Data Literacy with Python

¡ Walter de Gruyter GmbH & Co KG
āχ-āĻŦ⧁āĻ•
320
āĻĒ⧃āĻˇā§āĻ āĻž
āϰ⧇āϟāĻŋāĻ‚ āĻ“ āϰāĻŋāĻ­āĻŋāω āϝāĻžāϚāĻžāχ āĻ•āϰāĻž āĻšā§ŸāύāĻŋ  āφāϰāĻ“ āϜāĻžāύ⧁āύ

āĻāχ āχ-āĻŦ⧁āϕ⧇āϰ āĻŦāĻŋāĻˇā§Ÿā§‡

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.

āϞ⧇āĻ–āĻ• āϏāĻŽā§āĻĒāĻ°ā§āϕ⧇

Oswald Campesato (San Francisco, CA) specializes in Deep Learning, Python, and GPT-4. He is the author/co-author of over thirty-five books including Python 3 Using ChatGPT / GPT-4, NLP for Developers, and Artificial Intelligence, Machine Learning and Deep Learning (all Mercury Learning).

āχ-āĻŦ⧁āϕ⧇ āϰ⧇āϟāĻŋāĻ‚ āĻĻāĻŋāύ

āφāĻĒāύāĻžāϰ āĻŽāϤāĻžāĻŽāϤ āϜāĻžāύāĻžāύāĨ¤

āĻĒāĻ āύ āϤāĻĨā§āϝ

āĻ¸ā§āĻŽāĻžāĻ°ā§āϟāĻĢā§‹āύ āĻāĻŦāĻ‚ āĻŸā§āϝāĻžāĻŦāϞ⧇āϟ
Android āĻāĻŦāĻ‚ iPad/iPhone āĻāϰ āϜāĻ¨ā§āϝ Google Play āĻŦāχ āĻ…ā§āϝāĻžāĻĒ āχāύāĻ¸ā§āϟāϞ āĻ•āϰ⧁āύāĨ¤ āĻāϟāĻŋ āφāĻĒāύāĻžāϰ āĻ…ā§āϝāĻžāĻ•āĻžāωāĻ¨ā§āĻŸā§‡āϰ āϏāĻžāĻĨ⧇ āĻ…āĻŸā§‹āĻŽā§‡āϟāĻŋāĻ• āϏāĻŋāĻ™ā§āĻ• āĻšā§Ÿ āĻ“ āφāĻĒāύāĻŋ āĻ…āύāϞāĻžāχāύ āĻŦāĻž āĻ…āĻĢāϞāĻžāχāύ āϝāĻžāχ āĻĨāĻžāϕ⧁āύ āύāĻž āϕ⧇āύ āφāĻĒāύāĻžāϕ⧇ āĻĒ⧜āϤ⧇ āĻĻā§‡ā§ŸāĨ¤
āĻ˛ā§āϝāĻžāĻĒāϟāĻĒ āĻ“ āĻ•āĻŽā§āĻĒāĻŋāωāϟāĻžāϰ
Google Play āĻĨ⧇āϕ⧇ āϕ⧇āύāĻž āĻ…āĻĄāĻŋāĻ“āĻŦ⧁āĻ• āφāĻĒāύāĻŋ āĻ•āĻŽā§āĻĒāĻŋāωāϟāĻžāϰ⧇āϰ āĻ“ā§Ÿā§‡āĻŦ āĻŦā§āϰāĻžāωāϜāĻžāϰ⧇ āĻļ⧁āύāϤ⧇ āĻĒāĻžāϰ⧇āύāĨ¤
eReader āĻāĻŦāĻ‚ āĻ…āĻ¨ā§āϝāĻžāĻ¨ā§āϝ āĻĄāĻŋāĻ­āĻžāχāϏ
Kobo eReaders-āĻāϰ āĻŽāϤ⧋ e-ink āĻĄāĻŋāĻ­āĻžāχāϏ⧇ āĻĒāĻĄāĻŧāϤ⧇, āφāĻĒāύāĻžāϕ⧇ āĻāĻ•āϟāĻŋ āĻĢāĻžāχāϞ āĻĄāĻžāωāύāϞ⧋āĻĄ āĻ“ āφāĻĒāύāĻžāϰ āĻĄāĻŋāĻ­āĻžāχāϏ⧇ āĻŸā§āϰāĻžāĻ¨ā§āϏāĻĢāĻžāϰ āĻ•āϰāϤ⧇ āĻšāĻŦ⧇āĨ¤ āĻŦā§āϝāĻŦāĻšāĻžāϰāĻ•āĻžāϰ⧀āϰ āωāĻĻā§āĻĻ⧇āĻļā§āϝ⧇ āϤ⧈āϰāĻŋ āϏāĻšāĻžā§ŸāϤāĻž āϕ⧇āĻ¨ā§āĻĻā§āϰāϤ⧇ āĻĻ⧇āĻ“ā§ŸāĻž āύāĻŋāĻ°ā§āĻĻ⧇āĻļāĻžāĻŦāϞ⧀ āĻ…āύ⧁āϏāϰāĻŖ āĻ•āϰ⧇ āϝ⧇āϏāĻŦ eReader-āĻ āĻĢāĻžāχāϞ āĻĒāĻĄāĻŧāĻž āϝāĻžāĻŦ⧇ āϏ⧇āĻ–āĻžāύ⧇ āĻŸā§āϰāĻžāĻ¨ā§āϏāĻĢāĻžāϰ āĻ•āϰ⧁āύāĨ¤