Description
Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. Youâ??ll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support. You will discover Pythonâ??s rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go in-depth with demonstrations of real-world data applications, including using location data to power a taxi service, market basket analysis to identify items commonly purchased together, and machine learning to predict stock prices.
Books 
No Starch Press
Exclusively Paid 
6h 3m 
No Certificate 
248 pages -
TypeBooks
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ProviderNo Starch Press
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PricingExclusively Paid
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Duration6h 3m
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CertificateNo Certificate
Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. Youâ??ll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support.
You will discover Pythonâ??s rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go in-depth with demonstrations of real-world data applications, including using location data to power a taxi service, market basket analysis to identify items commonly purchased together, and machine learning to predict stock prices.
