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

By: Mueller, John Paul, author Contributor(s): Massaron, Luca, author Material type: Text

TextSeries: (For dummies)Publisher: Hoboken, New Jersey : John Wiley & Sons, c2019Edition: Second editionDescription: xvi, 467 pages : illustrations ; 23 cmISBN: 9781119547624Subject(s): Python (Computer program language) | Computer programmingLOC classification: QA 76.76 | M84 2019

Contents:

Part 1 - Getting stared 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

Summary: 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. 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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Item type	Current location	Collection	Shelving location	Call number	Copy number	Status	Date due	Barcode
Book	Cavite State University - CCAT Campus	Book	GCS	CIR QA 76.76 M84 2019 (Browse shelf)	1	Available		R0012213

Includes index.

Part 1 - Getting stared 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

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. 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.

In English Text.

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