Data science from scratch: first principles with Python
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Bejing ; Boston ; Farnham ; Sebastopol ; Tokyo
O'Reilly
May 2019
|
| Ausgabe: | Second edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | xvii, 384 Seiten Illustrationen, Diagramme |
| ISBN: | 9781492041139 |
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Datensatz im Suchindex
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Table of Contents Preface to the Second Edition. xi Preface to the First Edition. xv 1. Introduction. 1 The Ascendance of Data What Is Data Science? Motivating Hypothetical: DataSciencester Finding Key Connectors Data Scientists You May Know Salaries and Experience Paid Accounts Topics of Interest Onward 1 1 2 3 6 8 11 11 13 2. A Crash Course in Python. 15 The Zen of Python Getting Python Virtual Environments Whitespace Formatting Modules Functions Strings Exceptions Lists Tuples Dictionaries defaultdict 15 16 16 17 19 20 21 21 22 23 24 25 iii
Counters Sets Control Flow Truthiness Sorting List Comprehensions Automated Testing and assert Object-Oriented Programming Iterables and Generators Randomness Regular Expressions Functional Programming zip and Argument Unpacking args and kwargs Type Annotations How to Write Type Annotations Welcome to DataSciencester! For Further Exploration 26 26 27 28 29 30 30 31 33 35 36 36 36 37 38 41 42 42 3. Visualizing Data. 43 matplotlib Bar Charts Line Charts Scatterplots For Further Exploration 43 45 49 50 52 4. Linear Algebra. 55 Vectors Matrices For Further Exploration 55 59 62 5. Statistics. 63 Describing a Single Set of Data Central Tendencies Dispersion Correlation Simpson’s Paradox Some Other CorrelationalCaveats Correlation and Causation For Further Exploration iv j Table of Contents 63 65 67 68 71 72 73 73
6. Probability. 75 Dependence and Independence Conditional Probability Bayes’s Theorem Random Variables Continuous Distributions The Normal Distribution The Central Limit Theorem For Further Exploration 75 76 78 79 80 81 84 86 7. Hypothesis and Inference. 87 Statistical Hypothesis Testing Example: Flipping a Coin p-Values Confidence Intervals p-Hacking Example: Running an A/В Test Bayesian Inference For Further Exploration 87 87 90 92 93 94 95 99 8. Gradient Descent. 101 The Idea Behind Gradient Descent Estimating the Gradient Using the Gradient Choosing the Right Step Size Using Gradient Descent to Fit Models Minibatch and Stochastic Gradient Descent For Further Exploration 101 102 105 106 106 108 109 9. Getting Data. 111 111 113 113 115 116 117 118 121 121 122 123 124 stdin and stdout Reading Files The Basics of Text Files Delimited Files Scraping the Web HTML and the Parsing Thereof Example: Keeping Tabs on Congress Using APIs JSONandXML Using an Unauthenticated API Finding APIs Example: Using the Twitter APIs Table of Contents | v
Getting Credentials For Further Exploration 124 128 10. Working with Data. 129 Exploring Your Data Exploring One-Dimensional Data Two Dimensions Many Dimensions Using NamedTuples Dataclasses Cleaning and Munging Manipulating Data Rescaling An Aside: tqdm Dimensionality Reduction For Further Exploration 11. Machine Learning. . Modeling What Is Machine Learning? Overfitting and Underfitting Correctness The Bias-Variance Tradeoff Feature Extraction and Selection For Further Exploration 129 129 131 133 135 137 138 140 142 144 146 151 153 153 154 155 157 160 161 163 12. k-Nearest Neighbors. . 165 The Model Example: The Iris Dataset The Curse of Dimensionality For Further Exploration 13. Naive Bayes. A Really Dumb Spam Filter A More Sophisticated Spam Filter Implementation Testing Our Model Using Our Model For Further Exploration 165 167 170 174 175 175 176 178 180 181 183 14. Simple Linear Regression. . 185 The Model vi I Table of Contents 185
189 190 190 Using Gradient Descent Maximum Likelihood Estimation For Further Exploration 15. Multiple Regression. 191 The Model Further Assumptions of the Least Squares Model Fitting the Model Interpreting the Model Goodness of Fit Digression: The Bootstrap Standard Errors of Regression Coefficients Regularization For Further Exploration 191 192 193 195 196 196 198 200 202 16. Logistic Regression. 203 The Problem The Logistic Function Applying the Model Goodness of Fit Support Vector Machines For Further Investigation 203 206 208 209 210 214 17. Decision Trees. What Is a Decision Tree? Entropy The Entropy of a Partition Creating a Decision Tree Putting It All Together Random Forests For Further Exploration 215 215 217 219 220 223 225 226 18. Neural Networks. 227 227 230 233 235 238 Perceptrons Feed-Forward Neural Networks Backpropagation Example: Fizz Buzz For Further Exploration 19. DeepLearning. 239 The Tensor The Layer Abstraction 239 242 Table of Contents | vii
The Linear Layer Neural Networks as a Sequence of Layers Loss and Optimization Example: XOR Revisited Other Activation Functions Example: FizzBuzz Revisited Softmaxes and Cross-Entropy Dropout Example: MNIST Saving and Loading Models For Further Exploration 243 246 247 250 251 252 253 256 257 261 262 20. Clustering. 263 The Idea The Model Example: Meetups Choosing к Example: Clustering Colors Bottom-Up Hierarchical Clustering For Further Exploration 21. Natural Language Processing. Word Clouds n-Gram Language Models Grammars An Aside: Gibbs Sampling Topic Modeling Word Vectors Recurrent Neural Networks Example: Using a Character-Level RNN For Further Exploration 263 264 266 268 269 271 277 279 279 281 284 286 288 293 301 304 308 22. Network Analysis. 309 Betweenness Centrality Eigenvector Centrality Matrix Multiplication Centrality D irected Graphs and PageRank For Further Exploration 309 314 314 316 318 320 23. Recommender Systems. 321 Manual Curation viii I Table of Contents 322
Recommending Whaťs Popular User-Based Collaborative Filtering Item-Based Collaborative Filtering Matrix Factorization For Further Exploration 322 323 326 328 333 24. Databases and SQL. CREATE TABLE and INSERT UPDATE DELETE SELECT GROUP BY ORDER BY JOIN Subqueries Indexes Query Optimization NoSQL For Further Exploration 335 335 338 339 340 342 345 346 348 349 349 350 350 25. MapReduce. 351 Example: Word Count Why MapReduce? MapReduce More Generally Example: Analyzing Status Updates Example: Matrix Multiplication An Aside: Combiners For Further Exploration 352 353 354 355 357 359 359 26. Data Ethics. 361 What Is Data Ethics? No, Really, What Is Data Ethics? Should I Care About Data Ethics? Building Bad Data Products Trading Off Accuracy and Fairness Collaboration Interpretability Recommendations Biased Data Data Protection In Summary For Further Exploration 361 362 362 363 364 365 365 366 367 368 368 369 labie of Contents | ix
27. Go Forth and Do Data Science. . 371 IPython Mathematics Not from Scratch NumPy pandas scikit-learn Visualization R Deep Learning Find Data Do Data Science Hacker News Fire Trucks T-Shirts Tweets on a Globe And You? 371 372 372 372 372 373 373 373 374 374 375 375 375 375 376 376 Index. . 377 x I Table of Contents
O'REILLY’ Data Science from Scratch 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 к-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 research engineer at the Allen Institute for Artificial Intelligence. Previously he worked as a software engineer at Google and as a data scientist at several 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. |
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| genre | 1\p (DE-588)4151278-9 Einführung gnd-content |
| genre_facet | Einführung |
| id | DE-604.BV045902086 |
| illustrated | Illustrated |
| indexdate | 2025-04-01T08:01:21Z |
| institution | BVB |
| isbn | 9781492041139 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031284875 |
| oclc_num | 1103913575 |
| open_access_boolean | |
| owner | DE-29T DE-573 DE-703 DE-824 DE-523 DE-N2 DE-83 DE-B768 DE-573n DE-634 DE-355 DE-BY-UBR DE-384 DE-898 DE-BY-UBR |
| owner_facet | DE-29T DE-573 DE-703 DE-824 DE-523 DE-N2 DE-83 DE-B768 DE-573n DE-634 DE-355 DE-BY-UBR DE-384 DE-898 DE-BY-UBR |
| physical | xvii, 384 Seiten Illustrationen, Diagramme |
| psigel | BSB_NED_20190604 |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | O'Reilly |
| record_format | marc |
| spelling | Grus, Joel Verfasser (DE-588)1098174119 aut Data science from scratch first principles with Python Joel Grus Second edition Bejing ; Boston ; Farnham ; Sebastopol ; Tokyo O'Reilly May 2019 xvii, 384 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Datenanalyse (DE-588)4123037-1 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Daten (DE-588)4135391-2 gnd rswk-swf Python 2.7 (DE-588)7741095-6 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Datenstruktur (DE-588)4011146-5 gnd rswk-swf Data Science (DE-588)1140936166 gnd rswk-swf Datenmanagement (DE-588)4213132-7 gnd rswk-swf 1\p (DE-588)4151278-9 Einführung gnd-content Data Science (DE-588)1140936166 s Python Programmiersprache (DE-588)4434275-5 s DE-604 Daten (DE-588)4135391-2 s Datenmanagement (DE-588)4213132-7 s Datenstruktur (DE-588)4011146-5 s Data Mining (DE-588)4428654-5 s Python 2.7 (DE-588)7741095-6 s 2\p DE-604 3\p DE-604 Datenanalyse (DE-588)4123037-1 s 4\p DE-604 5\p DE-604 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031284875&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031284875&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 4\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 5\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Grus, Joel Data science from scratch first principles with Python Datenanalyse (DE-588)4123037-1 gnd Data Mining (DE-588)4428654-5 gnd Daten (DE-588)4135391-2 gnd Python 2.7 (DE-588)7741095-6 gnd Python Programmiersprache (DE-588)4434275-5 gnd Datenstruktur (DE-588)4011146-5 gnd Data Science (DE-588)1140936166 gnd Datenmanagement (DE-588)4213132-7 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4428654-5 (DE-588)4135391-2 (DE-588)7741095-6 (DE-588)4434275-5 (DE-588)4011146-5 (DE-588)1140936166 (DE-588)4213132-7 (DE-588)4151278-9 |
| title | Data science from scratch first principles with Python |
| title_auth | Data science from scratch first principles with Python |
| title_exact_search | Data science from scratch first principles with Python |
| title_full | Data science from scratch first principles with Python Joel Grus |
| title_fullStr | Data science from scratch first principles with Python Joel Grus |
| title_full_unstemmed | Data science from scratch first principles with Python Joel Grus |
| title_short | Data science from scratch |
| title_sort | data science from scratch first principles with python |
| title_sub | first principles with Python |
| topic | Datenanalyse (DE-588)4123037-1 gnd Data Mining (DE-588)4428654-5 gnd Daten (DE-588)4135391-2 gnd Python 2.7 (DE-588)7741095-6 gnd Python Programmiersprache (DE-588)4434275-5 gnd Datenstruktur (DE-588)4011146-5 gnd Data Science (DE-588)1140936166 gnd Datenmanagement (DE-588)4213132-7 gnd |
| topic_facet | Datenanalyse Data Mining Daten Python 2.7 Python Programmiersprache Datenstruktur Data Science Datenmanagement Einführung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031284875&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031284875&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT grusjoel datasciencefromscratchfirstprincipleswithpython |