6 Practical Things I Learned Today
Today, my learning sat at the intersection of Web Scraping and Statistics, and surprisingly… both answered the same real-world question: "How do you make decisions when you don't have all the data?"
Here's how that played out, in ways founders, recruiters, analysts, and builders can relate to.
1️⃣ You don't need permission to access public information
With Python Response objects, I learned how websites send back data (HTML, headers, status codes).
Real life: This is like opening a public report instead of waiting for someone to email it to you.
2️⃣The URL tells a story
From the response object, you can extract the exact URL that returned the data.
Real life: Knowing where data came from matters as much as the data itself (think source credibility).
3️⃣You don't need data from everyone to make decisions
This is where Central Limit Theorem (CLT) hit me.
You can take small samples and still understand the bigger picture.
Real life: You don't interview every customer, you sample and learn.
4️⃣Averages become trustworthy with enough samples
CLT says: if you sample enough times, the distribution of averages becomes normal.
Real life: One review can lie. 1,000 reviews don't.
5️⃣Variability explains risk
Understanding standard deviation helped me see how unstable outcomes can be.
Real life: Two businesses can earn the same revenue, but one is far riskier.
6️⃣Scraping + statistics = insight you can act on
Scraping gets raw data. Statistics turns it into understanding.
Real life: DATA without ANALYSIS is noise. ANALYSIS without DATA is guessing.
I didn't just "learn syntax" today. I learned how people make sense of incomplete information, and that skill travels across industries.
If you're building, hiring, or deciding, this is the quiet engine behind it all.
Still learning. Still curious. Still showing up.
-SP
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