What this book covers

Chapter 1, Big Data Analytics with Spark, introduces Scala, Python and R can be used for data analysis. It also details about Spark programming model, API will be introduced, shows how to install, set up a development environment for the Spark framework and run jobs in distributed mode. I will also show working with DataFrames and Streaming computation models.

Chapter 2, Tricky Statistics with Spark, shows how to apply various statistical measures such as generating sample data, constructing frequency tables, summary and descriptive statistics on large datasets using Spark and Pandas

Chapter 3, Data Analysis with Spark, details how to apply common data exploration and preparation techniques such as univariate analysis, bivariate analysis, missing values treatment, identifying the outliers and techniques for variable transformation using Spark.

Chapter 4, Clustering, Classification and Regression, deals with creating models for regression, classification and clustering as well as shows how to utilize standard performance-evaluation methodologies for the machine learning models built.

Chapter 5, Working with Spark MLlib, provides an overview of Spark MLlib and ML pipelines and presents examples for implementing Naive Bayes classification, decision trees and recommendation systems.

Chapter 6, NLP with Spark, shows how to install NLTK, Anaconda and apply NLP tasks such as POS tagging, Named Entity Recognition, Chunker, Sentence Detector, Lemmatization using Core NLP and Stanford NLP over Spark.

Chapter 7, Working with Sparkling Water - H2O, details how to integrate H2O with Spark and shows applying various algorithms such as k-means, deep learning and SVM and also show developing applications –spam detection and crime detection with Sparkling Water.

Chapter 8, Data Visualization with Spark, show the integration of widely used visualization tools such as Zeppelin, Lightning Server and highly active Scala bindings (Bokeh-Scala) for visualizing large data sets.

Chapter 9, Deep Learning on Spark, shows how to implement deep learning algorithms such as RBM, CNN for learning MNIST, Feed-forward neural networks with the tools Deep Learning4j, TensorFlow using Spark.

Chapter 10, Working with SparkR, provides examples on creating distributed data frames in R, various operations that could be applied in SparkR and details on applying user-defined functions, SQL queries and machine learning in SparkR.