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Day 20 of improving my Data Science skills

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Scraping with XPath notation

It's been a while since my last update, but I haven't stopped learning and I am excited to continue sharing everything I have been learning. Now, let's catch up !



On one hand, I was learning how to navigate messy HTML structures with XPath… On the other, I was breaking down probability distributions in Python.



Somehow, both taught me the same lesson: The world belongs to people who know how to find patterns, whether in code, data, or business decisions.



I learned how to write XPath expressions that actually target the exact data I want, including:



✔️ Forward slash (/) : This selects elements step-by-step through the HTML tree.

✔️ Double slash (//) : Jumps directly to elements no matter where they are and are perfect for chaotic websites.

✔️ Attributes: This targets elements with precision using HTML attributes like class, id, hrefs...



And I practiced combining them to extract data.

This is the kind of skill that powers product research, competitor analysis, data engineering, automation, and business intelligence.



In a bid to understand discrete probability, I also practiced how to calculate the probability of events like:



The odds of a certain group size in a dataset



Summing probabilities to estimate the chance of a group ≥ 4



Understanding why we divide counts by total rows



I got to understand one thing, whether I am predicting customer behavior, risk levels, market shifts, or user preferences, discrete probability is the foundation.



Why do I need to learn these skills as a Data scientist?

Both skills, XPath and Probability, sharpen my ability to:



Extract clean data from messy environments



Build trustworthy datasets for analysis



Understand uncertainty and risk



Make decisions based on patterns, not intuition



Support strategies with evidence



Build smarter products powered by real data



Every new concept I learn, whether scraping a website or probability, is another step toward becoming the kind of professional who can turn raw information into insight.



Because data isn't just numbers. It's stories, opportunities, and decisions waiting to be made.



-SP

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