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

By: Contributor(s): Material type: TextTextSeries: Publisher: Hoboken, NJ : John Wiley and Sons, Inc., 2019Edition: 2nd edDescription: 496 pages : illustrations ; 24 cmISBN:
  • 9788126524938
Other title:
  • Python for data science
Subject(s): DDC classification:
  • 005.133 23 MUP
LOC classification:
  • QA76.73.P98 M376 2015
Contents:
pt. I. Getting started with Python for data science -- Discovering the match between data science and Python -- Introducing Python's capabilities and wonders -- Setting up Python for data science -- Reviewing basic Python -- pt. II. Getting your hands dirty with data -- Working with real data -- Conditioning your data -- Shaping data -- Putting what you know in action -- pt. III. Visualizing the invisible -- Getting a crash course in MatPlotLib -- Visualizing the data -- Understanding the tools -- pt. IV. Wrangling data -- Stretching Python's capabilities -- Exploring data analysis -- Reducing dimensionality -- Clustering -- Detecting outliers in data -- pt. V. Learning from data -- Exploring four simple and effective algorithms -- Performing cross-validation, selection, and optimization -- Increasing complexity with linear and nonlinear tricks -- Understanding the power of the many -- pt. VI. The part of tens -- Ten essential data science resource collections -- Ten data challenges you should take.
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Holdings
Item type Current library Call number Copy number Status Date due Barcode
Books Books Seminar Library, Department of Genetic Engineering & Biotechnology General Stacks 005.133 MUP (Browse shelf(Opens below)) 1 Available 0081406

Includes index.

pt. I. Getting started with Python for data science -- Discovering the match between data science and Python -- Introducing Python's capabilities and wonders -- Setting up Python for data science -- Reviewing basic Python -- pt. II. Getting your hands dirty with data -- Working with real data -- Conditioning your data -- Shaping data -- Putting what you know in action -- pt. III. Visualizing the invisible -- Getting a crash course in MatPlotLib -- Visualizing the data -- Understanding the tools -- pt. IV. Wrangling data -- Stretching Python's capabilities -- Exploring data analysis -- Reducing dimensionality -- Clustering -- Detecting outliers in data -- pt. V. Learning from data -- Exploring four simple and effective algorithms -- Performing cross-validation, selection, and optimization -- Increasing complexity with linear and nonlinear tricks -- Understanding the power of the many -- pt. VI. The part of tens -- Ten essential data science resource collections -- Ten data challenges you should take.

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