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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.
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/1492041130?&linkCode=ll2&tag=cvthunderx-20&linkId=6a80cfa01a1dfc03e0f6ac710a0a670b&language=en_US&ref_=as_li_ss_tl
Discover our free courses in tech and cybersecurity, Start learning today:
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You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/1492041130?&linkCode=ll2&tag=cvthunderx-20&linkId=6a80cfa01a1dfc03e0f6ac710a0a670b&language=en_US&ref_=as_li_ss_tl
Discover our free courses in tech and cybersecurity, Start learning today:
https://linktr.ee/cybercode_academy
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By CyberSecurity Summary
2.2
55 ratings
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.
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/1492041130?&linkCode=ll2&tag=cvthunderx-20&linkId=6a80cfa01a1dfc03e0f6ac710a0a670b&language=en_US&ref_=as_li_ss_tl
Discover our free courses in tech and cybersecurity, Start learning today:
https://linktr.ee/cybercode_academy
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/1492041130?&linkCode=ll2&tag=cvthunderx-20&linkId=6a80cfa01a1dfc03e0f6ac710a0a670b&language=en_US&ref_=as_li_ss_tl
Discover our free courses in tech and cybersecurity, Start learning today:
https://linktr.ee/cybercode_academy
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