Python for data science for dummies

By:

Mueller, John

Contributor(s):

Massaron, Luca

Material type: Text

9788126557394

Subject(s):

Genre/Form:

DDC classification:

005.133 23 MUE-P

LOC classification:

QA76.73.P98 M37 2015eb

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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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Books	IIITD General Stacks	Computer Science and Engineering	005.133 MUE-P (Browse shelf(Opens below))	Available		006353

Total holds: 0

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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