☕️📊 The Real Data Science Case Study: Coffee & Office Attendance

Hey guys, let’s have some fun with this one today, it’s a no-brainer but still a serious post!

They say data science is all about finding hidden patterns, but some correlations are just screaming at us. Like this one:

☕️ Quality of office coffee 📈 Percentage of office days attended 📈

Coincidence? I think not.

And let’s talk about the most crucial productivity metric:

☕️ Number of coffee cups per dayLines of code written / Meetings survived


Honestly, my coffee addiction is almost as strong as some players’ gambling addiction. And after analyzing withdrawal behaviors in gambling data, I can totally relate—quitting coffee feels just as hard as hitting that “withdraw funds” button for a player on a hot streak. 😆

But jokes aside, working with user data in the gambling industry has taught me one thing:

➡️ Human behavior is rarely obvious.

We look for patterns, anomalies, and drivers of player engagement, but sometimes the biggest insights come from the unseen correlations.


🎨 Feature Engineering: The Art & Science of Data

This is where feature engineering becomes more than just a technical skill—it’s an art.

🎭 Too much? You overfit.
🎯 Too little? You miss the essence.

Identifying the right signals in user behavior is like fine-tuning a coffee recipe—balance is everything!


🤔 What Drives Gambling Behavior?

Is it:
🎰 Risk-taking?
🎯 Dopamine-seeking?
😐 Boredom?
Or a mix of all three?

Behavioral psychology & social science are just as crucial for data scientists as statistics & machine learning. Because behind every dataset, there’s a real person making choices (and probably drinking coffee while doing so).


Final Thoughts: Data, Decisions & Coffee

Let’s keep an open mind, challenge assumptions, and most importantly—never underestimate the power of a good cup of coffee.