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Introduction to computation and programming using Python : with application to computational modeling and understanding data
"Based on an MIT massive open online course (MOOC), this book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including numpy, matplotib, random, pandas, and sklearn. This third edition has...
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En ligne Via Introduction to computation and programming using Python
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|---|---|
| Auteur principal : | |
| Format : | Livre |
| Langue : | anglais |
| Titre complet : | Introduction to computation and programming using Python : with application to computational modeling and understanding data / John V. Guttag |
| Édition : | 3rd edition |
| Publié : |
Cambridge (Mass.), London :
The MIT Press
, C 2021 |
| Description matérielle : | 1 vol. (XVIII-637 p.) |
| Sujets : | |
| Documents associés : | Autre format:
Introduction to computation and programming using Python |
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| 200 | 1 | |a Introduction to computation and programming using Python |e with application to computational modeling and understanding data |f John V. Guttag | |
| 205 | |a 3rd edition | ||
| 214 | 0 | |a Cambridge (Mass.) |a London |c The MIT Press | |
| 214 | 4 | |d C 2021 | |
| 215 | |a 1 vol. (XVIII-637 p.) |c tabl., graph. |d 23 cm | ||
| 320 | |a Notes bibliogr. Index | ||
| 330 | |a "Based on an MIT massive open online course (MOOC), this book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including numpy, matplotib, random, pandas, and sklearn. This third edition has expanded the initial explanatory material, making it a gentler introduction to programming for the beginner, with more programming examples and many more finger exercises. A new chapter shows how to use the Pandas package for analyzing time series data. All the code has been rewritten to make it stylistically consistent with the PEP 8 standards. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. The book also includes a Python 3 quick reference guide." |2 4ème de couverture | ||
| 359 | 2 | |p P. XV |b Preface |p P. XIX |b Acknowledgments |p P. 1 |b 1. Getting started |p P. 11 |b 2. Introduction to Python |p P. 45 |b 3. Some simple numerical programs |p P. 63 |b 4. Functions, scoping, and abstraction |p P. 89 |b 5. Structured types and mutability |p P. 123 |b 6. Recursion and global variables |p P. 135 |b 7. Modules and files |p P. 147 |b 8. Testing and debugging |p P. 167 |b 9. Exceptions and assertions |p P. 177 |b 10. Classes and object-orientedprogramming |p P. 213 |b 11. A simplistic introduction to algorithmic complexity |p P. 233 |b 12. Some simple algorithms and data structures |p P. 257 |b 13. Plotting and more about classes |p P. 281 |b 14. Knapsack and graph optimization problems |p P. 305 |b 15. Dynamic programming |p P. 321 |b 16. Random walks and more about Data visualization |p P. 341 |b 17. Stochastic programs, probability, and distribution |p P. 393 |b 18. Monte Carlo simulation |p P. 413 |b 19. Sampling and confidence |p P. 431 |b 20. Understanding experimental Data |p P. 457 |b 21. Randomized trials and hypothesis checking |p P. 487 |b 22. Lies, damned lies, and statistics |p P. 511 |b 23. Exploring Data with Pandas |p P. 545 |b 24. A quick look at machine learning |p P. 561 |b 25. Clustering |p P. 585 |b 26. Classification methods |p P. 619 |b Python 3.8 quick reference |p P. 623 |b Index | |
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