Transcription of M.Tech. DATA ANALYTICS
1 (Applicableform2017-18onwards)Department ofComputerApplicationsNationalInstituteo fTechnologyTiruchirappalli 620015,Tamilnadu1 SYLLABUSS emesterSubjectCodeSubjectNameCreditsICA6 01 StatisticalComputing3CA603 BigDataAnalytics3CA605 MachineLearningTechniques3**Elective-13* *Elective-23**Elective-33CA609 BigDataManagementandDataAnalyticsLab2 IICS618 RealTimeSystems3CA602 NextGenerationDatabases3CA604 HighPerformanceComputing3**Elective-43** Elective-53**Elective-63CA610 MachineLearningLab2 IIICA647 Projectwork-PhaseI12 IVCA648 Projectwork-PhaseII12 TotalCredits642 LISTOFELECTIVESS emesterSubjectCodeSubjectNameCreditsICS6 55 DigitalForensics3CA611 CyberSecurityandInformationAssurance3CA6 12 NaturalLanguageComputing3CA613 MassiveGraphAnalysis3CA614 Bioinformatics3CA615 ParallelandDistributedComputing3CA616 DataAcquisitionandProductization3CA617 EssentialsofHumanResourceAnalytics3CA618 CustomerRelationshipandManagement3
2 IICA619 PrinciplesofDeepLearning3CA620 ImageandVideoAnalytics3CA621 SocialNetworkingandMining3CA622 WebIntelligence3CA623 InternetofThings3CA624 HealthcareDataAnalytics3CA625 LinkedOpenDataandSemanticWeb3CA626 FinancialRiskAnalyticsandManagement3CA62 7 LogisticsandSupplyChainManagement33 SEMESTER-ICA601 STATISTICALCOMPUTINGO bjectives: Tolearntheprobabilitydistributionsandden sityestimationstoperformanalysisofvariou skindsofdata. Toexplorethestatisticalanalysistechnique susingPythonandRprogramminglanguages. :SampleSpaces-Events-Axioms Counting-ConditionalProbabilityandBayes Theorem TheBinomialTheorem :TheCentralLimitTheorem,distributionsoft hesamplemeanandthesamplevarianceforanorm alpopulation,Samplingdistributions(Chi-S quare,t,F,z).TestofHypothesis-Testingfor Attributes MeanofNormalPopulation One-tailedandtwo-tailedtests,F-testandCh i-Squaretest--AnalysisofvarianceANOVA , LearningR ,O Reilly, ,Peter, IntroductorystatisticswithR ,SpringerScience&BusinessMedia, , AHandbookofStatisticalAnalysisUsingR ,SecondEdition,4 LLC, , MasteringPythonforDataScience ,Packt, , IntroductiontoProbabilityandStatisticsfo rEngineersandScientists ,4thedition,AcademicPress; , RCookbook,O Reilly, , LearningPython ,O Reilly,5thEdition,2013 Outcomes:Studentswillbeableto: Implementstatisticalanalysistechniquesfo rsolvingpracticalproblems.
3 Performstatisticalanalysisonvarietyofdat a. : Tooptimizebusinessdecisionsandcreatecomp etitiveadvantagewithBigDataanalytics Toexplorethefundamentalconceptsofbigdata ANALYTICS . Tolearntoanalyzethebigdatausingintellige nttechniques. Tounderstandthevarioussearchmethodsandvi sualizationtechniques. Tolearntousevarioustechniquesforminingda tastream. TounderstandtheapplicationsusingMapReduc eConcepts. TointroduceprogrammingtoolsPIG& :IntroductiontoBigDataPlatform ChallengesofConventionalSystems-Intellig entdataanalysis :IntroductionToStreamsConcepts StreamDataModelandArchitecture-StreamCom puting-SamplingDatainaStream FilteringStreams CountingDistinctElementsinaStream EstimatingMoments CountingOnenessinaWindow DecayingWindow-RealtimeAnalyticsPlatform (RTAP) :HistoryofHadoop-theHadoopDistributedFil eSystem ComponentsofHadoopAnalysingtheDatawithHa doop-ScalingOut-HadoopStreaming-Designof HDFS-JavainterfacestoHDFSB asics-DevelopingaMapReduceApplication-Ho wMapReduceWorks-AnatomyofaMapReduceJobru n-Failures-JobScheduling-ShuffleandSort :ApplicationsonBigDataUsingPigandHive DataprocessingoperatorsinPig Hiveservices HiveQL , , IntelligentDataAnalysis ,Springer, Hadoop.
4 TheDefinitiveGuide ThirdEdition,O reillyMedia, ,DirkDeRoos,TomDeutsch,GeorgeLapis,PaulZ ikopoulos, UnderstandingBigData:AnalyticsforEnterpr iseClassHadoopandStreamingData ,McGrawHillPublishing, , MiningofMassiveDatasets ,CUP, , TamingtheBigDataTidalWave:FindingOpportu nitiesinHugeDataStreamswithAdvancedAnaly tics ,JohnWiley&sons, , MakingSenseofData ,JohnWiley&Sons, , BigDataGlossary ,O Reilly, ,MichelineKamber DataMiningConceptsandTechniques ,2ndEdition,Elsevier, ,GuoquingChen, ,GeertWets, IntelligentDataMining ,Springer, ,DirkdeRoos,KrishnanParasuraman,ThomasDe utsch,JamesGiles,DavidCorrigan, HarnessthePowerofBigDataTheIBMBigDataPla tform ,TataMcGrawHillPublications, ,VijayMadisetti, BigDataScience& ANALYTICS :AHands-OnApproa ch ,VPT, AnalyticsinaBigDataWorld:TheEssentialGui detoDataScienceanditsApplications(WILEYB igDataSeries) ,JohnWiley&Sons,2014 Outcomes:Studentswillbeableto: Workwithbigdataplatformandexplorethebigd ataanalyticstechniquesbusinessapplicatio ns.
5 Designefficientalgorithmsforminingthedat afromlargevolumes. AnalyzetheHADOOPandMapReducetechnologies associatedwithbigdataanalytics. ExploreonBigDataapplicationsUsingPigandH ive. Understandthefundamentalsofvariousbigdat aanalyticstechniques. Buildacompletebusinessdataanalyticssolut ionCA605 MACHINELEARNINGTECHNIQUESO bjectives: Tointroducethebasicconceptsandtechniques ofMachineLearning. Todeveloptheskillsinusingrecentmachinele arningsoftwareforsolvingpracticalproblem s. Tobefamiliarwithasetofwell-knownsupervis ed, ;HierarchicalClustering-Agglomerative-Di visive-Distancemeasures;DensitybasedClus tering-DBScan; , , ElementsofStatisticalLearning ,Springer, , MachineLearning ,MITP ress, , MachineLearning:AProbabilisticPerspectiv e ,MITP ress, , PatternRecognitionandMachineLearning,Spr inger , ,ShaiBen-David, UnderstandingMachineLearning:FromTheoryt oAlgorithms ,CambridgeUniversityPress, , MachineLearningForDummies ,JohnWiley&Sons, :Studentswillbeableto: Selectreal-worldapplicationsthatneedsmac hinelearningbasedsolutions.
6 Implementandapplymachinelearningalgorith ms. Selectappropriatealgorithmsforsolvingapa rticulargroupofreal-worldproblems. : Optimizebusinessdecisionsandcreatecompet itiveadvantagewithBigDataanalytics ImpartingthearchitecturalconceptsofHadoo pandintroducingmapreduceparadigm IntroducingJavaconceptsrequiredfordevelo pingmapreduceprograms7 Derivebusinessbenefitfromunstructureddat a IntroduceprogrammingtoolsPIG&HIVEinHadoo pechosystem. DevelopingBigDataapplicationsforstreamin gdatausingApacheSparkLabExercises:1.(i)P erformsettingupandInstallingHadoopinitst wooperatingmodes: Pseudodistributed, Fullydistributed.(ii) (i)Implementthefollowingfilemanagementta sksinHadoop: Addingfilesanddirectories Retrievingfiles Deletingfilesii) Findthenumberofoccurrenceofeachwordappea ringintheinputfile(s) PerformingaMapReduceJobforwordsearchcoun t(lookforspecifickeywordsinafile) : Input:oAlargetextualfilecontainingonesen tenceperlineoAsmallfilecontainingasetofs topwords(Onestopwordperline) ,whichisagoodcandidateforanalysiswithMap Reduce, : Findaverage,maxandmintemperatureforeachy earinNCDC dataset?
7 Filterthereadingsofasetbasedonvalueofthe measurement, Insteadofbreakingthesalesdownbystore,giv eusasalesbreakdownbyproductcategoryacros sallofourstoresoWhatisthevalueoftotalsal esforthefollowingcategories? Toys ConsumerElectronics Findthemonetaryvalueforthehighestindivid ualsaleforeachseparatestore8oWhatarethev aluesforthefollowingstores? Reno Toledo Chandler Findthetotalsalesvalueacrossallthestores , ,group,join,project, (AcorpusofeBooksavailableat:ProjectGuten berg) ,alter,anddropdatabases,tables,views,fun ctions, ,Deploy& ,findallthepairsofitemsfrequentlyreviewe dtogether. WriteasingleSparkapplicationthat:oTransp osestheoriginalAmazonfooddataset,obtaini ngaPairRDDofthetype:<user_id> <listoftheproduct_idsreviewedbyuser_id>oCountsthefrequenciesofallthepairsofprod uctsreviewedtogether; : Preparingfordatasummarization,query,anda nalysis.
8 Applyingdatamodellingtechniquestolargeda tasets CreatingapplicationsforBigDataanalytics Buildingacompletebusinessdataanalyticsol utionSEMESTER-IICS618 REALTIMESYSTEMSO bjectives: Tostudyissuesrelatedtothedesignandanalys isofsystemswithreal-timeconstraints. TolearnthefeaturesofRealtimeOS. TostudythevariousUniprocessorandMultipro cessorschedulingmechanisms. Tolearnaboutvariousrealtimecommunication protocols. ;Exampleofreal-timeapplications Structureofarealtimesystem Characterizationofrealtimesystemsandtask s-HardandSofttimingconstraints-DesignCha llenges-Performancemetrics-PredictionofE xecutionTime:Sourcecodeanalysis,Micro-ar chitecturelevelanalysis,Cacheandpipeline issues-ProgrammingLanguagesforReal-TimeS ystemsRealtimeOS ThreadsandTasks StructureofMicrokernel Timeservices SchedulingMechanismsCommunicationandSync hronization EventNotificationandSoftwareinterruptTas kassignmentandScheduling-Taskallocationa lgorithms-Single-processorand9 Multiprocessortaskscheduling-Clock-drive nandpriority-basedschedulingalgorithms-F aulttolerantschedulingRealTimeCommunicat ion-Networktopologiesandarchitectureissu es protocols contentionbased,tokenbased,polledbus,dea dlinebasedprotocol, Transactionpriorities Concurrencycontrolissues Diskschedulingalgorithms : , RealTimeSystems ,InternationalEdition,McGrawHillCompanie s,Inc.
9 ,NewYork,1997. , Real-TimeSystems ,PearsonEducationIndia,2000. , Real-TimeSystemsDesignandAnalysis:Toolsf orthePractitioner IVEditionIEEEP ress,Wiley,2013. SanjoyBaruah,MarkoBertogna,GiorgioButtaz zo, MultiprocessorSchedulingforReal-TimeSyst ems ,SpringerInternationalPublishing, :Studentswillbeableto: GainKnowledgeaboutSchedulabilityanalysis . LearnabouttheReal-timeprogrammingenviron ments. Attainknowledgeaboutrealtimecommunicatio nanddatabases. : ToexploretheconceptsofNoSQLD atabases. Tounderstandandusecolumnaranddistributed databasepatterns. DataWarehousingSchemes-ColumnarAlternati ve-SybaseIQ-C-StoreandVertica-ColumnData baseArchitectures-SSDandIn-MemoryDatabas es DistributedRelationalDatabases-Non-relat ional10 DistributedDatabases-MongoDB-SharingandR eplication-HBase-Cassandra-ConsistencyMo dels PostgreSQL-Riak-CouchDB-NEO4J-Redis-Futu reDatabases : AbrahamSilberschatz, , , DatabaseSystemConcepts ,SixthEdition,McGrawHill.
10 GuyHarrison, NextGenerationDatabases ,Apress,2015. EricRedmond,JimRWilson, SevenDatabasesinSevenWeeks , DanSullivan, NoSQLforMereMortals ,Addison-Wesley,2015. AdamFowler, NoSQLforDummies ,JohnWiley&Sons, :Studentswillbeableto: ExploretherelationshipbetweenBigDataandN oSQLdatabases WorkwithNoSQLdatabasestoanalyzethebigdat aforusefulbusinessapplications. : Toknowhowmodernhighperformanceprocessors areorganizedtheirstrengthsandweaknesses. Tostudyaboutthearchitectureofparallelsys tems. 'sAlgorithm-Single-SourceShortestPaths-D ijkstra' GPUC omputing CUDA , IntroductiontoHighPerformanceComputingfo rScientistsandEngineers ,Chapman&Hall, , Introductiontoparallelcomputing ,Addison-Wesley, , HighPerformanceComputing:ProgrammingandA pplications ,Chapman&Hall, , ComputerArchitecture-AQuantitativeApproa ch ,Elsevier, , ParallelProgramminginCwithMPIandOpenMP ,Indianedition,McGrawHillEducation, :Studentswillbeableto: Investigatemoderndesignstructuresofpipel inedandmultiprocessorssystems.
