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Data Science from Scratch 原版PDF by Grus
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Data scientist has been called “the sexiest job of the 21st century,” presumably by someone who has never visited a fire station. Nonetheless, data science is a hot and growing field, and it doesn’t take a great deal of sleuthing to find analysts breathlessly prognosticating that over the next 10 years, we’ll need billions and billions more data scientists than we currently have.
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DATADATA SCIENCE
Data Science from Scratch
ISBN: 978-1-491-90142-7
US $39.99 CAN $45.99
“
Joel takes you on a
journey from being
data-curious to getting a
thorough understanding
of the bread-and-butter
algorithms that every data
scientist should know.
”
—Rohit Sivaprasad
Data Science, Soylent
datatau.com
Twitter: @oreillymedia
facebook.com/oreilly
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 Python
■ Learn the basics of linear algebra, statistics, and probability—
and understand 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, Naive Bayes,
linear and logistic regression, decision trees, neural networks,
and clustering
■ Explore recommender systems, natural language processing,
network analysis, MapReduce, and databases
Joel Grus is a software engineer at Google. Before that, he worked as a data
scientist at multiple startups. He lives in Seattle, where he regularly attends data
science happy hours. He blogs infrequently at joelgrus.com and tweets all day
long at @joelgrus.
Joel Grus
Data Science
from Scratch
FIRST PRINCIPLES WITH PYTHON
Data Science from Scratch
Grus
Joel Grus
Data Science from Scratch
978-1-491-90142-7
[LSI]
Data Science from Scratch
by Joel Grus
Copyright © 2015 O’Reilly Media. All rights reserved.
Printed in the United States of America.
Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.
O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are
also available for most titles (http://safaribooksonline.com). For more information, contact our corporate/
institutional sales department: 800-998-9938 or corporate@oreilly.com.
Editor: Marie Beaugureau
Production Editor: Melanie Yarbrough
Copyeditor: Nan Reinhardt
Proofreader: Eileen Cohen
Indexer: Ellen Troutman-Zaig
Interior Designer: David Futato
Cover Designer: Karen Montgomery
Illustrator: Rebecca Demarest
April 2015: First Edition
Revision History for the First Edition
2015-04-10: First Release
See http://oreilly.com/catalog/errata.csp?isbn=9781491901427 for release details.
The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Data Science from Scratch, the cover
image of a Rock Ptarmigan, and related trade dress are trademarks of O’Reilly Media, Inc.
While the publisher and the author have used good faith efforts to ensure that the information and
instructions contained in this work are accurate, the publisher and the author disclaim all responsibility
for errors or omissions, including without limitation responsibility for damages resulting from the use of
or reliance on this work. Use of the information and instructions contained in this work is at your own
risk. If any code samples or other technology this work contains or describes is subject to open source
licenses or the intellectual property rights of others, it is your responsibility to ensure that your use
thereof complies with such licenses and/or rights.
Table of Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
1.
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
The Ascendance of Data 1
What Is Data Science? 1
Motivating Hypothetical: DataSciencester 2
Finding Key Connectors 3
Data Scientists You May Know 6
Salaries and Experience 8
Paid Accounts 11
Topics of Interest 11
Onward 13
2.
A Crash Course in Python. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
The Basics 15
Getting Python 15
The Zen of Python 16
Whitespace Formatting 16
Modules 17
Arithmetic 18
Functions 18
Strings 19
Exceptions 19
Lists 20
Tuples 21
Dictionaries 21
Sets 24
Control Flow 25
iii
Truthiness 25
The Not-So-Basics 26
Sorting 27
List Comprehensions 27
Generators and Iterators 28
Randomness 29
Regular Expressions 30
Object-Oriented Programming 30
Functional Tools 31
enumerate 32
zip and Argument Unpacking 33
args and kwargs 34
Welcome to DataSciencester! 35
For Further Exploration 35
3. Visualizing Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
matplotlib 37
Bar Charts 39
Line Charts 43
Scatterplots 44
For Further Exploration 47
4.
Linear Algebra. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Vectors 49
Matrices 53
For Further Exploration 55
5.
Statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Describing a Single Set of Data 57
Central Tendencies 59
Dispersion 61
Correlation 62
Simpson’s Paradox 65
Some Other Correlational Caveats 66
Correlation and Causation 67
For Further Exploration 68
6.
Probability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
Dependence and Independence 69
Conditional Probability 70
Bayes’s Theorem 72
Random Variables 73
iv | Table of Contents
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