📢 Hey guys, today’s going to be a long read, but please, I’m begging you 🙏 read till the end!
I’m sharing my personal battle scars from transitioning into data science. Learn from my pain so you don’t have to suffer like I did.
🚀 The Myth of Being “Ready” for Data Science
When I switched to data science after 14 years in academia, I thought I was prepared:
✅ I had the math
✅ I had the logic
✅ I had the ambition (HAHAHA)
Then I took my first online courses, and they all followed the same magical recipe:
1️⃣ A tiny, perfectly clean CSV file—no missing values, no outliers.
2️⃣ Split into train and test.
3️⃣ Throw fancy models at it.
4️⃣ Get insanely high accuracy (95%? I must be a genius).
5️⃣ Print results, feel accomplished, close laptop.
🛑 And Then Reality Hit Me Like a Freight Train 🫠
Because in the real world, this is a total lie.
If you follow this fairy tale workflow, I can predict (with 95% confidence) that your model will never make it to production. Instead, it will retire in a forgotten Jupyter notebook, while you wonder why no one wants to use it.
💡 So, What’s the Right Way?
First, take a deep breath. Now, forget your hard skills for a second.
Yes, yes, I know—you spent months mastering TensorFlow and PyTorch, and your heart aches to build a deep learning model with 273 layers.
But not today, my friend.
Instead, you need to talk to the business.
🔹 Step 1: Establish the deployment goal
👉 What problem are we actually solving?
👉 No, “training a model” is NOT the problem. (I know, I was shocked too.)
🔹 Step 2: Define the prediction goal
👉 What exactly are we predicting, and why does anyone care?
👉 If this is unclear, you’re basically just playing with expensive Excel formulas.
🔹 Step 3: Pick evaluation metrics (BEFORE model selection!)
👉 Forget about reaching 99% accuracy.
👉 What does a good enough prediction mean for the business?
👉 Because trust me, nobody outside the data team cares about F1-scores.
🔹 Step 4: ONLY NOW start preparing the data
👉 Not before. Not. Before.
👉 Now that you actually know what you’re doing, you can prepare the data correctly.
🚨 And spoiler alert:
It will NOT be a nice, clean CSV file.
It will be messy.
It will be noisy.
It will make you question your life choices.
🎯 But Once You Survive This Step, THEN You Can Finally Do the Fun Stuff:
✔️ Model selection
✔️ Training
✔️ Optimization
✔️ Celebrating like a data rockstar 🎸
🔥 The Hard Truth About Data Science
Data Science isn’t just about writing fancy algorithms.
It’s about solving real problems.
And if you skip the first steps, you’re just running in circles, wasting time, money, and your sanity.
I learned this the hard way, failing over and over.
But hey, at least now I can pass my pain on to you. You’re welcome. 😆
💬 So tell me—have you ever built a model that ended up in the graveyard of forgotten Jupyter notebooks? Let’s discuss! 🚀