Want to learn big Data? Let’s finish reading these books first

True data enthusiasts have a lot to read: Big data, machine learning, data science, data mining, etc. In addition to these technical areas, there are specific technologies and languages that you need to continue to study: Hadoop, Spark, Python, and R to name a few, and there are countless automation tools that you use almost every day, which requires constant learning. Fortunately, there is no shortage of books about any of these.

Here are some basic big data books that are on Amazon’s bestseller list:

About Big Data

1. Big Data

In the context of big data, I rarely see data modeling, data layers, data processing requirements analysis, and data architecture and storage implementation issues. This book offers refreshingly comprehensive solutions.

But it also introduces complexity that most developers aren’t familiar with and that plagues traditional architectures. This book will teach you how to create these systems by taking full advantage of the Lambda architecture of cluster hardware, as well as new tools specifically designed to capture and analyze network scale data.

About Apache Hadoop

Hadoop: The Definitive Guide

This book uses a wealth of case studies to explain the behind-the-scenes mechanics of Hadoop and how It solves real-world problems. Release 3 covers the latest developments in Hadoop, including the new MapReduceAPI, as well as MapReduce2 and its more flexible execution model (YARN).

About the Apache Spark

3. Learning Spark

Spark Fast Big Data Analysis is a book for Spark beginners. It focuses more on the usage of upper-layer users than on the implementation details. However, this book is not only about the usage of Spark. It also introduces the core concepts and basic principles of Spark, so that the readers can understand what Spark is and why.

About Data Mining

4. Data Mining

This book is a comprehensive overview of the field of data mining, which I think is best used as a course book for graduate students, or as a reference book. The previous edition of this book has been voted the most popular data mining monograph by KDnuggets readers and is an extremely readable textbook.

It systematically introduces the concept, method and technology of data mining and its research progress from the perspective of database, and focuses on the important and latest topics in this field in recent years — data warehouse and data cube technology, streaming data mining, social network mining, spatial, multimedia and other complex data mining.

5. Mining of Massive Datasets

The book is based on material from a quarterly course that Anand Rajaraman and Jeff Ullman taught at Stanford for many years. Simply put, this book is about data mining. However, this book focuses on the mining of data that is too large to be stored in memory.

Because of the emphasis on the size of the data, most of the examples in this book come from the Web itself or data exported from the Web. In addition, the book looks at data mining from an algorithmic perspective, that is, data mining is the application of algorithms to data, rather than the use of data to “train” some type of machine learning engine.

e-books

In addition to the books mentioned above, there are many introductory books in data science, but you should have a broad understanding of the field before you really start.

Below we’ve selected five eBooks that will help you better understand what data science is all about and prepare you for your future studies in data science, big data, and data analytics.

Big Data: The Numbers Game Deciphered

For a succinct overview of the world of big data, read this 11-page ebook, which is based on the latest developments in data science. After reading the book, you’ll learn:

● Qualifications to become a data scientist

● Technical/non-technical skills required in the field of data science

● Learning resources for data science

Book download address:

http://www.simplilearn.com/the-numbers-game-deciphered-guide-pdf

2. Top Programming Languages for a Data Scientist

Programming is a core technical skill that data scientists absolutely must have. Learn which programming languages can be a priority for beginners in data science with this detailed guide. After reading this book, you can understand

● A list of top 10 programming languages for data science careers;

● The characteristics of these programming languages;

● How to apply your skills to data scientists.

Book download address:

http://www.simplilearn.com/top-programming-languages-for-data-scientist-guide-pdf

8 Essential Concepts of Big Data and Hadoop

Hadoop is arguably the most important technology in the big data family and the core of the big data revolution. Learn everything you need to know about Hadoop and its ecosystem by reading this handy guide.

Book download address:

http://www.simplilearn.com/big-data-and-hadoop-8-essential-concepts-guide-pdf

Secret to Unlocking Tableau’s Hidden Potential

Tableau makes analytics easy, not just for analysts, but for senior managers, IT professionals, and everyone else. If you’re looking for tips to make the most of Tableau, as well as useful hacking tips, this ebook will show you what you need to know.

Book Address:

http://www.simplilearn.com/secret-to-unlocking-tableau-hidden-potential-guide-pdf

5. Top 25 Interview Questions and Answers: Big Data Analysis

Even if you’re a great data scientist, you still need to impress the interviewer in a job interview, or you’ll still have a hard time landing the job you’ve always dreamed of. This book explores the most frequently asked questions and answers in big data interviews.

Book download address:

http://www.simplilearn.com/top-big-data-analysis-interview-questions-answers-guide-pdf