Python data science essentials : a practitioner's guide covering essential data science principles, tools, and techniques / Alberto Boschetti and Luca Massaron
Material type:
- 9781789537864
- 005.133 BOS-A
Item type | Current library | Collection | Shelving location | Call number | Status | Date due | Barcode | Item holds | |
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BITS Pilani Hyderabad | 003-007 | General Stack (For lending) | 005.133 BOS-A (Browse shelf(Opens below)) | Available | 42624 |
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005.133 BLA-J Programming rust : fast safe systems development / | 005.133 BLO-J Effective java / | 005.133 BOO-B C# 2008 Programming, Covers .NET 3. 5, / Black Book | 005.133 BOS-A Python data science essentials : a practitioner's guide covering essential data science principles, tools, and techniques / | 005.133 BRA-G Dart programming language / | 005.133 BRA-H Bitcoin and lightning network on raspberry Pi : running nodes on Pi3, Pi4 and Pi Zero / | 005.133 BRA-J Programming in visual basic.NET / |
Book Description
Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn.
The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost.
By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users.
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