Technical Executive Academy

How I Taught Myself Python (And How You Can Do It Faster)

Introduction

Here’s how I taught myself Python to the point where I can build systems that run entire business operations on autopilot—without spending any money, and in a way that was fun, engaging, and relatively quick (about a year in my case).

In this article, I’ll share the specific steps I took, the projects I built, and how I leveraged real-world applications to accelerate my learning. I’ll also show you how you can do it faster than I did, especially if you’re brand-new to programming.

What is Python?

If you’re in the tech space at all, you already know what Python is and why it’s important. But for the uninitiated:

  • Python is a programming language used everywhere—from web development and automation to artificial intelligence and scientific computing (especially data science).
  • It’s known for its simple, readable syntax and massive ecosystem of libraries and frameworks.
  • Because of its versatility and ease of entry, it’s a great first language to learn for anyone looking to break into tech or automate tasks in their current job.

Why bother learning Python? If you want to solve problems that involve repetitive tasks, data analysis, or custom automation, Python can be a huge time-saver and a powerful tool in your toolkit.

Why I Learned Python (And Should You?)

It’s important to address who should learn Python at a high level and why:

  1. Anyone working with information: If your job involves spreadsheets, large datasets, or repetitive tasks, Python can help automate these workflows.
  2. Entrepreneurs or aspiring leaders: If you’re starting or managing a business, Python can help you build processes that scale—think automating client data management, generating reports, or even handling marketing tasks.
  3. Curious learners: Even if you’re not directly working in tech, understanding how to script and automate tasks opens up a new world of possibilities—freeing up your time and reducing busywork.

I decided to learn Python after repeatedly encountering boring but important tasks at work—like updating client records—that could be automated. Initially, I tried off-the-shelf solutions like Microsoft Power Automate, but they were too glitchy and limited. A friend who works as an actuary recommended Python, so I decided to give it a shot. It quickly became apparent that Python was the key to avoiding rote manual work while still maintaining high-quality results.

How I Learned Python

At first, I went the typical route: free online courses and tutorials. However, I struggled because:

  • Pacing issues: Courses sometimes moved too slow or too fast for my needs.
  • Limited real-world context: Many tutorials focus on contrived examples (“print ‘Hello World!’” and simple loops) rather than real business problems.

I ultimately found a combination of structured reading and project-based experimentation to be the best approach.

1. Textbook Study

  • Automate the Boring Stuff with Python by Al Sweigart was a turning point. Its focus on practical tasks—like automating Excel, PDFs, or Word documents—matched exactly what I wanted to do.
  • As I worked through the chapters, I kept notes on which Python concepts solved actual issues at my job. This gave me immediate ways to apply the lessons.

Still, I noticed a gap: reading about concepts is not the same as practicing them on real problems. So I transitioned to a more project-based learning style.

2. Project-Based Learning

Instead of doing random exercises, I tackled small to mid-sized problems that mattered to me. This drastically increased my retention because I was both curious and invested in the results.

Project-based learning also forces you to confront errors and search for solutions (on places like Stack Overflow or by reading documentation), which accelerates mastery.

Projects I Built

To illustrate how my learning progressed, here’s a breakdown of projects that I undertook by difficulty level. You are welcome to attempt these yourself as I believe they are a good roadmap for success

Beginner Projects

  1. File Organizer
    • What it does: Sorts files into folders based on their extensions.
    • Why it helped: This got me familiar with file I/O, the os module, and basic scripting.
  2. Unit Converter
    • What it does: A dashboard (using a simple command-line interface) that converts units I often needed in my coursework.
    • Key takeaway: Learned about user input, data conversion, and printing formatted strings.
  3. Anagram Trainer
    • What it does: A custom game to improve my anagram skills.
    • Key takeaway: Practiced string manipulation, basic user interaction, and randomization in Python.
  4. Password Generator
    • What it does: Generates secure passwords using a combination of letters, numbers, and special characters.
    • Key takeaway: Learned how to integrate randomness (random or secrets modules) and produce truly random outputs.

Intermediate Projects

  1. To-Do List App
    • What it does: A small GUI (using Tkinter) that tracked tasks and synced with Google Calendar.
    • Why it helped: Forced me to learn about GUIs, the requests library for API calls, and user authentication flows.
  2. Personal News Aggregator
    • What it does: Pulled articles from multiple news APIs and filtered out irrelevant or sensationalist content.
    • Why it helped: Dived into web APIs, JSON parsing, and building simple filters to process text.
  3. Automated Email Sender
    • What it does: Scheduled custom emails (with attachments, greetings, etc.).
    • Why it helped: Practiced using the smtplib library, handling file attachments (MIME), and scheduling tasks with cron (on Linux/Mac) or Task Scheduler (on Windows).
  4. Personal Finance Tracker
    • What it does: Categorized transactions from CSV files using basic NLP rules, then summarized spending trends.
    • Why it helped: Learned about CSV parsing, text classification, and building small dashboards (like summarizing how much I spent by category).
  5. Web Scraper for Job Listings
    • What it does: Extracted job postings from certain sites and emailed them to me each morning.
    • Key takeaway: Learned how to use BeautifulSoup or requests_html, handle pagination, and bypass basic anti-scraping measures.

Advanced Projects

Client Database Automation System

  • Problem: Manually updating and organizing client data took ~40 hours of labor per week.
  • Solution: A Python-based automation system that integrates with an SQL database and a third-party CRM.
  • Impact: Eliminated virtually all manual data entry, allowing colleagues to shift their focus toward strategic or client-facing tasks.

How I Built the Client Database Automation

Breaking down the big project into manageable pieces helped me stay organized and avoid getting overwhelmed. Granted, I did not plan everything from the beginning because that can be overwhelming in my experience. What I mean by managable pieces is tackling one challenge at a time, meaning that I first built the infrastructure to read and write from the database,

  1. Database Management
    • I chose MySQL for its reliability and scalability.
    • I used SQLAlchemy to map database tables to Python objects, simplifying create-read-update-delete (CRUD) operations.
  2. Data Processing
    • Scripts parsed CSV files, incoming emails, and external APIs.
    • Leveraged Python’s standard libraries (csv, email, json) plus third-party packages for API calls (requests).
  3. Third-Party Integration
    • Used the CRM’s REST API to update records programmatically.
    • Combined these calls with my local data checks to keep everything in sync.
  4. Automation & Optimization
    • Scheduled tasks via cron (on Linux) or Task Scheduler (Windows) to run hourly.
    • Included logging and error notifications so I’d be alerted if something went wrong.

The Right and Wrong Way to Use AI for Learning Python

AI tools like ChatGPT, DeepSeek, and GitHub Copilot can be either a massive help or a crutch:

  • Wrong way: Copy-pasting AI-generated code without understanding it. This may solve your immediate problem but leads to zero learning.
  • Right way: Use AI as a teacher or debugging partner. Once you’ve tried solving a problem manually, ask AI for hints or to explain a concept. Then, dissect the code, tinker with it, and confirm you understand every line.

I typically approach AI-driven learning like this:

  1. Attempt to solve the problem myself using basic docs and Stack Overflow.
  2. If stuck, ask AI for a hint or explanation, not the entire code.
  3. Compare the AI’s approach with mine. Integrate insights if they improve my solution.

How to Do It Faster and Easier Yourself

Whether you have zero programming experience or you’re looking to pick up a second language, here’s how you can ramp up more quickly than I did:

Master the Fundamentals

Before diving into any substantial projects, make sure you understand these basics:

  • Control Flow: If-else statements, for/while loops.

  • Syntax & Data Types: Variables, numbers, strings, booleans, lists, tuples, dictionaries, and sets.

  • Functions: How to define, call, and return values from them.

  • Basic Input/Output: Using print() and input().

  • Error Handling: Basic try-except blocks.

These are universal concepts across many programming languages. I like to think of this as learning the alphabet before you learn how to write a short story (which is what a python script is in reality).

Pick a Starter Resource

If you’re brand-new, consider working through a free course (e.g., on Codecademy or Coursera) or a textbook like Automate the Boring Stuff with Python.

Focus on free resources first, especially since many are comprehensive and community-reviewed.

Identify a Tangible Project

Reflect on your daily life—work tasks, personal routines, or recurring annoyances.

Ask: “How could I eliminate or simplify this with code?”

By focusing on personal pain points, your motivation stays high and you’ll learn relevant Python tools quickly.

Adopt a “Build-Measure-Learn” Mentality

  • Build: Start coding something—even if it’s ugly or incomplete.

  • Measure: Check if it works, measure time saved, or gather feedback from actual users.

  • Learn: Study what went wrong (or could be improved). Iterate.
This cycle mirrors how products are developed in the real world and keeps you iterating until you have a solid final product.

Use AI and Community Resources Wisely

AI: Great for clarifying documentation, generating skeleton code, or brainstorming solutions.

Community: Platforms like Stack Overflow or Python subreddits can give quick pointers and help you see how others solve similar problems.

Practical Tips for Success

  1. Set Clear Goals
    • Decide on a timeline or a particular project milestone (e.g., “Finish my web scraper by the end of next month”).
  2. Version Control
    • Even if you’re a beginner, learn Git and GitHub early. This teaches you good habits—like committing changes, writing commit messages, and branching your work to test features.
  3. Stay Organized
    • Keep a project diary or a simple README file in each project folder to track decisions, bugs, and breakthroughs. This is invaluable when revisiting a project months later.
  4. Refactor as You Learn
    • It’s normal to write inefficient code at first. As you pick up new tricks (e.g., list comprehensions, better variable naming, or error handling), revisit and polish older projects.
  5. Celebrate Small Wins
    • Each time you automate a task or learn a new library, recognize that you’re leveling up. Momentum is essential for self-taught learners.

Final Thoughts

Learning Python (or any programming language) is less about cramming syntax and more about solving real problems. I firmly believe that if you approach learning with curiosity and a desire to automate something tangible in your own life, you’ll:

  1. Enjoy the process more.
  2. Retain concepts better.
  3. Ultimately become a more resourceful engineer or professional.

And if you feel overwhelmed, remember: no one writes flawless code on the first try. Errors, debugging, and iteration are all part of the journey.

So pick a project—big or small—and get started. Leverage free resources, ask AI for help when you’re stuck, and keep pushing through. Before you know it, you’ll have a portfolio of projects showcasing your new Python skills.

🚀 The best way to learn Python? Build something today.

Additional Resources

  • Official Python Documentation – The definitive guide for Python syntax and libraries.
  • Automate the Boring Stuff with Python – If you like a hands-on, project-focused textbook.
  • Stack Overflow – When you hit an error message or need a second opinion on how to approach a problem.
  • Real Python – Great articles and tutorials that go deeper into specific topics like web scraping, GUI development, etc.

Good luck on your Python journey, and don’t forget to have fun automating the mundane so you can spend more time on strategic, creative, and fulfilling tasks!

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