How to Actually Get Started with Python
CS15 - A clear (and human) guide to get started without drowning
You’ve wanted to learn Python for a while…
Too many tabs, not enough progress?
This guide cuts the noise and gives you a shippable path.
Only the pieces that actually move you forward.
Why this, why now
Python is the most versatile “one language, many careers” tool: analytics, ML, web, scripting, automation, LLM apps—you name it.
If you learn it now, you compound for the next decade.
Need more reasons? Then let’s try to give them!
Why Python can supercharge your career?
From data science and web development to automation and artificial intelligence, Python’s applications are vast and continue to expand. You can check the latest StackOverflow Developer Survey 2025, where Python stands as the 4th most-used programming language of all developers!
(And the second one if we only consider those used in the data science field 🔥)
So here are the 5 main reasons why you should start learning Python today…
#1. Beginner-Friendly
Python’s clear syntax resembles English, making it the perfect starting point for beginners.
The learning curve is gentle, allowing you to focus more on solving problems than wrestling with complex code structures.
#2. Extensive Libraries
Python’s ecosystem is rich with libraries. Tools like Pandas, NumPy, and Matplotlib simplify data analysis, while Django and Flask are great for web development.
This wide array of libraries reduces the need to write code from scratch, speeding up development.
#3. High Demand in the Job Market
Python developers enjoy competitive salaries, thanks to the language’s popularity across industries like AI, data science, software development, and automation. Its applications span across finance, tech, healthcare, and more.
#4. Artificial Intelligence
Python is dominant in AI and machine learning. With libraries such as TensorFlow and Keras, developers can implement sophisticated AI models for tasks like natural language processing, image recognition, and even self-driving car algorithms.
#5. Strong Community Support
One of Python’s greatest strengths is its active community. With numerous tutorials, forums, and resources, help is always available. This vibrant support system ensures you won’t be stuck for long when facing a challenge.
So after all of this information, you might be wondering…
How to (ACTUALLY) get started with Python?
This roadmap will help you to get started, build a strong foundation, and progress through more advanced topics.
Let’s break down the steps to mastering Python!
Navigate Your Way Through Python
Whether you’re a data enthusiast, a budding analyst, or an experienced developer looking to sharpen your data science coding skills, understanding Python is crucial.
So let’s start by…
1. Learning the Basics
Before diving into data-heavy tasks, you need to understand Python fundamentals:
Basic Syntax and Variables
Data Types: Strings, Integers, Floats, etc.
Conditionals and Type Casting
Error Handling and Functions (including built-in functions)
Data Structures: Lists, Tuples, Sets, Dictionaries
Loops: for and while loops
2. Data Structures
Once you have a solid foundation of the basics, a good grasp of data structures is crucial for efficient programming:
Arrays and Linked Lists
Heaps, Stacks, and Queues
Hash Tables
Trees, including Binary Search Trees
3. Algorithms
With data structures covered, it’s time to explore algorithms:
Sorting Algorithms: Bubble, Merge, Quick Sort, etc.
Searching Algorithms: Binary Search and Linear Search
Recursion and its applications
Already proficient with Python’s algorithms?
Now it’s turn to further understand Python’s world with its modules and functions.
4. Modules
Python’s extensive library of modules can make your life easier:
Built-in Modules: os, sys, math, etc.
Creating Custom Modules for reusability
5. Advanced Topics
Dive deeper with advanced Python techniques:
Lambdas: Anonymous functions
Decorators: Enhancing functions
Iterators and Generators
Regular Expressions for pattern matching
If you have arrived here… congrats! You are already a mid-level user. Now it’s turn to spice up your coding abilities with OOP 🔥
6. Object-Oriented Programming (OOP)
Python is an object-oriented language, so understanding OOP is essential:
Classes and Objects
Inheritance
Dunder (Magic) Methods to customize class behavior
Now it’s turn to start understanding how to install (and why!) Python’s best libraries.
7. Package Managers
To install libraries and manage dependencies, you’ll need to know:
PyPI and pip
Conda for data science environments
Poetry for project management
Already bored of essentials concepts? Then Python still has a lot more to offer ✨
8. Comprehensions
Python offers elegant ways to create data structures:
List Comprehensions
Generator Expressions
9. Learn a Framework
Choose a framework based on your goals:
Web Development: Django, Flask, Pyramid
Data Science/Visualization: Plotly Dash
Asynchronous: FastAPI, Aiohttp, Tornado
10. Concurrency
For high-performance applications, learn concurrency:
Global Interpreter Lock (GIL)
Threading and Multiprocessing
Asynchrony with async/await
11. Environments
Managing different Python environments is critical:
Virtualenv and Pyenv
Pipenv for dependency management
12. Static Typing
Add type hints to improve readability and catch errors early:
Pydantic and Mypy
Pyright and Pyre
13. Code Formatting
Maintain clean, readable code with formatting tools:
Black, YAPF, and Ruff
14. Documentation
Document your code for clarity:
Sphinx for generating docs
15. Common Packages
Familiarize yourself with essential packages:
Typing and Tox
16. Testing
Testing is crucial for reliable code:
Pytest and Unittest
Doctest and Nose
17. DevOps
Finally, integrate Python into DevOps practices for deployment and scaling.
Phew, that was a lot to cover!
This roadmap is just a summary to help you start your Python journey.
We’ll dive deeper into each topic in upcoming issues, so stay tuned for more!
How to Get Started
Master the Basics: Begin with Python fundamentals—variables, control structures, functions, and data types.
Explore Libraries: Get hands-on with Pandas for data manipulation, Matplotlib for visualizations, and Flask for creating web applications.
Work on Real Projects: Apply your skills by building small projects like a task automation script or a simple web app.
A final note
If you’ve made it this far, you already did the hardest part: decide to start.
Python rewards short, consistent reps.
Ship something small this week, then another next week.
Momentum compounds.
Are you in?
— Josep
Still with me? 🧐
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P.S. Share this with the coworker who isn’t confident in Python, yet.
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