Dòng Nội dung
1
Advanced analytics with Spark :patterns for learning from data at scale /Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills.
Beijing : O'Reilly, 2017
xii, 264 pages :illustrations ;24 cm
Ký hiệu phân loại (DDC): 006.312
The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by presenting examples and a set of self-contained patterns for performing large-scale data analysis with Spark. You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques-classification, collaborative filtering, and anomaly detection among others-to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you'll find these patterns useful for working on your own data applications.
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2
Learning Spark /Holden Karau, Andy Konwinski, Patrick Wendell, and Matei Zaharia.
Beijing ; Sebastopol : O'Reilly, 2015
xvi, 254 pages :illustrations ;24 cm
Ký hiệu phân loại (DDC): 006.312
This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. You'll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.--
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3
R for data science : import, tidy, transform, visualize, and model data / Hadley Wickham and Garrett Grolemund.
Sebastopol, CA : O'Reilly, 2017
xxv, 492 pages :illustrations (some color) ;23 cm
Ký hiệu phân loại (DDC): 006.312
"This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience"--
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