Practical data science with Hadoop and Spark : designing and building effective analytics at scale / Ofer Mendelevitch, Casey Stella, Douglas Eadline.

Includes index.

I. Data science with Hadoop : an overview. Introduction to data science ; Use cases for data science ; Hadoop and data science -- II. Preparing and visualizing data with Hadoop. Getting data into Hadoop ; Data munging with Hadoop ; Exploring and visualizing data -- III. Applying data modeling with Hadoop. Machine learning with Hadoop ; Predictive modeling ; Clustering ; Anomaly detection with Hadoop ; Natural language processing ; Data science with Hadoop : the next frontier.

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.