Spiegare come funzionano Microsoft R Server e Microsoft R Client
Utilizzare il client R con il server R per esplorare i Big Data contenuti nei diversi store di dati
Visualizzare i dati utilizzando grafici e diagrammi
Trasformare e pulire grandi set di dati
Implementare le opzioni per dividere i lavori di analisi in attività parallele
Costruire e valutare i modelli di regressione generati dai Big Data
Creare, contrassegnare e distribuire i modelli di partizionamento generati dai Big Data
Utilizzare R nell'ambiente SQL Server e Hadoop
Esperienza di programmazione usando R, e familiarità con i comuni pacchetti R.
Conoscenza dei metodi statistici comuni e delle migliori pratiche di analisi dei dati.
Conoscenza di base del sistema operativo Microsoft Windows e delle sue funzionalità principali.
Module 1: Microsoft R Server and R Client
What is Microsoft R server
Using Microsoft R client
The ScaleR functions
Lab : Exploring Microsoft R Server and Microsoft R Client
Using R client in VSTR and RStudio
Exploring ScaleR functions
Connecting to a remote server
After completing this module, students will be able to:
Explain the purpose of R server.
Connect to R server from R client
Explain the purpose of the ScaleR functions.
Module 2: Exploring Big Data
Understanding ScaleR data sources
Reading data into an XDF object
Summarizing data in an XDF object
Lab : Exploring Big Data
Reading a local CSV file into an XDF file
Transforming data on input
Reading data from SQL Server into an XDF file
Generating summaries over the XDF data
After completing this module, students will be able to:
Explain ScaleR data sources
Describe how to import XDF data
Describe how to summarize data held in XCF format
Module 3: Visualizing Big Data
Visualizing In-memory data
Visualizing big data
Lab : Visualizing data
Using ggplot to create a faceted plot with overlays
Using rxlinePlot and rxHistogram
After completing this module, students will be able to:
Use ggplot2 to visualize in-memory data
Use rxLinePlot and rxHistogram to visualize big data
Module 4: Processing Big Data
Transforming Big Data
Managing datasets
Lab : Processing big data
Transforming big data
Sorting and merging big data
Connecting to a remote server
After completing this module, students will be able to:
Transform big data using rxDataStep
Perform sort and merge operations over big data sets
Module 5: Parallelizing Analysis Operations
Using the RxLocalParallel compute context with rxExec
Using the revoPemaR package
Lab : Using rxExec and RevoPemaR to parallelize operations
Using rxExec to maximize resource use
Creating and using a PEMA class
After completing this module, students will be able to:
Use the rxLocalParallel compute context with rxExec
Use the RevoPemaR package to write customized scalable and distributable analytics.
Module 6: Creating and Evaluating Regression Models
Clustering Big Data
Generating regression models and making predictions
Lab : Creating a linear regression model
Creating a cluster
Creating a regression model
Generate data for making predictions
Use the models to make predictions and compare the results
After completing this module, students will be able to:
Cluster big data to reduce the size of a dataset.
Create linear and logit regression models and use them to make predictions.
Module 7: Creating and Evaluating Partitioning Models
Creating partitioning models based on decision trees.
Test partitioning models by making and comparing predictions
Lab : Creating and evaluating partitioning models
Splitting the dataset
Building models
Running predictions and testing the results
Comparing results
After completing this module, students will be able to:
Create partitioning models using the rxDTree, rxDForest, and rxBTree algorithms.
Test partitioning models by making and comparing predictions.
Module 8: Processing Big Data in SQL Server and Hadoop
Using R in SQL Server
Using Hadoop Map/Reduce
Using Hadoop Spark
Lab : Processing big data in SQL Server and Hadoop
Creating a model and predicting outcomes in SQL Server
Performing an analysis and plotting the results using Hadoop Map/Reduce
Integrating a sparklyr script into a ScaleR workflow
After completing this module, students will be able to:
Use R in the SQL Server and Hadoop environments.
Use ScaleR functions with Hadoop on a Map/Reduce cluster to analyze big data.