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Python for Data Science For Dummies

af John Paul Mueller
Python for Data Science For Dummies af John Paul Mueller
Bag om Python for Data Science For Dummies

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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  • Sprog:
  • Engelsk
  • ISBN:
  • 9781119547624
  • Indbinding:
  • Paperback
  • Sideantal:
  • 496
  • Udgivet:
  • 5. april 2019
  • Udgave:
  • 2
  • Størrelse:
  • 188x234x23 mm.
  • Vægt:
  • 914 g.
  • Ukendt - mangler pt..
Forlænget returret til d. 31. januar 2025
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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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