000			03237nam a2200325 i 4500
001			10015926
003			CaAEU
005			20240319104112.0
006			m o d |
007			cr cnu||||||||
008			190429s2019 enk o 001 0 eng d
020			_a9781789955286
035			_aon1097972859
039			_aexclude
040			_aMiAaPQ _beng _erda _epn _cMiAaPQ _dMiAaPQ _dAEU
050		4	_aQA76.73.P98 _b.M375 2019
082	0		_a005.133 _223
090			_aInternet Access _bAOAC
100	1		_aMarin, Ivan, _eauthor.
245	1	0	_aBig data analysis with Python _b: combine spark and python to unlock the powers of parallel computing and machine learning _c/ Ivan Marin, Ankit Shukla, Sarang VK.
264		1	_aBirmingham : _bPackt Publishing, _c2019.
300			_a276 pages
500			_aIncludes index.
520			_aProcessing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. With this book, you'll learn practical techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems. The book begins with an introduction to data manipulation in Python using pandas. You'll then get familiar with statistical analysis and plotting techniques. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated in memory. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The book also covers Spark and explains how it interacts with other tools. By the end of this book, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs. Learning Objectives: Use Python to read and transform data into different formats — Generate basic statistics and metrics using data on disk — Work with computing tasks distributed over a cluster — Convert data from various sources into storage or querying formats — Prepare data for statistical analysis, visualization, and machine learning — Present data in the form of effective visuals. Approach: Big Data Analysis with Python takes a hands-on approach to understanding how to use Python and Spark to process data and make something useful out of it. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context. Audience: Big Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights. Basic knowledge of statistical measurements and relational databases will help you to understand various concepts explained in this book.
650		0	_aPython (Computer program language)
700	1		_aShukla, Ankit, _eauthor.
700	1		_aVK, Sarang, _eauthor.
942			_2lcc
999			_c3695 _d3695
041			_aEnglish