000			aam a22 4500
999			_c114185 _d114185
003			OSt
005			20190218100922.0
008			190212b ||||| |||| 00| 0 eng d
020			_a9781786462138
040			_c
082			_a005.133 _bBOS
100			_aBoschetti, Alberto _935225
245			_aPython Data Science Essentials: Become an Eefficient Data Science Practitioner By Understanding Python's Key Concepts
250			_a2nd ed
260			_bPackt Publishing Ltd. _c2016 _aBirmingham
300			_a363p
500			_aPart I. First steps 1. Introducing data science and Python 2. Installing Python 3. Introducing Jupyter 4. Datasets and code used in the book Part II. Data munging 5. Data Munging 6. The data science process 7. Data loading and preprocessing with pandas 8. Working with categorical and text data 9. Data processing with NumPy 10. Creating NumPy arrays 11. NumPy's fast operations and computations Part III. The data pipeline 12. The Data Pipeline 13. Introducing EDA 14. Building new features 15. Dimensionality reduction 16. The detection and treatment of outliers 17. Validation metrics 18. Testing and validating 19. Cross-validation 20. Hyperparameter optimization 21. Feature selection 22. Wrapping everything in a pipeline 23. Machine Learning Part IV. Machine learning 24. Preparing tools and datasets 25. Linear and logistic regression 26. Naive Bayes 27. K-Nearest Neighbors 28. Nonlinear algorithms 29. Ensemble strategies 30. Dealing with big data 31. Approaching deep learning 32. A peek at Natural Language Processing (NLP) 33. An overview of unsupervised learning 34. Social Network Analysis Part V. Social network analysis 35. Introduction to graph theory 36. Graph algorithms 37. Graph loading, dumping, and sampling 38. Visualization, Insights, and Results Part VI. Visualization, Insights, and Results 39. Introducing the basics of matplotlib 40. Wrapping up matplotlib's commands 41. Interactive visualizations with Bokeh 42. Advanced data-learning representations
600			_aDatabases - Data Mining _935226
700			_aMassaron, Luca _935227
942			_2ddc _cLB _k005.133 _mBOS