DATAMATH
Data Algorithms
ISBN: 978-1-491-90618-7
US $69.99 CAN $80.99
Twitter: @oreillymedia
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If you are ready to dive into the MapReduce framework for processing
large datasets, this practical book takes you step by step through
the algorithms and tools you need to build distributed MapReduce
applications with Apache Hadoop or Apache Spark. Each chapter provides
a recipe for solving a massive computational problem, such as building a
recommendation system. You’ll learn how to implement the appropriate
MapReduce solution with code that you can use in your projects.
Dr. Mahmoud Parsian covers basic design patterns, optimization techniques,
and data mining and machine learning solutions for problems in bioinformatics,
genomics, statistics, and social network analysis. This book also includes an
overview of MapReduce, Hadoop, and Spark.
Topics include:
■ Market basket analysis for a large set of transactions
■ Data mining algorithms (K-means, KNN, and Naive Bayes)
■ Using huge genomic data to sequence DNA and RNA
■ Naive Bayes theorem and Markov chains for data and market
prediction
■ Recommendation algorithms and pairwise document similarity
■ Linear regression, Cox regression, and Pearson correlation
■ Allelic frequency and mining DNA
■ Social network analysis (recommendation systems, counting
triangles, sentiment analysis)
Mahmoud Parsian, PhD in Computer Science, is a practicing software professional with
30 years of experience as a developer, designer, architect, and author. Currently the leader
of Illumina’s Big Data team, he’s spent the past 15 years working with Java (server-side),
databases, MapReduce, and distributed computing. Mahmoud is the author of JDBC
Recipes and JDBC Metadata, MySQL, and Oracle Recipes (both Apress).
Mahmoud Parsian
Data
Algorithms
RECIPES FOR SCALING UP WITH HADOOP AND SPARK
Data
Algorithms
Parsian
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