat sheets for specific Python libraries, such as Seaborn and SciPy, which include example code snippets and tips to get the most out of the tools.
A selection of cheat sheets
Top Python cheat sheets
- Python Cheat Sheet for Beginners
- Working with Dates and Times in Python Cheat Sheet
- Python Data Visualization: Bokeh Cheat Sheet
- Importing Data in Python Cheat Sheet
Projects
Working on projects helps you utilize the skills you’ve learned already to tackle new challenges. As you work your way through, you’ll need to adapt your approach and research new ways of getting results, helping you to master new Python techniques.
You can find a whole range of data science projects to work on at DataCamp. These allow you to apply your coding skills to a wide range of datasets to solve real-world problems in your browser, and you can filter specifically by those that require Python.
Top Python projects
- 60+ Python Projects for All Levels of Expertise
- Exploring the History of Lego
- Investigating Netflix Movies and Guest Stars in The Office
- NYC Airbnb Data Analysis
Books
Books are an excellent resource for learning Python, especially for those who prefer self-paced learning. Learn Python the Hard Way by Zed Shaw and Python Crash Course by Eric Matthes are two highly recommended books for beginners. These books provide in-depth explanations of Python concepts along with numerous exercises and projects to reinforce your learning.
Top Python books
- Learning Python, 5th Edition: Lutz, Mark
- Head First Python: A Brain-Friendly Guide: Barry, Paul
- Python Distilled (Developer's Library): Beazley, David
- Python 3: The Comprehensive Guide to Hands-On Python Programming
Python for Business Users
It's not just individuals who may want to upskill in Python. As businesses increasingly rely on data-driven decision-making, the demand for Python proficiency among professionals has surged. For those looking to enhance their team's capabilities, DataCamp for Business offers a comprehensive solution.
Why Choose DataCamp for Business?
DataCamp for Business offers all of the great benefits of a regular DataCamp subscription but in a way that can scale depending on the needs of your organization. Here are just a few of the benefits:
- Tailored learning paths: Structured learning paths cater to various roles, ensuring relevant training for each team member.
- Hands-on practice: Interactive exercises and real-world projects help users apply their knowledge practically.
- Scalability and flexibility: Suitable for training small teams or entire departments, with users learning at their own pace.
- Expert instructors: Courses are designed by industry experts, ensuring practical and up-to-date content.
- Comprehensive content library: Extensive resources cover Python for data analysis, machine learning, and more.
The key to standing out is to show your project made an impact and show that other people cared. Why are we in data? We're trying to find insights that actually impact a business, or we're trying to find insights that will actually shape society or create something novel. We're trying to improve profitability or improve people's lives using and analyzing data, so if you don’t somehow quantify the impact, then you are lacking impact.
Nick Singh, Author of Ace the Data Science Interview
60% to 70% of applications get shifted out of consideration before humans actually look at the application.
Jen Bricker, Former Head of Career Services at DataCamp
