Advanced Analytics with Spark 1st Edition by Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills ISBN 9781491912713 1491912715

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Product details:
ISBN 10: 1491912715
ISBN 13: 9781491912713
Author: Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills

Advanced Analytics with Spark 1st Edition Table of contents:

1. Analyzing Big Data

The Challenges of Data Science

Introducing Apache Spark

About This Book

2. Introduction to Data Analysis with Scala and Spark

Scala for Data Scientists

The Spark Programming Model

Record Linkage

Getting Started: The Spark Shell and SparkContext

Bringing Data from the Cluster to the Client

Shipping Code from the Client to the Cluster

Structuring Data with Tuples and Case Classes

Aggregations

Creating Histograms

Summary Statistics for Continuous Variables

Creating Reusable Code for Computing Summary Statistics

Simple Variable Selection and Scoring

Where to Go from Here

3. Recommending Music and the Audioscrobbler Data Set

Data Set

The Alternating Least Squares Recommender Algorithm

Preparing the Data

Building a First Model

Spot Checking Recommendations

Evaluating Recommendation Quality

Computing AUC

Hyperparameter Selection

Making Recommendations

Where to Go from Here

4. Predicting Forest Cover with Decision Trees

Fast Forward to Regression

Vectors and Features

Training Examples

Decision Trees and Forests

Covtype Data Set

Preparing the Data

A First Decision Tree

Decision Tree Hyperparameters

Tuning Decision Trees

Categorical Features Revisited

Random Decision Forests

Making Predictions

Where to Go from Here

5. Anomaly Detection in Network Traffic with K-means Clustering

Anomaly Detection

K-means Clustering

Network Intrusion

KDD Cup 1999 Data Set

A First Take on Clustering

Choosing k

Visualization in R

Feature Normalization

Categorical Variables

Using Labels with Entropy

Clustering in Action

Where to Go from Here

6. Understanding Wikipedia with Latent Semantic Analysis

The Term-Document Matrix

Getting the Data

Parsing and Preparing the Data

Lemmatization

Computing the TF-IDFs

Singular Value Decomposition

Finding Important Concepts

Querying and Scoring with the Low-Dimensional Representation

Term-Term Relevance

Document-Document Relevance

Term-Document Relevance

Multiple-Term Queries

Where to Go from Here

7. Analyzing Co-occurrence Networks with GraphX

The MEDLINE Citation Index: A Network Analysis

Getting the Data

Parsing XML Documents with Scala’s XML Library

Analyzing the MeSH Major Topics and Their Co-occurrences

Constructing a Co-occurrence Network with GraphX

Understanding the Structure of Networks

Connected Components

Degree Distribution

Filtering Out Noisy Edges

Processing EdgeTriplets

Analyzing the Filtered Graph

Small-World Networks

Cliques and Clustering Coefficients

Computing Average Path Length with Pregel

Where to Go from Here

8. Geospatial and Temporal Data Analysis on the New York City Taxi Trip Data

Getting the Data

Working with Temporal and Geospatial Data in Spark

Temporal Data with JodaTime and NScalaTime

Geospatial Data with the Esri Geometry API and Spray

Exploring the Esri Geometry API

Intro to GeoJSON

Preparing the New York City Taxi Trip Data

Handling Invalid Records at Scale

Geospatial Analysis

Sessionization in Spark

Building Sessions: Secondary Sorts in Spark

Where to Go from Here

9. Estimating Financial Risk through Monte Carlo Simulation

Terminology

Methods for Calculating VaR

Variance-Covariance

Historical Simulation

Monte Carlo Simulation

Our Model

Getting the Data

Preprocessing

Determining the Factor Weights

Sampling

The Multivariate Normal Distribution

Running the Trials

Visualizing the Distribution of Returns

Evaluating Our Results

Where to Go from Here

10. Analyzing Genomics Data and the BDG Project

Decoupling Storage from Modeling

Ingesting Genomics Data with the ADAM CLI

Parquet Format and Columnar Storage

Predicting Transcription Factor Binding Sites from ENCODE Data

Querying Genotypes from the 1000 Genomes Project

Where to Go from Here

11. Analyzing Neuroimaging Data with PySpark and Thunder

Overview of PySpark

PySpark Internals

Overview and Installation of the Thunder Library

Loading Data with Thunder

Thunder Core Data Types

Categorizing Neuron Types with Thunder

Where to Go from Here

A. Deeper into Spark

Serialization

Accumulators

Spark and the Data Scientist’s Workflow

File Formats

Spark Subprojects

MLlib

Spark Streaming

Spark SQL

GraphX

B. Upcoming MLlib Pipelines API

Beyond Mere Modeling

The Pipelines API

Text Classification Example Walkthrough

Index

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