Data Analytics With Hadoop ─ An Introduction for Data Scientists

If you’re a data scientist ready to tackle statistical and machine learning techniques across large data sets, this practical guide provides a solid introduction to the world of clustered computing and analytics with Hadoop. Instead of deployment, operations, or software development, this book focuses on particular analyses you can build, the data warehousing techniques that Hadoop provides, and the higher order data workflows it can produce.

You’ll learn a wide range of topics, from the basics to Mahout, Hive, and HBase, and gain an understanding of the analytical processes and data systems available to build and empower data products that require huge amounts of data.

Benjamin Bengfort is a data scientist with a passion for massive machine learning involving gigantic natural language corpora, and has been leveraging that passion to develop a keen understanding of recommendation algorithms at Cobrain in Bethesda, MD where he serves as the Chief Data Scientist. With a professional background in military and intelligence, and an academic background in economics and computer science, he brings a unique set of skills and insights to his work. Ben believes that data is a currency that can pave the way to discovering insights and solve complex problems. He is also currently pursuing a PhD in Computer Science at the University of Maryland.

Jenny Kim is an experienced data scientist who works in both commercialsoftware efforts as well as in academia. She has significant experience in working with large scale data, machine learning, and Hadoop implementations in production and research environments. Jenny (with Benjamin Bengfort) built a large scale recommender system that used a web crawler to gather ontological information about apparel products and produce recommendations from transactions. Currently she teaches Introduction to Hadoop and Advanced Hadoop courses on Statistics.com.