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Python for Data Science (A Hands-On Introduction)
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Product Details
Author:
Yuli Vasiliev
Format:
Paperback
Pages:
240
Publisher:
No Starch Press (August 2, 2022)
Language:
English
ISBN-13:
9781718502208
ISBN-10:
1718502206
Weight:
15.1oz
Dimensions:
7" x 9.19" x 0.67"
File:
RandomHouse-PRH_Book_Company_PRH_PRT_Onix_full_active_D20260705T120902_156890290-20260705.xml
Folder:
RandomHouse
List Price:
$59.99
Case Pack:
32
As low as:
$46.19
Publisher Identifier:
P-RH
Discount Code:
A
QuickShip:
Yes
Audience:
General/trade
Country of Origin:
United States
Pub Discount:
65
Imprint:
No Starch Press
Overview
A hands-on, real-world introduction to data analysis with the Python programming language, loaded with wide-ranging examples.
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. You’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 Python’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.
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. You’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 Python’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.
