Every powerful AI, startup, or product you admire today is secretly powered by one thing: data… lots of it.
Today, I officially began learning how to scrape websites to extract raw data from new sources, alongside my Introduction to Statistics in Python, and suddenly, everything started to connect.
Here is what I worked on today:
✅ Practiced how to detect outliers using the IQR method
✅ Learned how to calculate probabilities in Python
✅ Started my journey into Web Scraping
I realized something important:
Statistics help me understand data, but web scraping helps me get the data in the first place.
So… what is web scraping really used for?
It's how:
Startups collect market data automatically
Recruiters track job trends across platforms
Businesses monitor competitors' prices & products
Analysts gather real-time data for insights & predictions
AI systems are fed with massive real-world datasets
In simple terms: Web scraping is how raw information on the internet becomes usable data for decisions, products, and AI.
While statistics today taught me how to spot unusual values (outliers) and measure uncertainty with probability, web scraping showed me where the data itself comes from.
One thing is clear: Data science is not just about models, it is about how data is collected, cleaned, understood, and finally used to create impact.
Some days I feel overwhelmed, other days I feel unstoppable, but every day I am curious. I genuinely enjoy discovering how data explains the world around us, and I am excited to keep learning, experimenting,and sharing this journey openly.
To my followers, thank you for sticking with me😊
Auf wiedersehen!
-SP🤍
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