A Hands-on Introduction to Big Data Analytics

A Hands-on Introduction to Big Data Analytics
Author: Funmi Obembe
Publisher: SAGE Publications Limited
Total Pages: 415
Release: 2024-02-23
Genre: Business & Economics
ISBN: 1529615909


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This practical textbook offers a hands-on introduction to big data analytics, helping you to develop the skills required to hit the ground running as a data professional. It complements theoretical foundations with an emphasis on the application of big data analytics, illustrated by real-life examples and datasets. Containing comprehensive coverage of all the key topics in this area, this book uses open-source technologies and examples in Python and Apache Spark. Learning features include: - Ethics by Design encourages you to consider data ethics at every stage. - Industry Insights facilitate a deeper understanding of the link between what you are studying and how it is applied in industry. - Datasets, questions, and exercises give you the opportunity to apply your learning. Dr Funmi Obembe is the Head of Technology at the Faculty of Arts, Science and Technology, University of Northampton. Dr Ofer Engel is a Data Scientist at the University of Groningen.


A Hands-on Introduction to Big Data Analytics
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This practical textbook offers a hands-on introduction to big data analytics, helping you to develop the skills required to hit the ground running as a data pro
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Big data is defined as collections of datasets whose volume, velocity or variety is so large that it is difficult to store, manage, process and analyze the data
A Hands-on Introduction to Big Data Analytics
Language: en
Pages: 415
Authors: Funmi Obembe
Categories: Business & Economics
Type: BOOK - Published: 2024-02-23 - Publisher: SAGE Publications Limited

GET EBOOK

This practical textbook offers a hands-on introduction to big data analytics, helping you to develop the skills required to hit the ground running as a data pro
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Use PySpark to easily crush messy data at-scale and discover proven techniques to create testable, immutable, and easily parallelizable Spark jobs Key FeaturesW