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Data Science and Data Analytics Using PYTHON

Data Science and Data Analytics Using PYTHON

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Book Details
  • Author: Munesh Chandra Trivedi , Anil Kumar Dubey
  • Publisher: ? Khanna Book Publishing
  • Pages: 296
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The book is a comprehensive guide that delves into the fundamentals of data science and analytics leveraging the python programming language The Book covers key topics such as data manipulation visualization statistical analysis and machine learning it includes practical examples and code snippets to help readers understand and implement various data science techniques Designed for both beginners and experienced practitioners it provides a thorough understanding of how to use python for effective data The book also emphasizes real world applications making it a valuable resource for those looking to apply data science skills in various domains. SILENT FEATURES OF BOOK The textbook encompasses an extensive array of data science topics including data pre -processing visualization statistical analysis machine learning and deep learning with a pronounced emphasis on practical application using python programming It integrates case studies derived from real world scenarios alongside numerous exercises facilitating hands on practical experience The text provides detailed procedural guidance for implementing data science projects and explores various data visualization techniques utilizing libraries such as Matplotlib Seaborn and plotly It thoroughly examines a diverse range of machine learning algorithms and offers an introductory overview of deep learning concepts and frameworks including tensorFlow and keras The textbook incorporates expert tips best practices and focuses on high demand skills rendering it an invaluable resource for both novice and seasoned practitioners in the field of data science and analytics. Contents: 1. Introduction of data Science 2. Python Programming 3. Numpy and Pandas Library 4. Scikit Learn and Scipy Library 5. Data Manipulation 6. Modeling Techniques 7. Data Visualization 8. Data Analytics 9. Pre Processing 10. Stastics of data Analytics 11. Regression 12. Classification Read more

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