Practical Data Science with Hadoop® and Spark: Designing and Building Effective Analytics at Scale
Description
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
Books 
Addison-Wesley Professional
Exclusively Paid 
7h 26m 
No Certificate 
256 pages Practical Data Science with Hadoop® and Spark: Designing and Building Effective Analytics at Scale
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TypeBooks
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ProviderAddison-Wesley Professional
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PricingExclusively Paid
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Duration7h 26m
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CertificateNo Certificate
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
