Improving Data Science Skills: Extracting Data and Analyzing Normal Distribution
The article discusses the author's progress in improving their data science skills, focusing on using CSS locators to extract data from websites and exploring normal distribution using Python.
Why it matters
The article demonstrates the author's dedication to improving their data science skills, highlighting their hands-on approach to learning new techniques and applying them to real-world scenarios.
Key Points
- 1Practiced using CSS locators to pinpoint and extract specific data from websites
- 2Explored normal distribution and used tools like scipy.stats.norm(), mean, and standard deviation to understand real-life randomness
- 3Solved business-like questions related to sales deals and probabilities
- 4Shared visuals to demonstrate the techniques learned
Details
The author describes their day of practicing data extraction using CSS locators, which allowed them to precisely target and retrieve specific data from websites. They also explored normal distribution in Python, using tools like scipy.stats.norm() to visualize and understand real-life randomness in measurable ways. The author solved business-related questions, such as estimating the number of sales deals Amir can expect per week and the least number of deals he is likely to close in the bottom 25% of weeks. The article includes four visuals that capture the author's learning process, including an HTML tree showing CSS selector navigation, a highlighted CSS selector, a normal distribution curve, and Python code calculating probabilities.
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