5 data science books you should read in 2017

This article comes from Coursera Blog.

The Data Buzz series brings you a regular roundup of what’s trending in data science.

As data-driven technologies are more and more integrated into everyday life, knowledge of data science is becoming increasingly valuable. Here are five books to boost your data literacy – whether you’re new to the field or a seasoned expert.

1. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

In “Predictive Analytics“, Eric Siegel, a renowned expert in data analytics and former professor at Columbia University, explains how scientists use big data to help predict, well, anything – from what you will buy, to where you will travel, to when you will quit your job, and more. The Seattle Post-Intelligencer called the book “mesmerizing,” and also praised its relevance to multiple business departments.

2. Hadoop, the Definitive Guide

Apache Hadoop is a framework used to process large amounts of data. Tom White is an expert Hadoop consultant, trainer, and member of the Apache Software Foundation. His guide, “Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale,” will help you understand how to build and manage scalable systems using Hadoop. It’s a good reference for programmers, and for IT managers tasked with running Hadoop clusters.

3. An Introduction to Statistical Learning with Applications in R

“An Introduction to Statistical Learning With Applications in R” gives you an overview of analyzing, organizing, and leveraging data using the powerful and popular R programming language. Written by Gareth James, professor of data sciences at USC; Daniela Witten, professor of biostatistics at University of Washington; and Robert Tibshirani and Trevor Hastie, professors of statistics at Stanford, it is ideal for both statisticians and non-technical professionals who are looking to understand data management, analysis, and presentation techniques.

To see the 2 others data science books, click here.

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