How to get started with Python

BY: RYAN SINGH  |   ENFOCUS

Three free resources to get you started

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When I started learning Python about 6 years ago, the sheer amount of resources available on the internet made it hard to know where to begin.

At first glance, that might seem like a good thing. But with endless tutorials, forums and videos, it’s easy to feel paralyzed by not knowing where to start. Many beginners get caught up in searching for the “best” resource, weighing the opportunity cost of every choice. My advice is simple: Just start. Pick one resource, stick with it for a while and the rest will fall into place.

Too much choice can stop beginners before they even write their first line of code. Finding a clear, structured path is often more valuable than having every possible resource at your fingertips. Sometimes, less really is more.

You should read the following 3 resources in order, especially if you’re just starting out.

100 Page Python Intro

This book is a short, introductory guide to the Python programming language. It’s designed to help you get started quickly and build a strong foundation that you can build on as you tackle real-world projects.

It goes over Python fundamentals like – data types, functions and lists. This is the foundation of Python, no project in Python you will ever do will be without these foundational topics.

Book

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Python for Data Analysis

The gold standard for people getting started with Python. It goes over basic concepts like pandas and NumPy – foundational data structures for modern Python applications. The author of the book, Wes McKinney, started pandas, the most popular data wrangling library in Python.

Even though the book says “data analysis,” most concepts will translate over to other applications such as Gen. AI, software development and robotics. 

Even if you’re completely new to programming, the book explains core ideas clearly and helps you build confidence by showing how Python fits into the bigger picture of data science. By the end, you’ll have a solid foundation for tackling larger projects, exploring machine learning or working with big data.

Book

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Machine Learning Specialization

Probably the most iconic course from the most iconic professor by the most iconic university.

This course goes over how to build machine learning algorithms from the ground up. After this course, you should have a solid understanding of how machines learn. For example, this course gives you the foundation math for building algorithms to detect handwritten digits.

Although the course says you don’t need any Python experience, I’d recommend having at least 2-3 hours of basic Python under your belt. Knowing how to use for-loops and if-statements will make things much smoother. Having a basic understanding of calculus is also a plus but not required.

Course

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Overall, if you want to truly understand what’s under the hood of today’s AI tools, this is the best place to start.