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Data Science from Scratch: First Principles with Python

Joel Grus · 4 HN comments
HN Books has aggregated all Hacker News stories and comments that mention "Data Science from Scratch: First Principles with Python" by Joel Grus.
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Amazon Summary
To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with New material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and how and when they’re used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases. .
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Hacker News Stories and Comments

All the comments and stories posted to Hacker News that reference this book.
No answers. Any recommendations on books or blogs to learn data science?

I working through this book, which I do like:

https://www.amazon.com/Data-Science-Scratch-Principles-Pytho...

I’m rewriting the examples in Swift to help me learn:

https://github.com/melling/data-science-from-scratch-swift

Something with a little more theory might be good. Lots of the questions seem to require more theoretical knowledge.

alexey-olx
Try machine learning bookcamp: http://bit.ly/mlbookcamp Learning machine learning by doing projects
alexanderchr
I can really recommend Introduction to statistical learning by James, Witten, Hastie and Tibshirani if you are looking for something that covers the theory without going into too much detail.

There is also Elements of statistical learning by the same authors if you are looking for something more rigorous. I haven’t read very much of it but it is supposed to very good too.

Data science seems like a gateway drug to doing more math.

I’ve been working through Joel Grus’ Data Science from Scratch,

https://www.amazon.com/Data-Science-Scratch-Principles-Pytho...

rewriting the Python examples in Swift:

https://github.com/melling/data-science-from-scratch-swift

Feb 16, 2020 · melling on Math Keeps Changing
With Data Science and Machine Learning, my interest in math has greatly increased too. There’s definitely more of a need.

I’m working my way through this book:

https://www.amazon.com/Data-Science-Scratch-Principles-Pytho...

by rewriting the examples in Swift:

https://github.com/melling/data-science-from-scratch-swift

I do like the author’s idea of creating his Simple Statistics library:

https://macwright.org/2012/06/26/simple-statistics.html

You definitely learn much more by building something, even if it’s less than perfect.

May 09, 2019 · HuShifang on Build a Neural Network
A new edition of Grus comes out next week actually...

https://www.amazon.com/Data-Science-Scratch-Principles-Pytho...

quantumOctopus
Ugh I just bought the old one a week ago.
Anon84
Nice! He mentioned he was working on it when I met him at Strata last year, but I didn't know it was coming out already.
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