9781119547624-1119547628-Python for Data Science For Dummies, 2nd Edition (For Dummies (Computer/Tech))

Python for Data Science For Dummies, 2nd Edition (For Dummies (Computer/Tech))

ISBN-13: 9781119547624
ISBN-10: 1119547628
Edition: 2
Author: John Paul Mueller, Luca Massaron
Publication date: 2019
Publisher: For Dummies
Format: Paperback 496 pages
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Book details

ISBN-13: 9781119547624
ISBN-10: 1119547628
Edition: 2
Author: John Paul Mueller, Luca Massaron
Publication date: 2019
Publisher: For Dummies
Format: Paperback 496 pages

Summary

Python for Data Science For Dummies, 2nd Edition (For Dummies (Computer/Tech)) (ISBN-13: 9781119547624 and ISBN-10: 1119547628), written by authors John Paul Mueller, Luca Massaron, was published by For Dummies in 2019. With an overall rating of 4.3 stars, it's a notable title among other Microsoft Programming (Programming) books. You can easily purchase or rent Python for Data Science For Dummies, 2nd Edition (For Dummies (Computer/Tech)) (Paperback) from BooksRun, along with many other new and used Microsoft Programming books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $1.53.

Description

The fast and easy way to learn Python programming and statistics

Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library.

Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud.

  • Get started with data science and Python
  • Visualize information
  • Wrangle data
  • Learn from data

The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.

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