21 Essential Data Visualization Tools: http://www.kdnuggets.com/2015/05/21-essential-data-visualization-tools.html
Apache Zeppelin notebook home page: https://zeppelin.apache.org/
Jupyter notebook home page: https://jupyter.org/
Using IPython Notebook with Apache Spark: http://hortonworks.com/hadoop-tutorial/using-ipython-notebook-with-apache-spark/
Apache Toree, which enables interactive workloads between applications and Spark cluster. Can be used with jupyter to run Scala code: https://toree.incubator.apache.org/
GoogleVis package using R: https://cran.rproject.org/web/packages/googleVis/vignettes/googleVis_examples.html
GraphX Programming Guide: http://spark.apache.org/docs/latest/graphx-programming-guide.html
Going viral with R's igraph package: https://www.r-bloggers.com/going-viral-with-rs-igraph-package/
Plotting with categorical data: https://stanford.edu/~mwaskom/software/seaborn/tutorial/categorical.html#categorical-tutorial
Spark for Data Science
By :
Spark for Data Science
By:
Overview of this book
This is the era of Big Data. The words ‘Big Data’ implies big innovation and enables a competitive advantage for businesses. Apache Spark was designed to perform Big Data analytics at scale, and so Spark is equipped with the necessary algorithms and supports multiple programming languages.
Whether you are a technologist, a data scientist, or a beginner to Big Data analytics, this book will provide you with all the skills necessary to perform statistical data analysis, data visualization, predictive modeling, and build scalable data products or solutions using Python, Scala, and R.
With ample case studies and real-world examples, Spark for Data Science will help you ensure the successful execution of your data science projects.
Table of Contents (18 chapters)
Spark for Data Science
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Big Data and Data Science – An Introduction
The Spark Programming Model
Introduction to DataFrames
Unified Data Access
Data Analysis on Spark
Machine Learning
Extending Spark with SparkR
Analyzing Unstructured Data
Visualizing Big Data
Putting It All Together
Building Data Science Applications
Customer Reviews