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A01=OSWALD CAMPESATO
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Data Literacy With Python

English

By (author): OSWALD CAMPESATO

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.

FEATURES:
  • Introduces tools like Sweetviz, Skimpy, Matplotlib, and Seaborn offering readers a hands-on experience in rendering charts and graphs
  • Companion files with numerous Python code samples
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Original price €52.99
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A01=OSWALD CAMPESATOAge Group_UncategorizedAuthor_OSWALD CAMPESATOautomatic-updateCategory1=Non-FictionCategory=TBCCategory=UBCategory=UMCategory=UMXCategory=UNCCategory=UNFCategory=UYCOP=United StatesDelivery_Pre-orderLanguage_EnglishPA=Temporarily unavailablePrice_€50 to €100PS=Activesoftlaunch

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Product Details
  • Dimensions: 178 x 229mm
  • Publication Date: 30 Nov 2023
  • Publisher: De Gruyter
  • Publication City/Country: United States
  • Language: English
  • ISBN13: 9781501521997

About OSWALD CAMPESATO

Oswald Campesato is an adjunct instructor at UC-Santa Cruz and specializes in Deep Learning Python Data Science and GPT-4. He is the author/co-author of over forty books including Python and Machine Learning Data Cleaning and NLP for Developers (all Mercury Learning and Information).

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