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Analyzing Big Data with R Programming

Accelebrate's Analyzing Big Data with R Programming training teaches attendees how to use In-memory/on-disk, distributed analysis using H20, Hadoop, and Apache Spark, and how to integrate Microsoft...

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By Request
$3,020 USD
Course Code ACCEL-R-ABDP
Duration 4 days
Available Formats Classroom

Overview

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Accelebrate's Analyzing Big Data with R Programming training teaches attendees how to use In-memory/on-disk, distributed analysis using H20, Hadoop, and Apache Spark, and how to integrate Microsoft Machine Learning Server and R.

Skills Gained

  • Understand how R works with big data sets
  • Manage big data in memory with data.table
  • Conduct exploratory data analysis with data.table
  • Learn big data management strategies such as sampling, chunk-and-pull, and pushing compute to the database
  • Run SQL queries directly against R dataframes using DuckDB
  • Use DuckDB as an out-of memory backend for R dataframes
  • Perform machine learning operations using mlr3
  • Interface with Apache Spark using Sparklyr or SparkR
  • Use H2O for data munging and machine learning

Prerequisites

In addition to their professional experience, students who attend this course should have:

  • Programming experience using R, and familiarity with common R packages
  • Knowledge of common statistical methods and data analysis best practices
  • Basic knowledge of the Microsoft Windows operating system and its core functionality

Course Details

Training Materials

All R training students receive comprehensive courseware.

Software Requirements

  • A recent release of R 4.x
  • IDE or text editor of your choice (RStudio recommended)

Outline

  • Introduction: 
    • Does R work with big datasets?
    • What challenges does big data introduce when using R?
    • ETL and descriptive data tasks
    • Modeling tasks, optimization challenges
  • In-memory Big Data: Data.table
    • Why do we need data.table?
    • The i and the j arguments in data.table
    • Renaming columns
    • Adding new columns
    • Binning data (continuous to categorical)
    • Combining categorical values
    • Transforming variables
    • Group-by functions with data.table
    • Chaining commands with data.table
    • Data.table pronouns .N, .SD, SDCols
    • Handling missing data
  • EDA with Data.table
    • Data subsetting, splitting, and merging
    • Managing datasets
    • Long to wide and back
    • Merging datasets together
    • Stacking datasets together (concatenation)
    • Data summarization
      • Numerical summaries
      • Categorical summaries
      • Multivariate summaries
    • Creating visualizations
  • Big Three Strategies for dealing with Big Data in R
    • https://rviews.rstudio.com/2019/07/17/3-big-data-strategies-for-r/
    • 1. Sampling
    • 2. Chunk-and-pull
    • 3. Push compute to DB
  • DuckDB 
    • Overview: DuckDB works nicely with R
    • Basic SQL commands for working with DuckDB
    • Understanding query performance optimizations
    • Using dbplyr to work with DuckDB
  • mlr3 for Machine Learning in R
    • Overview of mlr3
    • Goals of machine learning
    • mlr3 R6 object-oriented R and methods
    • Defining a task
    • Assigning roles to data
    • Performing a classification
    • Performing a regression
    • Visualization with mlr3
    • Pipelines
    • Model assessment
    • Model optimization
    • Implementing general linear models
    • Establishing and leveraging partitions/clusters
    • Fitting regression models and making predictions
    • Decision trees and random forests
    • Naïve bayes
    • Implementing stacked models via pipelines
    • Implementing an AutoML model via pipelines
    • Managing resource utilization through parallelization
  • Apache Spark
    • Overview of Spark
    • APIs to use Apache Spark with R
    • Sparklyr versus SparkR
    • R, Python, Java and Scala APIs to Spark
    • Applied Examples using SparkR
    • Spark and H2O together: sparklingwater
    • Data import and manipulation in Spark(R)
    • The Spark machine learning library MLlib:
      • General linear models
      • Random forest
      • Naïve bayes
    • Data Munging and Machine Learning Via H20
      • Intro to H20
      • Launching the cluster, checking status
      • Data Import, manipulation in H20
      • Fitting models in H20
      • Generalized Linear Models
      • Naïve bayes
      • Random forest
      • Gradient boosting machine (GBM)
      • Ensemble model building
      • AutoML
      • Methods for explaining modeling output
  • Conclusion
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