I remember staring at my screen three years ago, overwhelmed by the same question: Where the heck do I start?
I’d just quit my marketing job, desperate to break into data science. My bookmarks folder looked like a cry for help—Coursera, Udemy, edX tabs spilling everywhere. I wasted $200 on a ‘complete ML bundle’ that taught me nothing but how to pick better courses.
If you’re like the Reddit user asking for recommendations, let’s cut through the noise. Here’s what actually worked for me—no sponsorships, no hype.
## The One That Made Math Click
Andrew Ng’s Machine Learning course on Coursera. Yeah, it’s everywhere for a reason. The math is explained like you’re chatting over coffee—not a textbook. It uses Octave instead of Python, which felt weird at first, but that’s the point. You learn concepts without getting stuck on libraries.
Downside? A bit dated (2012). But the fundamentals haven’t changed. Skip it and you’ll keep hitting walls later.
## The Job-Ready Project Launcher
Udacity’s Data Scientist Nanodegree. I’ll be real—it’s pricey ($400/month). But the hiring partner projects? Gold. You build portfolio pieces companies care about: A/B tests for Airbnb, churn prediction for banks.
They hold your hand through Git, SQL, even how to explain models to non-tech folks. Most courses skip this stuff. The final project I built? An Uber demand estimator that now gets ~2k visits/month on GitHub Pages.
## The Free ‘Just 10 Minutes a Day’ Option
Kaggle Learn. No signup wall. No credit card. Their micro-courses (like ‘Python for Data Science’) fixed my biggest weakness: writing clean, reusable code.
Try the ‘Pandas’ module. Go from ‘how do I filter rows?’ to merging datasets in 15 minutes. Perfect for lunch breaks.
## The Secret Weapon for Imposter Syndrome
Fast.ai. This one’s different. They start with real code first, theory later. You train image classifiers on day one.
It’s messy. You’ll break things. But that’s how you learn—by fixing mistakes. Free and built for people who hate math jargon.
## Here’s the Real Talk
No course will get you hired. I bombed three interviews after finishing Ng’s class. What changed? I treated the projects like my résumé. I blogged about cleaning healthcare data. I fixed typos in Kaggle kernels and tagged the authors. That visibility got me my first contract.
If you’re starting? Grab Ng’s course and build one tiny project this week. A single cleaned dataset on GitHub beats ten unfinished certificates.
The Reddit user asked about Coursera—but honestly? Mix free and paid. Spend money only when you need structure (like that Nanodegree). Otherwise, steal time in stolen moments: 20 minutes on Kaggle before work, one Fast.ai lesson on Sundays.
You’ve got this. Now go break something in Python.