Warning High-Performance Data Mining and Big Data Analytics Warning by Khosrow Hassibi.
Before you order this book, there are three things you need to take into account.
First, the book claims to target eight (8) separate audiences:
Target Audience: This book is intended for a variety of audiences:
(1) There are many people in the technology, science, and business disciplines who are curious to learn about big data analytics in a broad sense, combined with some historical perspective. They may intend to enter the big data market and play a role. For this group, the book provides an overview of many relevant topics. College and high school students who have interest in science and math, and are contemplating about what to pursue as a career, will also find the book helpful.
(2) For the executives, business managers, and sales staff who also have an interest in technology, believe in the importance of analytics, and want to understand big data analytics beyond the buzzwords, this book provides a good overview and a deeper introduction of the relevant topics.
(3) Those in classic organizations—at any vertical and level— who either manage or consume data find this book helpful in grasping the important topics in big data analytics and its potential impact in their
organizations.(4) Those in IT benefit from this book by learning about the challenges of the data consumers: data miners/scientists, data analysts, and other business users. Often the perspectives of IT and analytics users are different on how data is to be managed and consumed.
(5) Business analysts can learn about the different big data technologies and how it may impact what they do today.
(6) Statisticians typically use a narrow set of statistical tools and usually work on a narrow set of business problems depending on their industry. This book points to many other frontiers in which statisticians can continue to play important roles.
(7) Since the main focus of the book is high-performance data mining and contrasting it with big data analytics in terms of commonalities and differences, data miners and machine learning practitioners gain a holistic view of how the two relate.
(8) Those interested in data science gain from the historical viewpoint of the book since the practice of data science—as opposed to the name itself—has existed for a long time. Big data revolution has significantly helped create awareness about analytics and increased the need for data science professionals.
Second, are you wondering how a book covers that many audiences and that much technology in a little over 300 pages? Review the Table of Contents. See how in depth the coverage appears to be to you.
Third, you do know that Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeff Ullman, is available for free (electronic copy) and in hard copy from Cambridge University Press. Yes?
Its prerequisites are:
1. An introduction to database systems, covering SQL and related programming systems.
2. A sophomore-level course in data structures, algorithms, and discrete math.
3. A sophomore-level course in software systems, software engineering, and programming languages.
With one audience, satisfying technical prerequisites, Mining Massive Datasets (MMD) runs over five hundred (500) pages.
Up to you but I prefer narrower in depth coverage of topics.