Data science from scratch : first principles with Python Cover Image

Data science from scratch : first principles with Python / Joel Grus.

Record details

ISBN: 9781491901427
Physical Description: xvi, 311 páginas : ilustraciones ; 24 cm.
Publisher: Sebastopol, CA : O´Reilly, [2015].
Copyright: ©2015.

Content descriptions

General Note:	Incluye índice.
Immediate Source of Acquisition Note:	IPICYT ; Compra/R.3357 ; 2016.
Language Note:	En inglés.

Search for related items by subject

Subject:

Phyton (Lenguaje de progración de computadores).
Administración de bases de datos.
Estructura de datos (Ciencias de la computación).

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Location	Call Number / Copy Notes	Barcode	Shelving Location	Status	Due Date
Biblioteca Ipicyt	QA76.73.P98G7 D3 2015	LCI00970	Coleccion General	Available	-

Location

Call Number / Copy Notes

Barcode

Shelving Location

Status

Due Date

Biblioteca Ipicyt

QA76.73.P98G7 D3 2015

LCI00970

Coleccion General

Available

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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
	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	35
	Welcome to DataSciencester!	35
	For Further Exploration	35
3.	Visualizing Data	37
	matplotlib	37
	Line Charts	39
	Bar 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
	Continuous Distributions	74
	The Normal Distribution	75
	The Central Limit Theorem	78
	For Further Exploration	80
7.	Hypothesis and Inference	81
	Statistical Hypothesis Testing	81
	Example: Flipping a Coin	81
	Confidence Intervals	85
	P-hacking	86
	Example: Running an A/B Test	87
	Bayesian Inference	88
	For Further Exploration	92
8.	Gradient Descent	93
	The Idea Behind Gradient Descent	93
	Estimating the Gradient	94
	Using the Gradient	97
	Choosing the Right Step Size	97
	Putting It All Together	98
	Stochastic Gradient Descent	99
	For Further Exploration	100
9.	Getting Data	103
	stdin and stdout	103
	Reading Files	105
	The Basics of Text Files	105
	Delimited Files	106
	Scraping the Web	108
	HTML and the Parsing Thereof	108
	Example: O'Reilly Books About Data	110
	Using APIs	114
	JSON (and XML)	114
	Using an Unauthenticated API	115
	Finding APIs	116
	Example: Using the Twitter APIs	117
	Getting Credentials	117
	For Further Exploration	120
10.	Working with Data	121
	Exploring Your Data	121
	Exploring One-Dimensional Data	121
	Two Dimensions	123
	Many Dimensions	125
	Cleaning and Munging	127
	Manipulating Data	129
	Rescaling	132
	Dimensionality Reduction	134
	For Further Exploration	139
11.	Machine Learning	141
	Modeling	141
	What Is Machine Learning?	142
	Overfitting and Underfitting	142
	Correctness	145
	The Bias-Variance Trade-off	147
	Feature Extraction and Selection	148
	For Further Exploration	150
12.	k-Nearest Neighbors	151
	The Model	151
	Example: Favorite Languages	153
	The Curse of Dimensionality	156
	For Further Exploration	163
13.	Naive Bayes	165
	A Really Dumb Spam Filter	165
	A More Sophisticated Spam Filter	166
	Implementation	168
	Testing Our Model	169
	For Further Exploration	172
14.	Simple Linear Regression	173
	The Model	173
	Using Gradient Descent	176
	Maximum Likelihood Estimation	177
	For Further Exploration	177
15.	Multiple Regression	179
	The Model	179
	Further Assumptions of the Least Squares Model	180
	Fitting the Model	181
	Interpreting the Model	182
	Goodness of Fit	183
	Digression: The Bootstrap	183
	Standard Errors of Regression Coefficients	184
	Regularization	186
	For Further Exploration	188
16.	Logistic Regression	189
	The Problem	189
	The Logistic Function	192
	Applying the Model	194
	Goodness of Fit	195
	Support Vector Machines	196
	For Further Investigation	200
17.	Decision Trees	201
	What Is a Decision Tree?	201
	Entropy	203
	The Entropy of a Partition	205
	Creating a Decision Tree	206
	Putting It All Together	208
	Random Forests	211
	For Further Exploration	212
18.	Neural Networks	213
	Perceptrons	213
	Feed-Forward Neural Networks	215
	Backpropagation	218
	Example: Defeating a CAPTCHA	219
	For Further Exploration	224
19.	Clustering	225
	The Idea	225
	The Model	226
	Example: Meetups	227
	Choosing k	230
	Example: Clustering Colors	231
	Bottom-up Hierarchical Clustering	233
	For Further Exploration	238
20.	Natural Language Processing	239
	Word Clouds	239
	n-gram Models	241
	Grammars	244
	An Aside: Gibbs Sampling	246
	Topic Modeling	247
	For Further Exploration	253
21.	Network Analysis	255
	Betweenness Centrality	255
	Eigenvector Centrality	260
	Matrix Multiplication	262
	Directed Graphs and PageRank	264
	For Further Exploration	266
22.	Recommender Systems	267
	Manual Curation	268
	Recommending What's Popular	268
	User-Based Collaborative Filtering	269
	Item-Based Collaborative Filtering	272
	For Further Exploration	274
23.	Databases and SQL	275
	CREATE TABLE and INSERT	275
	UPDATE	277
	DELETE	278
	SELECT	278
	GROUP BY	280
	ORDER BY	282
	JOIN	283
	Subqueries	285
	Indexes	285
	Query Optimization	286
	NoSQL	287
	For Further Exploration	287
24.	MapReduce	289
	Example: Word Count	289
	Why MapReduce?	291
	MapReduce More Generally	292
	Example: Analyzing Status Updates	293
	Example: Matrix Multiplication	294
	An Aside: Combiners	296
	For Further Exploration	296
25.	Go Forth and Do Data Science	299
	IPython	299
	Mathematics	300
	Not from Scratch	300
	NumPy	301
	pandas	301
	scikit-learn	301
	Visualization	301
	R	302
	Find Data	302
	Do Data Science	303
	Hacker News	303
	Fire Trucks	303
	T-shirts	304
	And You?	304
	Index	305

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