Advanced Analytics with PySpark

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• Preface
  • Why Did We Write This Book Now?
  • How This Book Is Organized
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Analyzing Big Data
  • Working with Big Data
  • Introducing Apache Spark and PySpark
    • Components
    • PySpark
    • Ecosystem
  • Spark 3.0
  • PySpark Addresses Challenges of Data Science
  • Where to Go from Here
• 2. Introduction to Data Analysis with PySpark
  • Spark Architecture
  • Installing PySpark
  • Setting Up Our Data
  • Analyzing Data with the DataFrame API
  • Fast Summary Statistics for DataFrames
  • Pivoting and Reshaping DataFrames
  • Joining DataFrames and Selecting Features
  • Scoring and Model Evaluation
  • Where to Go from Here
• 3. Recommending Music and the Audioscrobbler Dataset
  • Setting Up the Data
  • Our Requirements for a Recommender System
    • Alternating Least Squares 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. Making Predictions with Decision Trees and Decision Forests
  • Decision Trees and Forests
  • Preparing the Data
  • Our First Decision Tree
  • Decision Tree Hyperparameters
  • Tuning Decision Trees
  • Categorical Features Revisited
  • Random Forests
  • Making Predictions
  • Where to Go from Here
• 5. Anomaly Detection with K-means Clustering
  • K-means Clustering
  • Identifying Anomalous Network Traffic
    • KDD Cup 1999 Dataset
  • 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 LDA and Spark NLP
  • Latent Dirichlet Allocation
    • LDA in PySpark
  • Getting the Data
  • Spark NLP
    • Setting Up Your Environment
  • Parsing the Data
  • Preparing the Data Using Spark NLP
  • TF-IDF
  • Computing the TF-IDFs
  • Creating Our LDA Model
  • Where to Go from Here
• 7. Geospatial and Temporal Data Analysis on Taxi Trip Data
  • Preparing the Data
    • Converting Datetime Strings to Timestamps
    • Handling Invalid Records
  • Geospatial Analysis
    • Intro to GeoJSON
    • GeoPandas
  • Sessionization in PySpark
    • Building Sessions: Secondary Sorts in PySpark
  • Where to Go from Here
• 8. Estimating Financial Risk
  • Terminology
  • Methods for Calculating VaR
    • Variance-Covariance
    • Historical Simulation
    • Monte Carlo Simulation
  • Our Model
  • Getting the Data
  • Preparing the Data
  • Determining the Factor Weights
  • Sampling
    • The Multivariate Normal Distribution
  • Running the Trials
  • Visualizing the Distribution of Returns
  • Where to Go from Here
• 9. Analyzing Genomics Data and the BDG Project
  • Decoupling Storage from Modeling
  • Setting Up ADAM
  • Introduction to Working with Genomics Data Using ADAM
    • File Format Conversion with the ADAM CLI
    • Ingesting Genomics Data Using PySpark and ADAM
  • Predicting Transcription Factor Binding Sites from ENCODE Data
  • Where to Go from Here
• 10. Image Similarity Detection with Deep Learning and PySpark LSH
  • PyTorch
    • Installation
  • Preparing the Data
    • Resizing Images Using PyTorch
  • Deep Learning Model for Vector Representation of Images
    • Image Embeddings
    • Import Image Embeddings into PySpark
  • Image Similarity Search Using PySpark LSH
    • Nearest Neighbor Search
  • Where to Go from Here
• 11. Managing the Machine Learning Lifecycle with MLflow
  • Machine Learning Lifecycle
  • MLflow
  • Experiment Tracking
  • Managing and Serving ML Models
  • Creating and Using MLflow Projects
  • Where to Go from Here
• Index

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