MARC details
| 000 -LEADER |
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| fixed length control field |
03260 a2200193 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230817b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781789537864 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
005.74015 |
| Cutter |
BOS |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Boschetti Alberto |
| 245 ## - TITLE STATEMENT |
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| Title |
Python Data Science Essentials: A practitioner’s guide covering essential data science principles, tools, and techniques |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
3rd |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
Packt Publishing Limited |
| Date of publication, distribution, etc |
2018 |
| Place of publication, distribution, etc |
Mumbai |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
458 |
| 520 ## - Remark |
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| Summary, etc |
Gain useful insights from your data using popular data science tools<br/><br/>Key Features<br/>A one-stop guide to Python libraries such as pandas and NumPy<br/>Comprehensive coverage of data science operations such as data cleaning and data manipulation<br/>Choose scalable learning algorithms for your data science tasks<br/>Book Description<br/>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.<br/><br/>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.<br/><br/>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<br/><br/>What you will learn<br/>Set up your data science toolbox on Windows, Mac, and Linux<br/>Use the core machine learning methods offered by the scikit-learn library<br/>Manipulate, fix, and explore data to solve data science problems<br/>Learn advanced explorative and manipulative techniques to solve data operations<br/>Optimize your machine learning models for optimized performance<br/>Explore and cluster graphs, taking advantage of interconnections and links in your data<br/>Who this book is for<br/>If you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.<br/><br/>Source: https://www.amazon.in/Python-Data-Science-Essentials-practitioners/dp/178953786X/ref=sr_1_1?crid=31JYKGGINXSFM&keywords=Python+Data+Science+Essentials%3A+A+practitioner%E2%80%99s+guide+covering+essential+data+science+principles%2C+tools%2C+and+techniques&qid=1692274935&sprefix=python+data+science+essentials+a+practitioner+s+guide+covering+essential+data+science+principles%2C+tools%2C+and+techniques%2Caps%2C214&sr=8-1 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Python |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Data Science |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
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| a |
Business Analytics |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Item type |
Book |
| Source of classification or shelving scheme |
Dewey Decimal Classification |
