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Data Science from Scratch: First Principles…
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Data Science from Scratch: First Principles with Python

by Joel Grus

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672178,380 (3.5)None
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    Doing Data Science: Straight Talk from the Frontline by Cathy O'Neil (ccatalfo)
    ccatalfo: In a similar vein this book talks about the practice of data science from a down to earth perspective that newcomers to the field will feel welcomed by.
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Ambitious, but uneven, made me think of the 'how to draw an owl' meme at part. The most interesting aspect might have been the author's functional Python. The "For Further Exploration" sections have some really interesting links. ( )
  encephalical | Feb 9, 2017 |
This is a very basic into topics in statistics and machine learning built around functioning code to perform (some of!) the tasks and algorithms discussed.

As an introduction it seemed very solid. I was looking for something a little more in depth, so this was not really the book I was looking for. What am I looking for? Something that bridges between a working knowledge of e.g. some methods in scikit learn to e.g. coding those methods, from scratch. Gradient descent and PCA are covered, but the book stops precisely at 'more interesting'/complex methods e.g. ridge regression/Lasso, and never even touches on e.g. ICA.

So, 3-ish stars for me. Maybe 4 stars if you are getting your feet wet for the first time. ( )
  dcunning11235 | Oct 17, 2016 |
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Amazon.com Product Description (ISBN 149190142X, Paperback)

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science 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 hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.

Get a crash course in PythonLearn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data scienceCollect, explore, clean, munge, and manipulate dataDive into the fundamentals of machine learningImplement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clusteringExplore recommender systems, natural language processing, network analysis, MapReduce, and databases

(retrieved from Amazon Tue, 07 Jul 2015 21:31:18 -0400)

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. In this book, you'll learn how many of the most fundamental data science 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 hacking skills you need to get started as a data scientist. Today's messy glut of data holds answers to questions no one's even thought to ask. This book provides you with the know-how to dig those answers out.… (more)

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