Practical Data Science with Hadoop and Spark: Designing and Building Effective Analytics at Scale

by Mendelevitch, Ofer; Stella, Casey; Eadline, Douglas

• ISBN13: 9780134024141
• ISBN10: 0134024141
• Edition: 1st
• Format: Paperback
• Copyright: 2016-12-12
• Publisher: Addison-Wesley Professional

Summary

The Complete Guide to Data Science with Hadoop—For Technical Professionals, Businesspeople, and Students

 

Demand is soaring for professionals who can solve real data science problems with Hadoop and Spark. Practical Data Science with Hadoop® and Spark is your complete guide to doing just that. Drawing on immense experience with Hadoop and big data, three leading experts bring together everything you need: high-level concepts, deep-dive techniques, real-world use cases, practical applications, and hands-on tutorials.

 

The authors introduce the essentials of data science and the modern Hadoop ecosystem, explaining how Hadoop and Spark have evolved into an effective platform for solving data science problems at scale. In addition to comprehensive application coverage, the authors also provide useful guidance on the important steps of data ingestion, data munging, and visualization.

 

Once the groundwork is in place, the authors focus on specific applications, including machine learning, predictive modeling for sentiment analysis, clustering for document analysis, anomaly detection, and natural language processing (NLP).

 

This guide provides a strong technical foundation for those who want to do practical data science, and also presents business-driven guidance on how to apply Hadoop and Spark to optimize ROI of data science initiatives.

 

Learn

• What data science is, how it has evolved, and how to plan a data science career
• How data volume, variety, and velocity shape data science use cases
• Hadoop and its ecosystem, including HDFS, MapReduce, YARN, and Spark
• Data importation with Hive and Spark
• Data quality, preprocessing, preparation, and modeling
• Visualization: surfacing insights from huge data sets
• Machine learning: classification, regression, clustering, and anomaly detection
• Algorithms and Hadoop tools for predictive modeling
• Cluster analysis and similarity functions
• Large-scale anomaly detection
• NLP: applying data science to human language

Author Biography

Ofer Mendelevitch is Vice President of Data Science at Lendup, where he is responsible for Lendup’s machine learning and advanced analytics group. Prior to joining Lendup, Ofer was Director of Data Science at Hortonworks, where he was responsible for helping Hortonwork’s customers apply Data Science with Hadoop and Spark to big data across various industries including healthcare, finance, retail and others. Before Hortonworks, Ofer served as Entrepreneur in Residence at XSeed Capital, VP of Engineering at Nor1, and Director of Engineering at Yahoo!.

 

Casey Stella is a Principal Software Engineer focusing on Data Science at Hortonworks, which provides an open source Hadoop distribution. Casey’s primary responsibility is leading the analytics/data science team for the Apache Metron (Incubating) Project, an open source cybersecurity project. Prior to Hortonworks, Casey was an architect at Explorys, which was a medical informatics startup spun out of the Cleveland Clinic.  In the more distant past, Casey served as a developer at Oracle, Research Geophysicist at ION Geophysical and as a poor graduate student in Mathematics at Texas A&M.

 

Douglas Eadline, PhD, began his career as analytical chemist with an interest in computer methods. Starting with the first Beowulf how-to document, Doug has written hundreds of articles, white papers, and instructional documents covering many aspects of HPC and Hadoop computing. Prior to starting and editing the popular ClusterMonkey.net website in 2005, he served as editor¿in¿chief for ClusterWorld Magazine and was senior HPC editor for Linux Magazine. He has practical hands-on experience in many aspects of HPC and Apache Hadoop, including hardware and software design, benchmarking, storage, GPU, cloud computing, and parallel computing. Currently, he is a writer and consultant to the HPC/analytics industry and leader of the Limulus Personal Cluster Project (http://limulus.basement-supercomputing.com). He is author of the Apache Hadoop® Fundamentals LiveLessons and Apache Hadoop® YARN Fundamentals LiveLessons videos from Pearson, and is book co-author of Apache Hadoop® YARN: Moving beyond MapReduce and Batch Processing with Apache Hadoop 2 and author of Hadoop® 2 Quick Start Guide: Learn the Essentials of Big Data Computing in the Apache Hadoop 2 Ecosystem, also from Addison-Wesley, and is author of High Performance Computing for Dummies.

Table of Contents

Part 1: Data Science with Hadoop - An Overview
1. Introduction to Data Science
2. Data Science Use-Cases
3. Hadoop and Data Science

Part 2: The Process of Data Science with Hadoop
4. The Process of Data Science
5. Getting the Data into Hadoop
6. Data Preparation
7. Data Modeling
8. Visualization

Part 3: Real World Examples
9. Building a Recommender System With Mahout
10. Customer Segmentation with Kmeans
11. Analyzing Sentiment
12. Predictive Risk Modeling

Part 4: The Road Ahead
13. Advanced Topics
14. The Data Science Journey