Advanced Analytics with Spark. Patterns for Learning from Data at Scale. 2nd Edition

Spis treści ebooka

• Foreword
• Preface
  • Whats in This Book
  • The Second Edition
  • Using Code Examples
  • OReilly Safari
  • How to Contact Us
  • Acknowledgments
• 1. Analyzing Big Data
  • The Challenges of Data Science
  • Introducing Apache Spark
  • About This Book
  • The Second Edition
• 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
  • From RDDs to Data Frames
  • Analyzing Data with the DataFrame API
  • Fast Summary Statistics for DataFrames
  • Pivoting and Reshaping DataFrames
  • Joining DataFrames and Selecting Features
  • Preparing Models for Production Environments
  • Model Evaluation
  • 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 with SparkR
  • Feature Normalization
  • Categorical Variables
  • Using Labels with Entropy
  • Clustering in Action
  • Where to Go from Here
• 6. Understanding Wikipedia with Latent Semantic Analysis
  • The Document-Term Matrix
  • Getting the Data
  • Parsing and Preparing the Data
  • Lemmatization
  • Computing the TF-IDFs
  • Singular Value Decomposition
  • Finding Important Concepts
  • Querying and Scoring with a Low-Dimensional Representation
  • Term-Term Relevance
  • Document-Document Relevance
  • Document-Term 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 Scalas 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 New York City Taxi Trip Data
  • Getting the Data
  • Working with Third-Party Libraries in Spark
  • 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
• Index

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