Skip to main content

Save up to 20% on Elsevier print and eBooks with free shipping. No promo code needed.

Save up to 20% on print and eBooks.

Data Science

Concepts and Practice

2nd Edition - November 27, 2018

Authors: Vijay Kotu, Bala Deshpande

Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 1 4 7 6 1 - 0
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 1 4 7 6 2 - 7

Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or… Read more

Data Science

Purchase options

LIMITED OFFER

Save 50% on book bundles

Immediately download your ebook while waiting for your print delivery. No promo code is needed.

Institutional subscription on ScienceDirect

Request a sales quote

Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions.

Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data.

You’ll be able to:

Gain the necessary knowledge of different data science techniques to extract value from data.

Master the concepts and inner workings of 30 commonly used powerful data science algorithms.

Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform

Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more...