Data Science from Scratch: First Principles with Python

Emphasizes a "from scratch" approach, where readers learn the field's foundations by manually building tools and implementing algorithms rather than relying solely on pre-existing libraries. The author transitions the curriculum to Python 3.6, introducing modern features like type annotations and f-strings to promote cleaner code. Early chapters use a hypothetical social network called DataSciencester to demonstrate practical data problems, such as finding key connectors or predicting salaries. Furthermore, the source includes a comprehensive Python crash course designed to prepare students for more advanced technical topics in statistics and machine learning. Overall, the book serves as a pedagogical guide for those with some mathematical and programming aptitude to master the first principles of data science.

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