Data Science and Big Data Analytics

Author: EMC Education Services
Publisher: Wiley, 2015
Pages: 432
ISBN: 9781118876138
Print: 111887613X
Kindle: B00RXHVQF6
Aimed at: Programmers who need to analyze data
Rating: 4
Reviewed by: Kay Ewbank

The subtitle "Discovering, Analyzing, Visualizing and Presenting Data" indicates that this is another title on a currently hot topic.

This book aims to teach you about big data analytics, though the techniques discussed are well known statistical methods. There are individual chapters on the most commonly used techniques, each chapter covering the key concepts of the technique, the principles behind it, R code using it, and sample exercises illustrating its use.

 

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The book starts with a chapter introducing big data analytics, looking briefly at what big data is before moving on to look at the ‘state of practice in analytics’. Having introduced the topic, the authors then move on to look at the lifecycle of data analytics – discovery, data preparation, model planning, and model building. The chapter ends with a sample case study showing the different stages in action, and the code and data samples can be downloaded so you can work through the exercises.

The programming language used throughout the book is R, and the next chapter looks at basic data analysis methods using R. The authors introduce the language, discuss exploratory data analysis, then look at hypothesis testing, difference of means, Wilcoxon Rank-Sum, and ANOVA.

 

 

From here onwards the chapters move on to different aspects of advanced analytical theory and methods, starting with a chapter on clustering with a good discussion of K-means. There are chapters on association rules, regression, classification, time series analysis and text analysis.

A chapter on technology and tools looks at MapReduce and Hadoop, though at an introductory level essentially saying what the different parts of the ecosystem are and what roles they play. A chapter on in-database analytics does a similar job with SQL. The book ends with a chapter putting the whole thing together.

I found the book to be quite formal in tone – it is essentially a textbook for the EMC Proven Professional Data Science Certification and is also used as the basis of the EMC MOOC Data Lakes For Big Data MOOC. However, the concepts are explained well, there are good examples, and the authors have picked a good middle route on the amount of technicality on the maths behind the statistical methods – not skimming over it, but not getting too bogged down on it either. 

 

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Author: James W. Cooper
Publisher: Addison-Wesley
Date: February 2022
Pages: 352
ISBN: 978-0137579938
Print: 0137579934
Kindle: B09D2RKQB5
Audience: Python developers
Rating: 1
Reviewer: Mike James
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Authors: Jon Krohn, Grant Beyleveld and Aglaé Bassens
Publisher: Addison-Wesley
Date: September 2019
Pages: 416
ISBN: 978-0135116692
Print: 0135116694
Kindle: B07W585JGG
Audience: Python developers interested in deep learning techniques
Rating: 3.5
Reviewer: Mike James
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Last Updated ( Friday, 01 June 2018 )