Practical data science with Hadoop and Spark : designing and building effective analytics at scale /

The Complete Guide to Data Science with Hadoop--For Technical Professionals, Businesspeople, and Students Demand is soaring for professionals who can solve real data science problems with Hadoop and Spark. Practical Data Science with Hadoop® and Spark is your complete guide to doing just that. Drawi...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Medelevitch, Ofer (Autor), Stella, Casey (Autor), Eadline, Doug, 1956- (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Boston : Addison-Wesley, [2017]
Colección:Addison-Wesley data and analytics series.
Temas:
Apache Hadoop.
Spark (Electronic resource : Apache Software Foundation)
Electronic data processing > Distributed processing.
Big data > Computer programs.
Traitement réparti.
Données volumineuses > Logiciels.
COMPUTERS > Data Processing.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Machine generated contents note: I. Data Science with Hadoop
  • An Overview
  • 1. Introduction to Data Science
  • What Is Data Science?
  • Example: Search Advertising
  • A Bit of Data Science History
  • Statistics and Machine Learning
  • Innovation from Internet Giants
  • Data Science in the Modern Enterprise
  • Becoming a Data Scientist
  • The Data Engineer
  • The Applied Scientist
  • Transitioning to a Data Scientist Role
  • Soft Skills of a Data Scientist
  • Building a Data Science Team
  • The Data Science Project Life Cycle
  • Ask the Right Question
  • Data Acquisition
  • Data Cleaning: Taking Care of Data Quality
  • Explore the Data and Design Model Features
  • Building and Tuning the Model
  • Deploy to Production
  • Managing a Data Science Project
  • Summary
  • 2. Use Cases for Data Science
  • Big Data
  • A Driver of Change
  • Volume: More Data Is Now Available
  • Variety: More Data Types
  • Velocity: Fast Data Ingest
  • Business Use Cases
  • Product Recommendation.
  • Note continued: Customer Churn Analysis
  • Customer Segmentation
  • Sales Leads Prioritization
  • Sentiment Analysis
  • Fraud Detection
  • Predictive Maintenance
  • Market Basket Analysis
  • Predictive Medical Diagnosis
  • Predicting Patient Re-admission
  • Detecting Anomalous Record Access
  • Insurance Risk Analysis
  • Predicting Oil and Gas Well Production Levels
  • Summary
  • 3. Hadoop and Data Science
  • What Is Hadoop?
  • Distributed File System
  • Resource Manager and Scheduler
  • Distributed Data Processing Frameworks
  • Hadoop's Evolution
  • Hadoop Tools for Data Science
  • Apache Sqoop
  • Apache Flume
  • Apache Hive
  • Apache Pig
  • Apache Spark
  • Python
  • Java Machine Learning Packages
  • Why Hadoop Is Useful to Data Scientists
  • Cost Effective Storage
  • Schema on Read
  • Unstructured and Semi-Structured Data
  • Multi-Language Tooling
  • Robust Scheduling and Resource Management
  • Levels of Distributed Systems Abstractions
  • Scalable Creation of Models.
  • Note continued: Scalable Application of Models
  • Summary
  • II. Preparing and Visualizing Data with Hadoop
  • 4. Getting Data Into Hadoop
  • Hadoop as a Data Lake
  • The Hadoop Distributed File System (HDFS)
  • Direct File Transfer to Hadoop HDFS
  • Importing Data from Files into Hive Tables
  • Import CSV Files into Hive Tables
  • Importing Data into Hive Tables Using Spark
  • Import CSV Files into HIVE Using Spark
  • Import a JSON File into HIVE Using Spark
  • Using Apache Sqoop to Acquire Relational Data
  • Data Import and Export with Sqoop
  • Apache Sqoop Version Changes
  • Using Sqoop V2: A Basic Example
  • Using Apache Flume to Acquire Data Streams
  • Using Flume: A Web Log Example Overview
  • Manage Hadoop Work and Data Flows with Apache Oozie
  • Apache Falcon
  • What's Next in Data Ingestion?
  • Summary
  • 5. Data Munging with Hadoop
  • Why Hadoop for Data Munging?
  • Data Quality
  • What Is Data Quality?
  • Dealing with Data Quality Issues.
  • Note continued: Using Hadoop for Data Quality
  • The Feature Matrix
  • Choosing the "Right" Features
  • Sampling: Choosing Instances
  • Generating Features
  • Text Features
  • Time-Series Features
  • Features from Complex Data Types
  • Feature Manipulation
  • Dimensionality Reduction
  • Summary
  • 6. Exploring and Visualizing Data
  • Why Visualize Data?
  • Motivating Example: Visualizing Network Throughput
  • Visualizing the Breakthrough That Never Happened
  • Creating Visualizations
  • Comparison Charts
  • Composition Charts
  • Distribution Charts
  • Relationship Charts
  • Using Visualization for Data Science
  • Popular Visualization Tools
  • R
  • Python: Matplotlib, Seaborn, and Others
  • SAS
  • Matlab
  • Julia
  • Other Visualization Tools
  • Visualizing Big Data with Hadoop
  • Summary
  • III. Applying Data Modeling with Hadoop
  • 7. Machine Learning with Hadoop
  • Overview of Machine Learning
  • Terminology
  • Task Types in Machine Learning.
  • Note continued: Big Data and Machine Learning
  • Tools for Machine Learning
  • The Future of Machine Learning and Artificial Intelligence
  • Summary
  • 8. Predictive Modeling
  • Overview of Predictive Modeling
  • Classification Versus Regression
  • Evaluating Predictive Models
  • Evaluating Classifiers
  • Evaluating Regression Models
  • Cross Validation
  • Supervised Learning Algorithms
  • Building Big Data Predictive Model Solutions
  • Model Training
  • Batch Prediction
  • Real-Time Prediction
  • Example: Sentiment Analysis
  • Tweets Dataset
  • Data Preparation
  • Feature Generation
  • Building a Classifier
  • Summary
  • 9. Clustering
  • Overview of Clustering
  • Uses of Clustering
  • Designing a Similarity Measure
  • Distance Functions
  • Similarity Functions
  • Clustering Algorithms
  • Example: Clustering Algorithms
  • k-means Clustering
  • Latent Dirichlet Allocation
  • Evaluating the Clusters and Choosing the Number of Clusters.
  • Note continued: Building Big Data Clustering Solutions
  • Example: Topic Modeling with Latent Dirichlet Allocation
  • Feature Generation
  • Running Latent Dirichlet Allocation
  • Summary
  • 10. Anomaly Detection with Hadoop
  • Overview
  • Uses of Anomaly Detection
  • Types of Anomalies in Data
  • Approaches to Anomaly Detection
  • Rules-based Methods
  • Supervised Learning Methods
  • Unsupervised Learning Methods
  • Semi-Supervised Learning Methods
  • Tuning Anomaly Detection Systems
  • Building a Big Data Anomaly Detection Solution with Hadoop
  • Example: Detecting Network Intrusions
  • Data Ingestion
  • Building a Classifier
  • Evaluating Performance
  • Summary
  • 11. Natural Language Processing
  • Natural Language Processing
  • Historical Approaches
  • NLP Use Cases
  • Text Segmentation
  • Part-of-Speech Tagging
  • Named Entity Recognition
  • Sentiment Analysis
  • Topic Modeling
  • Tooling for NLP in Hadoop
  • Small-Model NLP
  • Big-Model NLP.
  • Note continued: Textual Representations
  • Bag-of-Words
  • Word2vec
  • Sentiment Analysis Example
  • Stanford CoreNLP
  • Using Spark for Sentiment Analysis
  • Summary
  • 12. Data Science with Hadoop
  • The Next Frontier
  • Automated Data Discovery
  • Deep Learning
  • Summary
  • A. Book Web Page and Code Download
  • B. HDFS Quick Start
  • Quick Command Dereference
  • General User HDFS Commands
  • List Files in HDFS
  • Make a Directory in HDFS
  • Copy Files to HDFS
  • Copy Files from HDFS
  • Copy Files within HDFS
  • Delete a File within HDFS
  • Delete a Directory in HDFS
  • Get an HDFS Status Report (Administrators)
  • Perform an FSCK on HDFS (Administrators)
  • C. Additional Background on Data Science and Apache Hadoop and Spark
  • General Hadoop/Spark Information
  • Hadoop/Spark Installation Recipes
  • HDFS
  • MapReduce
  • Spark
  • Essential Tools
  • Machine Learning.

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