Today, I practiced using CSS locators to extract data from websites, not just clicking around, but pinpointing exactly what I want from a forest of HTML tags.

I practiced using classes and IDs to extract contents from Selectors.

It felt like telling a website: "Look, I know you’re busy, but show me exactly what I asked for." And it listened. Beautiful !

I also looked into Normal distribution using Python and explored business-like questions such as: How many sales deals can Amir expect per week? What’s the least number of deals Amir is likely to close in the bottom 25% of weeks?

I used tools like: scipy.stats.norm(), mean and standard deviation, to visualize and understand real-life randomness in measurable ways.

If I Had to Show You Exactly What I Did Today… Here are 4 visuals that capture my day: A website’s HTML tree showing how CSS selectors navigate the structure

A highlighted CSS selector pinpointing one tiny piece of data

A Normal distribution curve for sales deals

Python code calculating probabilities

Together, these visuals show what I actually do on this journey.

Every dataset tells a story. Every selector reveals a clue. Every distribution explains a pattern. And I’m enjoying the process way more than I expected.

Still learning. Still building. Still curious. And still very much in love with the journey.

-SP