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Tabla de Contenidos: “…-- Batch processing -- Streaming method -- Cloud-native -- Automating ETL pipelines -- Exploring use cases for ETL pipelines -- Summary -- References -- Chapter 3: Design Principles for Creating Scalable and Resilient Pipelines -- Technical requirements -- Understanding the design patterns for ETL -- Basic ETL design pattern -- ETL-P design pattern -- ETL-VP design pattern -- ELT two-phase pattern -- Preparing your local environment for installations -- Open source Python libraries for ETL pipelines -- Pandas -- NumPy -- Scaling for big data packages -- Dask -- Numba -- Summary -- References -- Part 2: Designing ETL Pipelines with Python -- Chapter 4: Sourcing Insightful Data and Data Extraction Strategies -- Technical requirements -- What is data sourcing? …”

Materias: “…Big data…”

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Acceso restringido con credenciales UPSA

Tabla de Contenidos: “…References -- Summary -- Chapter 5: Bayesian Regression Models -- Generalized linear regression -- The arm package -- The Energy efficiency dataset -- Regression of energy efficiency with building parameters -- Ordinary regression -- Bayesian regression -- Simulation of the posterior distribution -- Exercises -- References -- Summary -- Chapter 6: Bayesian Classification Models -- Performance metrics for classification -- The Naïve Bayes classifier -- Text processing using the tm package -- Model training and prediction -- The Bayesian logistic regression model -- The BayesLogit R package -- The dataset -- Preparation of the training and testing datasets -- Using the Bayesian logistic model -- Exercises -- References -- Summary -- Chapter 7: Bayesian Models for Unsupervised Learning -- Bayesian mixture models -- The bgmm package for Bayesian mixture models -- Topic modeling using Bayesian inference -- Latent Dirichlet allocation -- R packages for LDA -- The topicmodels package -- The lda package -- Exercises -- References -- Summary -- Chapter 8: Bayesian Neural Networks -- Two-layer neural networks -- Bayesian treatment of neural networks -- The brnn R package -- Deep belief networks and deep learning -- Restricted Boltzmann machines -- Deep belief networks -- The darch R package -- Other deep learning packages in R -- Exercises -- References -- Summary -- Chapter 9: Bayesian Modeling at Big Data Scale -- Distributed computing using Hadoop -- RHadoop for using Hadoop from R -- Spark - in-memory distributed computing -- SparkR -- Linear regression using SparkR -- Computing clusters on the cloud -- Amazon Web Services -- Creating and running computing instances on AWS -- Installing R and RStudio -- Running Spark on EC2 -- Microsoft Azure -- IBM Bluemix -- Other R packages for large scale machine learning -- The parallel R package…”