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Advanced analytics with PySpark : patterns for learning from data at scale using Python and Spark / Akash Tandon and others

By:

Tandon, Akash

Contributor(s):

Material type: Text

TextPublication details: India Shroff Publishers 2022Description: 220 pISBN:

9789355422804

Subject(s):

DDC classification:

006.312 TAN-A

Average rating: 0.0 (0 votes)

Holdings
Item type	Current library	Collection	Shelving location	Call number	Copy number	Status	Date due	Barcode	Item holds
Books	BITS Pilani Hyderabad	003-007	General Stack (For lending)	006.312 TAN-A (Browse shelf(Opens below))	INR 950.00.	Available		47398

Total holds: 0

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Previous	006.312 ROG-J Data science and analytics with python /	006.312 SCA-R Python and R for the modern data scientist : the best of both worlds /	006.312 SUR-T Data mining : principles and applications /	006.312 TAN-A Advanced analytics with PySpark : patterns for learning from data at scale using Python and Spark /	006.312 TOR-L Data mining with R : learning with case studies /	006.312 TSA-K Machine learning for knowledge discovery with R : methodologies for modeling, inference, and prediction /	006.312 TUF-S Data mining and statistics for decision making /	Next

The amount of data being generated today is staggering and growing. Apache Spark has emerged as the de facto tool to analyze big data and is now a critical part of the data science toolbox. Updated for Spark 3.0, this practical guide brings together Spark, statistical methods, and real-world datasets to teach you how to approach analytics problems using PySpark, Spark's Python API, and other best practices in Spark programming.

Data scientists Akash Tandon, Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills offer an introduction to the Spark ecosystem, then dive into patterns that apply common techniques-including classification, clustering, collaborative filtering, and anomaly detection, to fields such as genomics, security, and finance. This updated edition also covers NLP and image processing.

If you have a basic understanding of machine learning and statistics and you program in Python, this book will get you started with large-scale data analysis.

Familiarize yourself with Spark's programming model and ecosystem
Learn general approaches in data science
Examine complete implementations that analyse large public datasets
Discover which machine learning tools make sense for particular problems
Explore code that can be adapted to many uses.

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