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Python for data science / by John Paul Mueller and Luca Massaron.

By: Contributor(s): Material type: TextTextSeries: --For dummiesPublisher: Hoboken, NJ : John Wiley & Sons, Inc., [2019]Copyright date: ©2019Edition: 2nd editionDescription: xvi, 467 pages : illustrations ; 24 cmContent type:
  • text
  • still image
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 1119547628
  • 9781119547624
Subject(s):
Contents:
Part 1: Getting started with data science and Python -- Part 2: Getting your hands dirty with data -- Part 3: Visualizing information -- Part 4: Wrangling data -- Part 5: Learning from data -- Part 6: The part of tens.
List(s) this item appears in: Computer Science & Coding for Adults
Holdings
Item type Home library Collection Call number Materials specified Status Date due Barcode Item holds
Adult Book Adult Book Main Library NonFiction 006.312 M946 Available 33111009328309
Total holds: 0

Enhanced descriptions from Syndetics:

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.

Includes index.

Part 1: Getting started with data science and Python -- Part 2: Getting your hands dirty with data -- Part 3: Visualizing information -- Part 4: Wrangling data -- Part 5: Learning from data -- Part 6: The part of tens.

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