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Data Science from Scratch:

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Bibliographic Details
Main Author:	Grus, Joel (Author)
Format:	Book
Language:	English
Published:	Beijing [u.a.] O'Reilly 2015
Edition:	1. ed.
Subjects:	Python > Programmiersprache Datenanalyse Data Science Python 2.7 Daten Datenmanagement Datenstruktur Data Mining Einführung
Links:	http://deposit.dnb.de/cgi-bin/dokserv?id=5158440&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027920291&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Item Description:	Auf dem Cover: "first principles with Python"
Physical Description:	XVI, 311 S. graph. Darst.
ISBN:	9781491901427 149190142X

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Record in the Search Index

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adam_text	Titel: Data science from Scratch Autor: Grus, Joel Jahr: 2015 Table of Contents Preface...................................................... 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......................................... Dependence and Independence 69 Conditional Probability 70 Bayess Theorem 72 Random Variables 73 iv j Table of Contents 10 . Continuous Distributions 74 The Normal Distribution 75 The Central Limit Theorem 78 For Further Exploration 80 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 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 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 Working with Data....................................... ................. 121 Exploring Your Data 121 Exploring One-Dimensional Data 121 Table of Contents \| v Two Dimensions Many Dimensions ^5 Cleaning and Munging 127 Manipulating Data 129 Rescaling 1^2 Dimensionality Reduction 134 For Further Exploration 139 11. Machine Learning......................................................... Ml 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 vi I Table of Contents 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 Table of Contents \| vii An Aside: Gibbs Sampling 246 Topic Modeling 247 For Further Exploration 253 21. Network Analysis..........................................................255 Betweenness Centrality 255 Eigenvector Centrality 260 Matrix Multiplication 260 Centrality 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 viii \| Table of Contents 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 Table of Contents \| ix
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genre	1\p (DE-588)4151278-9 Einführung gnd-content
genre_facet	Einführung
id	DE-604.BV042485395
illustrated	Illustrated
indexdate	2024-12-20T17:12:36Z
institution	BVB
isbn	9781491901427 149190142X
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-027920291
oclc_num	912307506
open_access_boolean
owner	DE-706 DE-11 DE-739 DE-92 DE-523 DE-Re13 DE-BY-UBR DE-M382 DE-573 DE-83 DE-B768 DE-19 DE-BY-UBM DE-1049 DE-703
owner_facet	DE-706 DE-11 DE-739 DE-92 DE-523 DE-Re13 DE-BY-UBR DE-M382 DE-573 DE-83 DE-B768 DE-19 DE-BY-UBM DE-1049 DE-703
physical	XVI, 311 S. graph. Darst.
publishDate	2015
publishDateSearch	2015
publishDateSort	2015
publisher	O'Reilly
record_format	marc
spellingShingle	Grus, Joel Data Science from Scratch Python Programmiersprache (DE-588)4434275-5 gnd Datenanalyse (DE-588)4123037-1 gnd Data Science (DE-588)1140936166 gnd Python 2.7 (DE-588)7741095-6 gnd Daten (DE-588)4135391-2 gnd Datenmanagement (DE-588)4213132-7 gnd Datenstruktur (DE-588)4011146-5 gnd Data Mining (DE-588)4428654-5 gnd
subject_GND	(DE-588)4434275-5 (DE-588)4123037-1 (DE-588)1140936166 (DE-588)7741095-6 (DE-588)4135391-2 (DE-588)4213132-7 (DE-588)4011146-5 (DE-588)4428654-5 (DE-588)4151278-9
title	Data Science from Scratch
title_auth	Data Science from Scratch
title_exact_search	Data Science from Scratch
title_full	Data Science from Scratch Joel Grus
title_fullStr	Data Science from Scratch Joel Grus
title_full_unstemmed	Data Science from Scratch Joel Grus
title_short	Data Science from Scratch
title_sort	data science from scratch
topic	Python Programmiersprache (DE-588)4434275-5 gnd Datenanalyse (DE-588)4123037-1 gnd Data Science (DE-588)1140936166 gnd Python 2.7 (DE-588)7741095-6 gnd Daten (DE-588)4135391-2 gnd Datenmanagement (DE-588)4213132-7 gnd Datenstruktur (DE-588)4011146-5 gnd Data Mining (DE-588)4428654-5 gnd
topic_facet	Python Programmiersprache Datenanalyse Data Science Python 2.7 Daten Datenmanagement Datenstruktur Data Mining Einführung
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work_keys_str_mv	AT grusjoel datasciencefromscratch

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