The lesson? You can know every Python library, but if you can't connect dots between data and dollars, you're just an expensive calculator. What's the biggest gap you've noticed between courses and real jobs?.

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🔸 "Courses teach you to find patterns. Businesses need you to find profits". The gap: Most curricula miss how to translate technical insights into business language, calculate financial impact, and build stakeholder trust through clear communication.

Then reality hit on our first consulting project. "Knowing how to code doesn't mean you know how to solve business problems". The questions I wasn't prepared for: .➜ Why should we trust this model over our current process? .➜ How much revenue will this recommendation.

I spent 6 months learning Python, SQL and Tableau. Then my first client asked: "How will this help me sell more products?". This is why 90% of data science courses don't prepare you for real business problems. 🧵 Thread below ⬇️.

The lesson? .Your model might be mathematically perfect, but if it can't handle real-world messiness, it's financially dangerous. What's one outlier that taught you an expensive lesson?.🔄 RT to help others avoid the outlier trap.

The breakthrough: .When I visualized the data, that one extreme point was pulling our entire regression line toward fantasy land. What I learned: .🔸 Always visualize before you model .🔸 Apply the 3-standard-deviation rule systematically .🔸 Investigate extreme values, don't.

Here's what happened: .➜ The outlier skewed our spending predictions upward .➜ Our model recommended expensive campaigns for average customers.➜ We nearly launched a $50K campaign for someone who typically spends $200 .➜ The 3-standard-deviation rule would have caught this.

🔸 "Perfect training accuracy means nothing if one extreme value breaks your business logic". One customer's $45K accidental purchase was teaching our model that everyone should get premium treatment.

My team and I were analyzing customer lifetime value for an e-commerce client. Everything looked perfect - clean data, solid features, impressive test scores. Then we deployed it and experienced our first major model failure. .

The model was 94% accurate in testing. Then it recommended we spend $50K on a customer who bought one $45,000 luxury watch by mistake. This is why outliers will bankrupt your business faster than bad data. 🧵 Thread below ⬇️.

Hey #DataScience & #Product fam! 👋.As a Data Scientist in Product & Marketing Analytics, I'm exploring how AI & Data Automation can solve key business problems. What's the most critical problem you're seeing in businesses right now that's ripe for a data-driven solution?. Vote.

Hey #DataScience & #Product fam! 👋.As a Data Scientist in Product & Marketing Analytics, I'm exploring how AI & Data Automation can solve key business problems. What's the most critical problem you're seeing in businesses right now that's ripe for a data-driven solution?. Vote.

The real lesson? .Your model might be 86% accurate, but if it's missing your most valuable prospects, you're still losing money. What's one metric that looked great on paper but didn't work in reality?. Want the full breakdown?. 🔄 RT to help others.

My recommendations: .🔸 Stop calling everyone. Start calling smart. 🔸 Highlight premium categories in your pitch. 🔸 Target households with higher per-person income. 🔸 Re-engage dormant customers with "welcome back" messaging.

The breakthrough moment: .Customers who hadn't purchased recently were MORE likely to respond. ➜ They're not overwhelmed by your brand .➜ They're prime for a "we miss you" approach .➜ They represent untapped potential, not lost causes.