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Python for data science for dummies

By: Mueller, John.
Contributor(s): Massaron, Luca.
Material type: materialTypeLabelBookPublisher: New Delhi : Wiley, ©2015Description: xii, 418 p. : ill.; 25 cm.ISBN: 9788126557394.Subject(s): Python (Computer program language) | Programming languages (Electronic computers) | Data mining | Data structures (Computer science)Genre/Form: Electronic books. | Electronic books.Summary: Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. You'll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. It covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models; explains objects, functions, modules, and libraries and their role in data analysis; walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib. --
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Item type Current location Collection Call number Status Date due Barcode Item holds
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Computer Science and Engineering 005.133 MUE-P (Browse shelf) Checked out 27/08/2019 006353
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Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. You'll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. It covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models; explains objects, functions, modules, and libraries and their role in data analysis; walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib. --

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