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{ "item_title" : "Data Literacy with Python", "item_author" : [" Oswald Campesato "], "item_description" : "The purpose of this book is to usher readers into the world of data, ensuring a comprehensive understanding of its nuances, intricacies, and complexities. With Python 3 as the primary medium, the book underscores the pivotal role of data in modern industries, and how its adept management can lead to insightful decision-making. The book provides a quick introduction to foundational data-related tasks, priming the readers for more advanced concepts of model training introduced later on. Through detailed, step-by-step Python code examples, the reader will master training models, beginning with the kNN algorithm, and then smoothly transitioning to other classifiers, by tweaking mere lines of code. Tools like Sweetviz, Skimpy, Matplotlib, and Seaborn are introduced, offering readers a hands-on experience in rendering charts and graphs. Companion files with source code and data sets are available by writing to the publisher.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/1/50/152/199/1501521993_b.jpg", "price_data" : { "retail_price" : "52.99", "online_price" : "52.99", "our_price" : "52.99", "club_price" : "52.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Data Literacy with Python|Oswald Campesato

Data Literacy with Python

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Overview

The purpose of this book is to usher readers into the world of data, ensuring a comprehensive understanding of its nuances, intricacies, and complexities. With Python 3 as the primary medium, the book underscores the pivotal role of data in modern industries, and how its adept management can lead to insightful decision-making. The book provides a quick introduction to foundational data-related tasks, priming the readers for more advanced concepts of model training introduced later on. Through detailed, step-by-step Python code examples, the reader will master training models, beginning with the kNN algorithm, and then smoothly transitioning to other classifiers, by tweaking mere lines of code. Tools like Sweetviz, Skimpy, Matplotlib, and Seaborn are introduced, offering readers a hands-on experience in rendering charts and graphs. Companion files with source code and data sets are available by writing to the publisher.

Details

  • ISBN-13: 9781501521997
  • ISBN-10: 1501521993
  • Publisher: Mercury Learning and Information
  • Publish Date: December 2023
  • Dimensions: 9 x 7 x 0.57 inches
  • Shipping Weight: 0.94 pounds
  • Page Count: 320

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