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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...
Enregistré dans:
| Egalement en ligne : |
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 |
- P. XV
- Preface
- P. XIX
- Acknowledgments
- P. 1
- 1. Getting started
- P. 11
- 2. Introduction to Python
- P. 45
- 3. Some simple numerical programs
- P. 63
- 4. Functions, scoping, and abstraction
- P. 89
- 5. Structured types and mutability
- P. 123
- 6. Recursion and global variables
- P. 135
- 7. Modules and files
- P. 147
- 8. Testing and debugging
- P. 167
- 9. Exceptions and assertions
- P. 177
- 10. Classes and object-orientedprogramming
- P. 213
- 11. A simplistic introduction to algorithmic complexity
- P. 233
- 12. Some simple algorithms and data structures
- P. 257
- 13. Plotting and more about classes
- P. 281
- 14. Knapsack and graph optimization problems
- P. 305
- 15. Dynamic programming
- P. 321
- 16. Random walks and more about Data visualization
- P. 341
- 17. Stochastic programs, probability, and distribution
- P. 393
- 18. Monte Carlo simulation
- P. 413
- 19. Sampling and confidence
- P. 431
- 20. Understanding experimental Data
- P. 457
- 21. Randomized trials and hypothesis checking
- P. 487
- 22. Lies, damned lies, and statistics
- P. 511
- 23. Exploring Data with Pandas
- P. 545
- 24. A quick look at machine learning
- P. 561
- 25. Clustering
- P. 585
- 26. Classification methods
- P. 619
- Python 3.8 quick reference
- P. 623
- Index