Python Data Science Essentials: Become an Eefficient Data Science Practitioner By Understanding Python's Key Concepts (Record no. 114185)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | aam a22 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20190218100922.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 190212b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781786462138 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.133 |
| Item number | BOS |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Boschetti, Alberto |
| 9 (RLIN) | 35225 |
| 245 ## - TITLE STATEMENT | |
| Title | Python Data Science Essentials: Become an Eefficient Data Science Practitioner By Understanding Python's Key Concepts |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 2nd ed |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc. | Packt Publishing Ltd. |
| Date of publication, distribution, etc. | 2016 |
| Place of publication, distribution, etc. | Birmingham |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 363p |
| 500 ## - GENERAL NOTE | |
| General note | Part I. First steps<br/>1. Introducing data science and Python<br/>2. Installing Python<br/>3. Introducing Jupyter<br/>4. Datasets and code used in the book<br/><br/>Part II. Data munging<br/>5. Data Munging<br/>6. The data science process<br/>7. Data loading and preprocessing with pandas<br/>8. Working with categorical and text data<br/>9. Data processing with NumPy<br/>10. Creating NumPy arrays<br/>11. NumPy's fast operations and computations<br/><br/>Part III. The data pipeline<br/>12. The Data Pipeline<br/>13. Introducing EDA<br/>14. Building new features<br/>15. Dimensionality reduction<br/>16. The detection and treatment of outliers<br/>17. Validation metrics<br/>18. Testing and validating<br/>19. Cross-validation<br/>20. Hyperparameter optimization<br/>21. Feature selection<br/>22. Wrapping everything in a pipeline<br/>23. Machine Learning<br/><br/>Part IV. Machine learning<br/>24. Preparing tools and datasets<br/>25. Linear and logistic regression<br/>26. Naive Bayes<br/>27. K-Nearest Neighbors<br/>28. Nonlinear algorithms<br/>29. Ensemble strategies<br/>30. Dealing with big data<br/>31. Approaching deep learning<br/>32. A peek at Natural Language Processing (NLP)<br/>33. An overview of unsupervised learning<br/>34. Social Network Analysis<br/><br/>Part V. Social network analysis<br/>35. Introduction to graph theory<br/>36. Graph algorithms<br/>37. Graph loading, dumping, and sampling<br/>38. Visualization, Insights, and Results<br/><br/>Part VI. Visualization, Insights, and Results<br/>39. Introducing the basics of matplotlib<br/>40. Wrapping up matplotlib's commands<br/>41. Interactive visualizations with Bokeh<br/>42. Advanced data-learning representations |
| 600 ## - SUBJECT ADDED ENTRY--PERSONAL NAME | |
| Personal name | Databases - Data Mining |
| 9 (RLIN) | 35226 |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Massaron, Luca |
| 9 (RLIN) | 35227 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Book |
| Call number prefix | 005.133 |
| Call number suffix | BOS |
No items available.