Analysing Big Data with Microsoft R

Transcription

1 Analysing Big Data with Micrsft R Analysing Big Data with Micrsft R Curse Cde: Certificatin Exam: Duratin: 3 Days Certificatin Track: MCSA: Machine Learning Frmat: Classrm Level: 300 Abut this curse: The main purpse f the curse is t give students the ability t use Micrsft R Server t create and run an analysis n a large dataset, and shw hw t utilize it in Big Data envirnments, such as a Hadp r Spark cluster, r a SQL Server database. Audience prfile The primary audience fr this curse is peple wh wish t analyze large datasets within a big data envirnment. The secndary audience are develpers wh need t integrate R analyses int their slutins. At Curse Cmpletin After cmpleting this curse, students will be able t: Explain hw Micrsft R Server and Micrsft R Client wrk Use R Client with R Server t explre big data held in different data stres Visualize data by using graphs and plts Transfrm and clean big data sets Implement ptins fr splitting analysis jbs int parallel tasks Build and evaluate regressin mdels generated frm big data Create, scre, and deply partitining mdels generated frm big data Use R in the SQL Server and Hadp envirnments Prerequisites In additin t their prfessinal experience, students wh attend this curse shuld have: Prgramming experience using R, and familiarity with cmmn R packages Knwledge f cmmn statistical methds and data analysis best practices. Basic knwledge f the Micrsft Windws perating system and its cre functinality. Wrking knwledge f relatinal databases. Student Materials The student kit includes a cmprehensive wrkbk and ther necessary materials fr this class.

2 Analysing Big Data with Micrsft R Curse Outline: Mdule 1: Micrsft R Server and R Client Explain hw Micrsft R Server and Micrsft R Client wrk. What is Micrsft R server? Using Micrsft R client The ScaleR functins Lab 1: Explring Micrsft R Server and Micrsft R Client Using R client in VSTR and RStudi Explring ScaleR functins Cnnecting t a remte server Explain the purpse f R server. Cnnect t R server frm R client Explain the purpse f the ScaleR functins. Mdule 2: Explring Big Data At the end f this mdule the student will be able t use R Client with R Server t explre big data held in different data stres. Understanding ScaleR data surces Reading data int an XDF bject Summarizing data in an XDF bject Lab 2: Explring Big Data Reading a lcal CSV file int an XDF file Transfrming data n input Reading data frm SQL Server int an XDF file Generating summaries ver the XDF data Explain ScaleR data surces Describe hw t imprt XDF data Describe hw t summarize data held in XCF frmat

3 Analysing Big Data with Micrsft R Mdule 3: Visualizing Big Data Explain hw t visualize data by using graphs and plts. Visualizing In-memry data Visualizing big data Lab 3: Visualizing data Using ggplt t create a faceted plt with verlays Using rxlineplt and rxhistgram Use ggplt2 t visualize in-memry data Use rxlineplt and rxhistgram t visualize big data Mdule 4: Prcessing Big Data Explain hw t transfrm and clean big data sets. Transfrming Big Data Managing datasets Lab 4: Prcessing big data Transfrming big data Srting and merging big data Cnnecting t a remte server Transfrm big data using rxdatastep Perfrm srt and merge peratins ver big data sets Mdule 5: Parallelizing Analysis Operatins Explain hw t implement ptins fr splitting analysis jbs int parallel tasks. Using the RxLcalParallel cmpute cntext with rxexec Using the revpemar package Lab 5: Using rxexec and RevPemaR t parallelize peratins Using rxexec t maximize resurce use Creating and using a PEMA class

4 Analysing Big Data with Micrsft R Use the rxlcalparallel cmpute cntext with rxexec Use the RevPemaR package t write custmized scalable and distributable analytics. Mdule 6: Creating and Evaluating Regressin Mdels Explain hw t build and evaluate regressin mdels generated frm big data Clustering Big Data Generating regressin mdels and making predictins Lab 6: Creating a linear regressin mdel Creating a cluster Creating a regressin mdel Generate data fr making predictins Use the mdels t make predictins and cmpare the results Cluster big data t reduce the size f a dataset. Create linear and lgit regressin mdels and use them t make predictins. Mdule 7: Creating and Evaluating Partitining Mdels Explain hw t create and scre partitining mdels generated frm big data. Creating partitining mdels based n decisin trees. Test partitining mdels by making and cmparing predictins Lab 7: Creating and evaluating partitining mdels Splitting the dataset Building mdels Running predictins and testing the results Cmparing results Create partitining mdels using the rxdtree, rxdfrest, and rxbtree algrithms. Test partitining mdels by making and cmparing predictins. Mdule 8: Prcessing Big Data in SQL Server and Hadp Explain hw t transfrm and clean big data sets.

5 Analysing Big Data with Micrsft R Using R in SQL Server Using Hadp Map/Reduce Using Hadp Spark Lab 8: Prcessing big data in SQL Server and Hadp Creating a mdel and predicting utcmes in SQL Server Perfrming an analysis and pltting the results using Hadp Map/Reduce Integrating a sparklyr script int a ScaleR wrkflw Use R in the SQL Server and Hadp envirnments. Use ScaleR functins with Hadp n a Map/Reduce cluster t analyse big data.