MIT Data Science & Machine Learning Certificate Program

Learn to make data-driven decisions from prestigious MIT faculty by taking up the Data Science and Machine Learning program offered by Great Learning in ... AllPrograms Explore All Domains BrowsebyDomains PopularPrograms DataScience&BusinessAnalytics ArtificialIntelligence&MachineLearning Management CloudComputing CyberSecurity SoftwareDevelopment DigitalMarketing&Sales DesignThinking PopularPrograms PGPrograminDataScienceandBusinessAnalytics 6Months Online Weekend PGPrograminArtificialIntelligenceandMachineLearning 6Months Online Weekend AppliedDataScienceProgram 12Weeks LiveVirtual Weekdays&Weekend MITDataScienceandMachineLearningProgram 12weeks Online Weekend DesignThinking:FromInsightstoViability 3Months Online Weekend IITMadras:AdvancedCertificationinSoftwareEngineering 10months Online PGPrograminCloudComputing 6months Online Weekend Viewall(7) DataScience&BusinessAnalytics PGPrograminDataScienceandBusinessAnalytics 6Months Online Weekend PGPrograminDataScience&DataManagementSystems 7months Online Weekend AppliedDataScienceProgram 12Weeks LiveVirtual Weekdays&Weekend MITDataScienceandMachineLearningProgram 12weeks Online Weekend PracticalDecisionMakingUsingDataScience 6Months Online Weekends ArtificialIntelligence&MachineLearning PGPrograminArtificialIntelligenceandMachineLearning 6Months Online Weekend PGPrograminArtificialIntelligenceforLeaders 4Months Online Weekend AppliedDataScienceProgram 12Weeks LiveVirtual Weekdays&Weekend MITDataScienceandMachineLearningProgram 12weeks Online Weekend ApplicationsofAIProgram 10weeks Online ForHighSchoolers NoCodeAIandMachineLearning:BuildingDataScienceSolutions 12Weeks Online Weekend Management PGPinStrategicDigitalMarketing 6Months Online Weekend ExecutivePGPrograminManagement 12months Online Weekend NUSFutureLeadersProgram 12weeks Online Weekend Customer-CentricMarketingStrategy 12weeks Online NUSDigitalTransformation:RethinkYourBusinessGrowthProgramme 3Months Online CloudComputing IITMadras:AdvancedCertificationinSoftwareEngineering 10months Online PGPrograminCloudComputing 6months Online Weekend CyberSecurity PostGraduatePrograminCyberSecurity 16weeks Online SoftwareDevelopment IITMadras:AdvancedCertificationinSoftwareEngineering 10months Online DigitalMarketing&Sales ExecutivePGPrograminManagement 12months Online Weekend PGPinStrategicDigitalMarketing 6Months Online Weekend DesignThinking DesignThinking:FromInsightstoViability 3Months Online Weekend NUSDigitalTransformation:RethinkYourBusinessGrowthProgramme 3Months Online Knowmoreabout DataScienceandBusinessAnalytics 5programs 48%avg.salaryhike AI&MachineLearning 6programs 48%avg.salaryhike Management 5programs 48%avg.salaryhike CloudComputing 2programs 48%avg.salaryhike DesignThinking 2programs 48%avg.salaryhike CyberSecurity 1programs 48%avg.salaryhike DigitalMarketing 2programs 48%avg.salaryhike Knowmoreabout DataScienceandBusinessAnalytics 5programs 48%avg.salaryhike AI&MachineLearning 6programs 48%avg.salaryhike Management 5programs 48%avg.salaryhike CloudComputing 2programs 48%avg.salaryhike DesignThinking 2programs 48%avg.salaryhike CyberSecurity 1programs 48%avg.salaryhike DigitalMarketing 2programs 48%avg.salaryhike QuickLinks GLExcelerate Getthedesiredcareersupport WebinaronDemand Watchtheon-demandwebinars CareerSupport SuccessStories EnterpriseSolutions Login DataScienceandMachineLearning:MakingData-DrivenDecisions AdvanceyourDataScienceSkillstosolvebusinessproblemswiththishand-madeprogramforprofessionals 12Weeks LearnfromMITFaculty MentorshipfromIndustryExperts DownloadBrochure ApplyNow ApplicationsClose23rdJun2022 Enquire: +16175397216 ProgramDeliveredby: InCollaborationwith: WhyJointheDataScienceandMachineLearningProgram LearnfromrenownedMITFaculty RecordedVideoLecturesfromworld-renownedMITFaculty. Curriculumdesignedtobuildindustry-valuedskills. Read More PersonalizedMentorshipandSupport Livementorshipandguidancefromdatascienceandmachinelearningpractitionersonweekends. Collaborativeyetpersonalizedsessionsinsmallgroups. Read More Practical,Hands-onTraining Workon3industry-relevantprojectsand15+casestudies. GradedactivitiesassessmentsanddiscussionsonGreatLearningforums. Read More Recordedlecturesfrom11world-renownedMITfacultyandInstructors. Personalizedmentorshipandguidancefromdatascience&machinelearningpractitioners. Workon3industry-relevanthands-onprojectsand15+casestudies. CurriculumcoveringDataScience,MachineLearningandmore. WhoisthisProgramfor? Datascientists,dataanalysts,andprofessionalswhowishtoturnlargevolumesofdataintoactionableinsights. Earlycareerprofessionalsandseniormanagers,includingTechnicalmanagers,Businessintelligenceanalysts,ITpractitioners,Managementconsultants,andBusinessmanagers. Thosewithsomeacademic/professionaltraininginappliedmathematics/statistics.ParticipantswithoutthisexperiencewillhavetoputinextraworkandwillbeprovidedsupportbyGreatLearning. DownloadBrochure CertificateofCompletionfromMITIDSS AlumniIDSSBenefits: ExclusivediscountsoncurrentandfutureMITIDSSonlinecourseofferings SubscriptiontoIDSSnewsletterlist. MembershiptotheMITIDSSalumnimailinglistandadvancenoticeregardingupcomingcourses,programs,andevents. CertificatefromtheMITSchwarzmanCollegeofComputingandIDSS,uponparticipant’ssuccessfulcompletionoftheprogram. World#1 MITRankinWorldUniversities QSWorldUniversityRankings,2021 U.S#2 MITRankinNationalUniversities U.SNews&WorldReportRankings,2021 Note:Theimageisforillustrativepurposesonly.TheactualcertificatemaybesubjecttochangeatthediscretionofMITIDSS. Curriculum TheDataScienceandMachineLearning:MakingData-DrivenDecisionsProgramhasacurriculumcarefullycraftedbyMITfacultytoprovideyouwiththeskillsandknowledgetoapplydatasciencetechniquestohelpyoumakedata-drivendecisions. ThisDataScienceprofessionalcertificateprogramhasbeendesignedfortheneedsofdataprofessionalslookingtogrowtheircareersandenhancetheirdatascienceskillstosolvecomplexbusinessproblems.Inarelativelyshortperiod,theprogramaimstobuildyourunderstandingofmostindustry-relevanttechnologiestoday,suchasmachinelearningtodeeplearning,networkanalytics,recommendationsystems,graphneuralnetworks,andtimeseries.Hence,theprogramisbestsuitedforlearnerswithpriorexposuretoworkingwithdatausingsometoolsandapplyingbasicalgorithmsandmethods. CurriculumdesignedbyMITfacultyinDataScienceandMachineLearning BecomeaDataSciencedecisionmakerbylearningDeepLearning,MachineLearning,RecommendationSystems,andmore. TaughtinPython DownloadCurriculum Weeks1-2:FoundationsofDataScience Inthefirsttwoweeks,wewillcoverthefoundationalconceptsforDataSciencethatformthebuildingblocksofthecourseandwillhelpyousailthroughtherestofthejourneywithease. PythonforDataScience 1CaseStudy 1CaseStudy Python,forDataScientistsandMachineLearningspecialists,isalinguafrancaowingtotheimmensepromiseofthiswidely-usedprogramminglanguage.TostrengthenyourPythonfoundations,thismodulefocusesonNumPy,Pandas,andDataVisualization. Numpy NumpyisaPythonpackageforscientificcomputingthatenablesonetoworkwithmulti-dimensionalarraysandmatrices. Pandas Pandasisanopen-sourceandpowerfullibraryinPythonthatisusedtoanalyzeandmanipulatedata. DataVisualization DataVisualizationmeansdealingwiththegraphicrepresentationofdata,whicheffectivelygeneratesinsightsfromdatabyusingmatplotlib,seaborn,etc.,libraries. StatisticsforDataScience 1CaseStudy 1CaseStudy Thisweek’schapterwillhelpyouunderstandtheroleofstatisticsinhelpingorganizationsmakeeffectivedecisions,learnitsmostwidely-usedtools,andlearntosolvebusinessproblemsusinganalysis,datainterpretation,andexperiments.Itwillcoverthefollowingtopics: DescriptiveStatistics Itgivesyouthefundamentalmeasuresofastatisticalsummaryofthedata. InferentialStatistics Itwillexploretheareasofdistributionsandparameterestimation,ultimatelyallowingyoutomakeinferencesfromthedata. Week3:MakingSenseofUnstructuredData Inthisweek,youwilllearnhowtoapplydifferentMLtechniquestodiscoverpatternsandinsightsinunstructureddata. Introduction Here,youwilllearnaboutoneoftheessentialaspectsofML-UnsupervisedLearning. Whatisunsupervisedlearning,andwhyisitchallenging? Unsupervisedlearningalgorithmswillassistyouinanalyzingandclusteringunlabelleddatasets.Thischapterteachesyouaboutunsupervisedlearningandthechallengesencounteredusingthesealgorithms. Examplesofunsupervisedlearning Thischapterwillmakeyouunderstandtheimplementationofseveralunsupervisedlearningalgorithmswithexamples. Clustering 2CaseStudies 2CaseStudies Clusteringisanunsupervisedlearningtechniquetogroupsimilarsetsofdatapoints.ThenextmoduleofthecourseinDataSciencefromMITwillintroduceyoutothewidelyusedclusteringtechniques,i.e.,K-meansclustering. Whatisclustering? Here,wewilldiscussthebasicintuitionbehindclusteringandwhyitisincrediblyprevalentinnumerousindustries. Whentouseclustering? Thischapterwillteachyoutheprocedureforusingclusteringtechniques. K-meanspreliminaries ThischapterwillmakeyouunderstandafewpreliminariesbeforebeginningwithK-meansclustering. TheK-meansalgorithm TheK-meansalgorithminclusteringisoneofthemostcommonlyimplementedunsupervisedlearningalgorithmsforresolvingclusteringproblemsinDataScienceorMachineLearning. Howtoevaluateclustering? Thischapterwillmakeyoufamiliarwiththeproceduretoevaluateclustering. BeyondK-means:Whatmakesacluster? Here,youwilllearnseveraltechniquesthatmakeacluster. BeyondK-means:Othernotionsofdistance Thischapterwillfamiliarizeyouwithseveralothertypesofdistancemethodsinclusteringandteachyoutheusecasesforthesame. BeyondK-means:Dataandpre-processing Datapre-processingisatechniquetocleanrawdatatouseformachinelearningapplications.Itistheinitialandforemoststepwhenimplementingamachinelearningproject. Thischapterwilldiscusswhypre-processingisnecessaryforDataScienceandallthestepsinvolved. BeyondK-means:BigdataandnonparametricBayes Bigdataisutilizedfordetermininglargeandcomplexdatasetsthatcanbebothstructuredandunstructured.YoucanimplementbigdataforFraudProtection,MachineLearning,andProductDevelopment. ABayesiannonparametricmodelisaBayesianmodeloninfinite-dimensionalparameterspace. Beyondclustering Inthischapter,youwillunderstandallthecrucialtopicsbeyondclusteringanditsapplications. SpectralClustering,Components,andEmbeddings 2CaseStudies 2CaseStudies Spectralclusteringisoneofthemostwidelyimplementedtechniquesforclustergraphsandnetworks.Here,youwilllearnaboutspectral,modularityclustering,andthePCAalgorithm.Thismodulewilldiscussspectralclusteringanditscomponentsandembeddings. Whatifwedonothavefeaturestodescribethedataornotallaremeaningful? Thischapterwillteachyouhowtoprovideasolutionifyoudonothaveanyfeaturestodescribethedataorifnotallaremeaningful. Findingtheprincipalcomponentsindataandapplications PrincipalComponentAnalysisisamethodtoreducethecomplexityofanunsupervisedmachinelearningmodel.InLayman’sterms,PrincipalComponentAnalysisislikeeliminatingtheinputvariablesforapredictivemodeltoavoidoverfitting. ThemagicofeigenvectorsI Inthischapter,youwillunderstandtheproceduretoimplementeigenvectorsinamatrix. Clusteringingraphsandnetworks Here,youwillgainanunderstandingofclusteringingraphsandnetworks. Featuresfromgraphs:ThemagicofeigenvectorsII Here,youwillunderstandtheproceduretoimplementeigenvectorsinamatrixusingseveralfeaturesfromgraphs. Spectralclustering Spectralclusteringwillenableyoutoreducecomplexmulti-dimensionaldatasetsintoidenticaldataclustersinrarerdimensions. ModularityClustering ThemeasureofthestrengthofanetworkdivisionintoclustersiscalledModularityclustering. Embeddings:Newfeaturesandtheirmeaning Anembeddingisamoderatelylow-dimensionalspacetotranslatehigh-dimensionalvectors,whichassistsinmakingiteasiertodomachinelearningonenormousinputs. Week4:LearningbreakwithaconceptualsessiononMakingSenseofUnstructuredDataandRegressionandPrediction. Week5:RegressionandPrediction Inthisweek,youwillexploretheclassicalandmodernregressionmethodsforpredictionandinferentialpurposes. ClassicalLinearandNonlinearRegressionandExtensions 2CaseStudies 2CaseStudies Here,youwilllearnaboutlinearandnonlinearregressiontogetherwiththeirextensions,includingthecrucialcaseoflogisticregressionforbinaryclassificationandcausalinference,wherethegoalistounderstandtheeffectsofactivelymanipulatingavariableasopposedtopassivelymeasuringit. Linearregressionwithoneandseveralvariables Here,youwillunderstandtheproceduretoimplementlinearregressionwithoneandseveralvariables. Linearregressionforprediction Thischapterwillfamiliarizeyouwiththeproceduretoimplementlinearregressionforpredictiveanalysis. Linearregressionforcausalinference Thischapterwillfamiliarizeyouwiththeproceduretoimplementlinearregressionforcausalinference. Logisticandothertypesofnonlinearregression LogisticregressionisasimpleclassificationalgorithminMachineLearningthatpredictsthecategoricaldependentvariablesusingindependentvariables. ThischapterwillfamiliarizeyouwithallthefundamentalsofLogisticRegressionandothertypesofnonlinearregressioninMachineLearning. ModernRegressionwithHigh-DimensionalData 1CaseStudies 2CaseStudies InthenextmoduleofthisDataScienceforworkingprofessionalscourse,youwilllearnaboutmodernregressionwithhigh-dimensionaldataorfindinganeedleinahaystack.Forlargedatasets,itbecomesnecessarytosortoutwhichvariablesarerelevantforpredictionandwhicharenot.Recentyearshavewitnessedthedevelopmentofnewstatisticaltechniques,suchasLassoorRandomForests,thatarecomputationallysuperiortolargedatasetsandautomaticallyselectrelevantdata. Makinggoodpredictionswithhigh-dimensionaldata Thischapterwillteachyoutheprocessofmakinggoodpredictionswithhigh-dimensionaldata. Avoidingoverfittingbyvalidationandcross-validation Overfittingoccurswhenamodelover-trainsthedata.InLayman'sterms,supposeamodellearnsthedetailandnoisewithinthetrainingdata.Inthatcase,thetrainingdatawillnegativelyaffecttheperformanceofthemodelonnewdata. Thischapterwillteachyoutheprocessofavoidingoverfittingthroughvalidationandcross-validationtechniques. RegularizationbyLasso,Ridge,andtheirmodification Here,youwillunderstandregularizationbyLasso,Ridge,andtheirmodification. RegressionTrees,RandomForest,BoostedTrees RegressionTreesarebuiltusingbinaryrecursivepartitioning,aniterativeprocessthatsplitsthedataintopartitionsorbranches.Itlatersplitseachportionintosmallergroupsastheprocessadvanceseverybranch. RandomForestisaprevalentsupervisedMachineLearningalgorithmthatconstitutesnumerousdecisiontreesonthegiveninnumerablesubsetsofadataset.Later,itwillcalculatetheaveragetoenhancethedataset'spredictiveaccuracy.Boostingisameta-algorithminMachineLearning,whichtransformsrobustclassifiersfromseveralweakclassifiers. BoostingcanbedistinguishedasGradientboostingandAdaptive(ADA)boosting. TheUseofModernRegressionforCausalInference 2CaseStudies 2CaseStudies Thispartwillcoverregressionandcausalinferencetoexplainwhy“correlationdoesnotimplycausation”andhowwecanovercomethisintrinsiclimitationofregressionbyresortingtorandomizedcontrolstudiesorcontrollingforconfounding. RandomizedControlTrials ThischapterwillteachyoutheprocessofidentifyingandworkingwithRandomizedControlTrials. ObservationalStudieswithConfounding Confoundingisacommonhazardofobservationalclinicalresearchopposingrandomizedexperiments.Yet,itcaneasilypassunrecognized,althoughitsrecognitionisessentialforsignificantlyinterpretingcausalrelationships,likeevaluatingtreatmenteffects. Week6:ClassificationandHypothesisTesting Inthisweek,youwilllearnaboutthebasicsofanomalydetection,classification,andfundamentalsofhypothesistesting,whichistheformalizationofscientificinquiry.Thisdelicatestatisticalsetupobeysaspecificsetofrulesthatwillbeexplainedandputincontextwithclassification. HypothesisTestingandClassification 1CaseStudy 1CaseStudy InthismoduleoftheMITDataSciencecertificateprogram,youwilllearnHypothesistestingandseveralclassificationalgorithms.HypothesisTestingisatechniquetoperformexperimentsusingtheobserved/surveyeddata.Asthenameindicates,classificationisatechniquetoclassifyadatasetintodifferentcategoriesandcanbeperformedonbothstructuredandunstructureddata. Whatareanomalies?Whatisfraud?Spams? Anomaliesoccurwhendatabasesareinadequatelyplannedandun-normalized,whereallthedataisstoredinonetable.Fraud,asthenamesuggests,isafraudulentactwithnoauthorization.Spamisunsoliciteddigitalcommunication,suchassendingmessages,emails,etc.,tovastamountsofpeopleforcommercialpurposes. Inthischapter,youwillunderstandtheproceduretodetectanomalies,fraud,andfilterspaminMachineLearning. BinaryClassification:FalsePositive/Negative,Precision/Recall,F1-Score Binaryclassificationisasupervisedmachinelearningtechnique,wherethecategoriesarepredefinedandclassifiedintonewprobabilisticobservations.Whentherearetwocategories,itiscalledbinaryclassification. LogisticandProbitregression:Statisticalbinaryclassification Probitregressionisamethodwherethedependentvariabletakesonlytwovalues.Thischapterwilldiscussalltheessentialconcepts,likeLogisticregression,Probitregression,andStatisticalbinaryclassification. Hypothesistesting:RatioTestandNeyman-Pearsonp-values:Confidence Here,youwillgainanunderstandingofallthecriticalconceptsofhypothesistesting. Supportvectormachine:Non-statisticalclassifier SupportVectorMachine,shortenedtoSVM,isanotherpopularMachineLearningalgorithmusedforregressionandclassificationproblems. Perceptron:Simpleclassifierwithelegantinterpretation Aperceptronisanartificialneuron,orplainly,amathematicalmodelofabiologicalneuron.Thischapterwillfamiliarizeyouwithperceptronanditsvariousconcepts. Week7:LearningbreakwithaconceptualsessiononClassificationandHypothesisTesting Week8:DeepLearning Deeplearninghasemergedasadrivingforceintheongoingtechnologicalrevolution.TheessenceofDeepLearningliesinitsabilitytoimitatethehumanbraininprocessingdataforvariouspurposes,thattoowithoutanyhumansupervision.Neuralnetworksareattheheartofthistechnology.ThisweekwilltakeyoubeyondtraditionalMLandintotherealmofNeuralNetworksandDeepLearning.You’lllearnhowDeepLearningcanbesuccessfullyappliedtoareassuchasComputerVisionandmore. DeepLearning 1CaseStudy 1CaseStudy Here,thelearnerswillunderstandallthecriticalconceptsofDeepLearning,suchasimageclassification,back-propagation,transferlearning,NLP,speechrecognition,andmuchmore. Whatisimageclassification?IntroduceImageNetandshowexample Imageclassificationisafundamentalconceptindeeplearning.Itidentifiesobjectsinanimagebytrainingamodelthroughexperimentationwithlabeledimages. ThischapterwillteachyoutheprocessofidentifyingobjectsinanimageandintroduceyoutoImageNet,alongwithseveralexamples. Classificationusingasinglelinearthreshold(perceptron) Here,youwilllearntheprocessofimplementingclassificationtechniquesusingasinglelinearthreshold(perceptron). Hierarchicalrepresentations Here,youwilllearntheprocessofrepresentingdeeplearningmodelsinahierarchicalstructure. Fittingparametersusingback-propagation Inthischapter,youwilllearnhowtofindcoefficients(parameters)foroneornumerousmodelsforfittingdata. Non-convexfunctions Thischapterwillfamiliarizeyouwithnon-convexoptimizationfunctionsindeeplearning. Howinterpretableareitsfeatures? Here,youwillunderstandhowthefeaturesareinterpretable. Manipulatingdeepnets(ostrichexample) Here,youwillunderstandtheprocessofmanipulatingdeepneuralnetworksusingtheostrichexample. Transferlearning Transferlearningisawidelyimplementeddeeplearningapproach.Itisamodeldevelopedforanapplicationthatcanbereusedastheinitialpointforamodelonasecondapplication. OtherapplicationsI:Speechrecognition Speechrecognitionisatechniquetotransformhumanspeechintowrittentextbyrecognizingthevoiceofanindividual. OtherapplicationsII:Naturallanguageprocessing Naturallanguageprocessing(NLP)isatechniqueforapplyingcomputationallinguisticstobuildreal-worldapplications,whichworkwithlanguagescomprisingseveralstructures.Here,weattempttoteachacomputertolearnlanguagesandlaterexpectthecomputertoanalyzeandunderstandtheselanguagesusingsuitable,efficientalgorithms. Week9:RecommendationSystems Asorganizationsareincreasinglyleaningtowardsdata-drivenapproaches,anunderstandingofrecommendationsystemscanhelpnotonlydatascienceexpertsbutalsoprofessionalsinotherareassuchasmarketingwho,too,areexpectedtobedataliteratetoday.Learnwhyrecommendationsystemsarenoweverywhereandsomeinsightonwhatisrequiredtobuildasuitablerecommendationsystembycoveringstatisticalmodelingandalgorithms. RecommendationsandRanking 1CaseStudy 1CaseStudy RecommendationSystemalgorithms,simplyput,suggestrelevantitemstousers-explainingthetrendsoftheirusageacrossarangeofindustriesandtheircentralroleinrevenuegeneration. Whatdoesarecommendationsystemdo? Asthenameindicates,recommendationsystemsassistyouinpredictingthefuturepreferenceofanyproductandrecommendingthebest-suiteditemstousers. Inthischapter,youwillunderstandtheproceduretoutilizearecommendationsystemtochoosethebestproductsforusers. Sowhatistherecommendationpredictionproblem?Andwhatdatadowehave? Thetechniquewherethesystempredictswhetheranindividualorabusinesslikestheproduct(aclassificationproblem)orthereviewsorratingsbythem(aregressionproblem)isknownastherecommendationpredictionproblem. Usingpopulationaverages Here,youwillunderstandtheprocedureforusingpopulationaverages. Usingpopulationcomparisonsandranking Here,youwillunderstandtheprocedureforusingpopulationcomparisonsandranking. CollaborativeFiltering 1CaseStudy 1CaseStudy Collaborativefilteringisanaspectofrecommendationsystemswithwhichweinteractquitefrequently.Uponcollectingdataonthepreferencesofmultipleusers,collaborativefilteringmakespredictionsforthechoiceofaparticularuser. Personalizationusingcollaborativefilteringusingsimilarusers Here,youwillunderstandtheproceduretousecollaborativefilteringwiththehelpofsimilarusers. Personalizationusingcollaborativefilteringusingsimilaritems Here,youwillunderstandtheproceduretousecollaborativefilteringwiththehelpofsimilaritems. Personalizationusingcollaborativefilteringusingsimilarusersanditems Here,youwillunderstandtheproceduretousecollaborativefilteringwiththehelpofsimilarusersanditems. PersonalizedRecommendations 1CaseStudy 1CaseStudy Assuggestedbythenameitself,personalizedrecommendationsworktofilteroutrecommendationsthatarepersonallyrelevantforauser,basedontheirbrowsingtrends,etc. Personalizationusingcomparisons,rankings,anduseritems Here,youwilllearnhowtoutilizepersonalizationrecommendationswiththehelpofcomparisons,rankings,anduseritems. HiddenMarkovModel/NeuralNets,Bipartitegraph,andgraphicalmodel TheHiddenMarkovModel(HMM)isastatisticalMarkovmodelinwhichthesystembeingmodeledisregardedasaMarkovprocesswithhidden/unobservedstates. Usingsideinformation ThischapterwillfamiliarizeyouwiththeproceduretousesideinformationwiththeassistanceofMeta-Prod2Vec. Buildingasystem:Algorithmicandsystemchallenges Thischapterwillfamiliarizeyouwiththeproceduretomakeasystemconsideringalgorithmicandsystemchallenges. Week10:LearningbreakwithaconceptualsessiononDeepLearningandRecommendationSystems Week11:NetworkingandGraphicalModels Inthisweek,youwillgetasystematicoverviewofmethodsforanalyzinglargenetworks,determiningimportantstructuresinsuchnetworks,andinferringmissingdatainnetworks.Anemphasisisplacedongraphicalmodels,bothasapowerfulwaytomodelnetworkprocessesandtofacilitateefficientstatisticalcomputation. Introduction InthismoduleoftheMITDataSciencecourse,youwillgettoknowwhatnetworksareandhowwecanrepresentnetworkswiththeirpracticaluse-casesaroundus. Introductiontonetworks Youcandefineanetworkasagroupoftwoormorecomputersystemslinkedtogetherusingseveralhardwarecomponents,suchashubs,switches,andmore. Examplesofnetworks Inthischapter,youwillgainanunderstandingofalltheexamplesofnetworks. Representationofnetworks Thischapterwillfamiliarizeyouwiththeproceduretorepresentnetworks. Networks 1CaseStudy 1CaseStudy InthenextmoduleoftheMITDataScienceonlinecourse,youwilllearnaboutthestandarddescriptivemeasuresofanetwork,suchascentrality,closeness&betweenness,andstandardstochasticmodelsfornetworks,likeErdos-Renyi,preferentialattachment,infectionmodels,notionsofinfluence,etc. Centralitymeasures:degree,eigenvector,andpagerank Thischapterwillfamiliarizeyouwiththeproceduretoimplementcentralitymeasures,suchasdegree,eigenvector,andpagerank. Closenessandbetweennesscentrality Here,youwillgainanunderstandingofclosenessandbetweennesscentrality. Degreedistribution,clustering,andsmallworld Here,youwillgainanunderstandingofDegreedistribution,clustering,andthesmallworld. Networkmodels:Erdos-Renyi,configurationmodel,preferentialattachment TheErdos-Renyimodelassistsyouincreatingrandomnetworksorgraphsonsocialnetworking.Theconfigurationmodelisatechniquetogeneraterandomnetworksfromagivendegreesequence.Preferentialattachmentisamethodinwhichnewnetworkmembersattempttoestablishaconnectionwiththemoreprevalentexistingmembers. Stochasticmodelsonnetworksforthespreadofvirusesorideas Here,youwillgainanunderstandingofstochasticmodelsonnetworksforthespreadofvirusesorideas. Influencemaximization Theproblemofidentifyingasmallsubsetofnodes(seednodes)inasocialnetworkthatmaymaximizethespreadofinfluenceiscalledinfluencemaximization. GraphicalModels 1CaseStudy 1CaseStudy Here,youwillgettoknowhowtousegraphicalmodelstoestimateanddisplayanetworkofinteractions. Undirectedgraphicalmodels Inthischapter,youwilllearnaboutundirectedgraphicalmodels. IsingandGaussianmodels Isingmodelspecifiesthejointprobabilitydistributionofavectortounderstandphasetransitions.AGaussianmodelisatwo-dimensionalnormaldistributionoftheconcentrationinthecrosswindandverticaldirectionscenteredaroundthedownwindaxisfromtheinitialpoint. Learninggraphicalmodelsfromdata Here,youwillgainanunderstandingofseveralgraphicalmodelsfromdata. Directedgraphicalmodels Adirectedgraphicalmodelreferstotheprobabilityofrandomvariablesintoaproductofconditionalprobabilities,availableforeverynodeinthegraph. V-structures,“explainingaway,”andlearningdirectedgraphicalmodels Here,youwillunderstandmoreaboutdirectedgraphicalmodels,V-structures,and“explainingaway”. Inferenceingraphicalmodels:Marginalsandmessagepassing Thischapterwillteachyouaboutinferenceingraphicalmodels,suchasMarginalsandmessagepassing. HiddenMarkovModel(HMM) ThischapterwillbrushyourpreviousknowledgeoftheHiddenMarkovModel(HMM). Kalmanfilter TheKalmanfilteralgorithmisusedtoprovideestimatesofsomeunknownvariables,giventhemeasurementsareobservedoveraparticularperiod. Week12:PredictiveAnalytics Inthisweek,youwilllearnaboutsomepracticalexamplesoftemporaldatasourcesandhowwecanbegintounderstandthem.Then,youwilldiveintoseveralstrategiesforfeatureextraction,includingDeepFeatureSynthesiswithprimitivesandstacking.Finally,youwilllooktowardmodelsfortherealworldandhowtoensuretheysuccessfullypredictfuturedata. PredictiveModelingforTemporalData 1CaseStudy 1CaseStudy Predictivemodelingisthetechniqueofutilizingpreviousresultsforcreating,processing,andvalidatingamodel,whichcaneventuallybeusedtomakefuturepredictions.Here,youwilllearnaboutthestructureoftemporaldataandhowwecanclearlydefinetraininginputsandoutputsforprediction. PredictionEngineering Predictionengineeringisthetechniqueofgeneratingtrainingexamplesfromexistingdatatotrainamachinelearningmodelforfuturepredictions. FeatureEngineering 1CaseStudy 1CaseStudy Inthispart,youwillknowhowtoutilizefeatureengineeringtechniquestoextractmeaningfulinsightsfromtemporaldata;whatareeffectivestrategiesforevaluatingmodelperformanceandpreparingtodeployitintherealworld? Introduction Thischapterwillintroducelearnerstofeatureengineering,atechniquetotransformdatafromtherawstatetoanappropriatestateformodeling.Itassistsintransformingthedatacolumnsintofeaturesthatbetterrepresentagivensituationintermsofclarity. FeatureTypes Thischapterwillfamiliarizeyouwiththreefeaturetypes:quantitative,ordinal,andcategorical. DeepFeatureSynthesis:PrimitivesandAlgorithms DeepFeatureSynthesis,shortenedtoDFS,isanautomatedprocessthatexecutesfeatureengineeringonrelationalandtemporaldata. ThischapterwillteachyouaboutprimitivesandalgorithmsinvolvedinDFS. DeepFeatureSynthesis:Stacking ThischapterwillteachyouaboutstackinginDFS. CertificateofCompletionfromMITIDSS Uponsuccessfulcompletionoftheprogram,youwillreceiveoneofthebestprofessionalcertificatesinDataScience,foritwillbefromMITInstituteforData,Systems,andSociety(IDSS). ProjectsandCaseStudies Followinga“learnbydoing”pedagogy,theDataScienceandMachineLearningProgramoffersyoutheopportunitytoconstructyourunderstandingthroughsolvingreal-worldcasestudiesandpracticeactivities. Belowaresamplesofpotentialprojecttopicsandcasestudies. Healthcare PimaIndiansDiabetes AreaofProject ExploratoryDataAnalysis SmallSummary AnalyzethedifferentaspectsofDiabetesinthePimaIndianstribe. Tools&Techniquesused: Python,EDA,DescriptiveStatisticsetc. Learnmore Entertainment MoviesRecommendationSystem AreaofProject RecommendationSystems SmallSummary BuildyourownrecommendationsystemthatcanrecommendthebestmoviestoauserliketheoneusedbyNetflix. Tools&Techniquesused: Python,Contentbasedalgorithms,CollaborativeFiltering,Popularityrecommendations,etc. Learnmore Transportation NYCTaxiTrips AreaofProject PredictiveAnalytics SmallSummary Topredictthetripdurationofanewyorktaxicabride,builddifferenttypesoffeaturesandevaluatethem. Tools&Techniquesused: Python,Regression,FeatureEngineering,etc. Learnmore Research PredictingWages AreaofProject Regression&Prediction SmallSummary Predictwagesandassesspredictiveperformanceusingvariouscharacteristicsofworkers. Tools&Techniquesused: Python,Regression,etc. Learnmore Media GroupingNewsStories AreaofProject Clustering SmallSummary Buildyourownclusteringforonlinenewsstories—similartohowGoogleNewsorganizesstoriesviaauto-generatedtopics. Tools&Techniquesused: Python,Clustering,NLP,etc. Learnmore Space TheChallengerDisaster AreaofProject ClassificationandHypothesisTesting SmallSummary Estimatethelikelihoodoffailureoftheequipmentinarocketpostthelaunch. Tools&Techniquesused: Python,Classification,Hypothesistesting,etc. Learnmore Manufacturing Decisionboundaryofadeepneuralnetwork AreaofProject DeepLearning SmallSummary Playwithoneortwolayerperceptronstoassesstheirdecisionboundaries. Tools&Techniquesused: Python,NeuralNetworks,etc. Learnmore Healthcare IdentifyingnewGenesthatcauseAutism AreaofProject NetworkingandGraphicalModels SmallSummary Usenetwork-theoreticideastoidentifynewcandidategenesthatmightcauseautism. Tools&Techniquesused: Python,Networks,GraphicalModels,etc. Learnmore MITFacultyandIndustryExperts LearnfromthevastknowledgeoftopMITfacultyinthefieldofDataScienceandMachineLearning,alongwithexperienceddatascienceandmachinelearningpractitionersfromleadingglobalorganizations. ProgramFaculty AnkurMoitra RockwellInternationalCareerDevelopmentAssociateProfessor,MathematicsandIDSS,MIT CarolineUhler HenryL.&GraceDohertyAssociateProfessor,EECSandIDSS,MIT DavidGamarnik NanyangTechnologicalUniversityProfessorofOperationsResearch,SloanSchoolofManagementandIDSS,MIT DevavratShah Professor,EECSandIDSS,MIT GuyBresler AssociateProfessor,EECSandIDSS,MIT JonathanKelner Professor,Mathematics,MIT KalyanVeeramachaneni PrincipalResearchScientistattheLaboratoryforInformationandDecisionSystems,MIT. PhilippeRigollet Professor,MathematicsandIDSS,MIT StefanieJegelka X-ConsortiumCareerDevelopmentAssociateProfessor,EECSandIDSS,MIT TamaraBroderick AssociateProfessor,EECSandIDSS,MIT VictorChernozhukov Professor,EconomicsandIDSS,MIT PrevNext ProgramMentors MatthewNickens Manager,PartnershipScience Meta BhaskarjitSarmah DataScientist BlackRock BradfordTuckfield SeniorManager,DataScience CharlesSchwab OdaiyappanPichappan SeniorDataScientist Verizon4GWireless UditMehrotra DataScientist Dell ShirishGupta LeadDataScientist Novartis VaibhavVerdhan AnalyticsLeader,GlobalAdvancedAnalytics AstraZeneca PrevNext YourLearningExperience TheDataScienceandMachineLearning:MakingData-DrivenDecisionsProgramisdistinguishedbyitsuniquecombinationofMITacademicleadership,recordedlecturesbyMITfaculty,anapplication-basedpedagogy,andpersonalizedmentorshipfromindustryexperts. LEARNWITHMITFACULTY LearnDataScienceandMachineLearningwithMITFaculty Self-pacedprogramwithrecordedlecturesfromMITfacultyinDataScience&MachineLearning. Programcurriculumanddesignbyworld-renownedMITfaculty. Positionyourselfasadatascienceleaderbygainingindustry-valuedskills. PERSONALIZEDANDINTERACTIVE PersonalizedMentorshipandSupport WeeklyonlinementorshipfromDataScienceandMachineLearningexperts. Smallgroupsoflearnersforpersonalizedguidanceandsupport. Interactwithlike-mindedpeersfromdiversebackgroundsandgeographies. DedicatedProgramManagerprovidedbyGreatLearning,foracademicandnon-academicqueries. ViewExperience PRACTICALANDHANDS-ON BuildyourDataScienceandMachineLearningPortfolio DemonstrateDataScienceleadershipbybuildingaportfolioof3industry-relevantprojectsand15+casestudies. Learnviapracticalapplicationstounderstandhowdatascienceandmachinelearningconceptstranslateintotherealworld. WhyOurLearnersChoosetheDataScienceandMachineLearningProgram ThisisthefirststepinwhatIhopetobeaveryfruitfuladditiontomycareerinbehavioraleconomicsinhealthpromotionandnutritionsciences.Istronglyrecommendthisprogramtootherswhoarelookingtolearnmoreabouthowtointegratedatascienceandmachinelearningintoyourfieldofstudy.It’scompletelyworthit. MonicaPampell HealthscienceAnalyst,FoodandDrugAdministration,USA Thecombinationofthecurriculumandrelevantfacultytodeliveritwasamajorstrokefortheprogram,IreallyenjoyedthemodulesNeuralNetworkandDeepLearningwhereasrecommendationsystemsandPredictiveAnalyticswaschallengingandexcitingatthesametime. KaaseGbakon SeniorAnalyst,MinistryofEnergyandResource,GovernmentofSasketchewan PrevNext LearnerTestimonials MyexposuretoDataScienceandMachineLearningProgramwasexceptional.Theprofessorswerephenomenal.Theywerepatientandsupportedourlearningwithmentoringsessionsandampleonlinevideoeducationalsessionsthroughoutthecourse. LanettaBronte-Hall PresidentandCEO ,FoundationforSickleCellDiseaseResearch Beinganonlineprogram,onecanhaveallthebenefitsoftheflexiblestudyandinstantaccesstotherichresourcesthroughtheadvancedlearningplatform. RanaRisheh Developer-DirectorandQualitySupervisor ,Knowlgica Theinstructorsandcoordinatorswereamazing,theyalwaysansweredmyquestionsandclearedmydoubts.Iamalreadyusingunsupervisedmachinelearningalgorithmsinmyroboticsprojects. SalmanSiddiqui ControlSystemIntegrator ,Cherkam Sincerely,Ihavetakenseveralprograms,buttheexperiencefromthisprogramissimplydifferentfrommulti-dimensionalperspectives.IwouldrecommendthisprogramagainandagaintoprofessionalswhowouldliketoupgradetheirskillsinDataScienceandMachineLearning. OluwarotimiWilliamsSamuel ResearchScientist ,ShenzhenInstituteofAdvancedTechnology PrevNext Ratings&Reviewsbylearners Allreviews(6) AlexBramahLawani 30Jan2022 BatchofJanuary2022 |UniversityLecturer atAccraInstituteOfTechnology |Ghana Thelectureswereabsolutelywonderfulandeverythingwasonpoint.Hisinteractionwiththeclasswasgreatandhismodeofteachingcouldsatisfyalllevelsoflearners,fromabsolutebeginnerstoexperts. RicardoAlvaMaya 19Jan2022 BatchofJanuary2022 |EnterpriseApplicationsDeliveryLeader(Sap,Oracle,Ms)AtIbm atIbm |Mexico Ireallybelievetheexplanationwascompleteandclearinallaspectsandgoodtimeanddedicationwereprovidedbythepresentertosolvedoubts. Thanksalot KendraLai 23Jan2022 BatchofDecember2021 |Singapore Teachingpaceisjustnice,abletoanswerraisedquestionpreciselyandlinktheusecasewiththerealpracticalindustryexpectation. Readallreviews ProgramFees DataScienceandMachineLearning:MakingData-DrivenDecisions USD1900 EasyEMIoptionavailable ViewPlans RecordedlecturesfromworldrenownedMITFaculty LiveMentorshipfromDataScience&MachineLearningExperts 3industry-relevantprojectsand15+real-worldcasestudies ProgramManagerfromGreatLearningforAcademic&Non-AcademicSupport Apply Now Apply Now CandidatescanpaythecoursefeethroughCredit/DebitCardsandBankTransfer.Forfurtherdetails,pleasegetintouchwithouradmissionsteam. ContactUs ApplicationProcess Step1:Filltheapplicationform Registerbycompletingthe onlineapplicationform. Step2:ApplicationScreening Yourapplicationwillbereviewedtodetermineifitisafitwiththeprogram. Step3:JointheProgram Ifselected,youwillreceiveanofferfortheupcomingcohort.Secureyourseatbypayingthefee. UpcomingApplicationDeadline Admissionsareclosedoncetherequisitenumberofparticipants enrollfortheupcomingcohort.Applyearlytosecureyourseat. Deadline:23rdJun2022 Apply Now Reachouttous Wehopeyouhadagoodexperiencewithus.Ifyouhaven’treceivedasatisfactoryresponsetoyourqueriesorhaveanyotherissuetoaddress,pleaseemailusat [email protected] CohortStartDates Online Tobeannounced FrequentlyAskedQuestions ProgramDetails Istheprogramcompletelyvirtual? Yes,theprogramhasbeendesignedkeepinginmindtheneedsofworkingprofessionals.Thus,youcanlearnthepracticalapplicationsofdatascienceandmachinelearningfromtheconvenienceofyourhomeandwithinanefficient10-weekduration. Whatistherequiredweeklytimecommitment? Eachweekinvolves2hoursofrecordedlecturesandanadditional2hourshands-onsessioneachweekendfor7weekends,whichincludehands-onpracticalapplicationsandproblem-solving.Additionally,basedonyourbackground,youshouldexpecttoinvestbetween2to4hourseveryweektoself-studyandpractice.So,thatamountstoatimecommitmentof8-12hoursperweek. WillIhavetospendextraonbooks,virtuallearningmaterials,orlicensefee? No.AlltherequisitelearningmaterialisprovidedonlinetocandidatesthroughtheLearningManagementSystem. Butgiventhisfieldisvastandeverexpanding,thereisalwaysmoreyoucanreadandtherewillbealistofrecommendedbooksandotherresourcesforyourdeepdivereadingpleasure WillIreceiveatranscriptorgradesheetaftercompletionoftheprogram? No,DataScienceandMachineLearning:MakingData-DrivenDecisionsisanonlineprofessionalcertificationprogramofferedbyMIT-IDSS(InstituteforData,Systems,andSociety)incollaborationwithGreatLearning.Sinceitisnotadegree/full-timeprogramofferedbytheuniversity,therefore,therearenogradesheetsortranscriptsforthisprogrambytheuniversity.Youwillreceivemarksoneachassessmenttotestyourunderstandingandmarksoneachmoduletodetermineyoureligibilityforthecertificate.  Uponsuccessfulcompletionoftheprogram,i.e.aftercompletingallthemodulesaspertheeligibilityofthecertificate,youareissuedacertificatefromtheMITSchwarzmanCollegeofComputingandIDSS. FeeandPayment Whataremypaymentoptions? CandidatescanpaythecoursefeethroughBankTransferandCredit/DebitCards.Forfurtherdetails,[email protected]. Canmyemployersponsortheprogramfee? Weacceptcorporatesponsorshipsandcanassistyouwiththeprocess.Formoreinformation,[email protected]. WhatistheRefundPolicy? Pleasenotethatsubmittingtheadmissionfeedoesconstituteenrollingintheprogramandthebelowcancellationpenaltieswillbeapplied.Ifyouareunabletoattendyourprogram,pleasereviewourdropout,andrefundpoliciesbelow. Dropoutrequestsreceivedwithin7daysofenrollmentandmorethan42dayspriortothecommencementoftheprogramwillincurnofee.Anypaymentreceivedwillberefundedinfull. Dropoutrequestsreceivedmorethan42dayspriortotheprogrambutmorethan7daysaftertheacceptancearesubjecttoacancellationfeeof250USD Dropoutrequestsreceived22-41dayspriortothecommencementoftheprogramaresubjecttoacancellationfeeequalto50%oftheprogramfee. Anydropoutrequestsreceivedfewerthan22dayspriortothecommencementoftheprogramaresubjecttoacancellationfeeequalto100%oftheprogramfee. Norefundwillbemadetothosewhodonotengageintheprogramorleavebeforecompletingaprogramforwhichtheyhavebeenregistered. ApplicationProcessandEligibility Whoiseligiblefortheprogram? Theprogramisdesignedtocoverabreadthofindustry-relevanttopicsinarelativelyshortspanoftime.Giventhis,itisimportantforlearnerstohavepriorexperienceofworkingwithdatasothattheycanextractmostfromthe12-weekprogram.Theprogramis,thus,forthosewithPriorcontextandsomeacademic/professionaltraininginappliedmathematics/statisticstoworkwithdataandmakesenseofthedata.Participantswhodonothavethisexperiencewillhavetoputinextraworktobeabletolearneffectively;GreatLearningwillprovidethenecessarysupporttosuchparticipants. WhatistheApplicationprocess? Youwillneedtocompleteyouronlineapplicationform.Onreceivingtheapplication,theGreatLearningprogramteamwillreviewittodetermineyourfitwiththeprogram.Ifselected,youwillreceiveanofferfortheupcomingcohort.Secureyourseatbypayingthefee. WhyDataScienceandMachineLearning WhyDataScienceandMachineLearning? Theadvancementoftechnologyinvariousfieldscontributesgreatlytothegrowthofindustries.Hence,manybusinessesareusingadvancedMachineLearningandDataScienceapplicationstodrawthebestoutcomes.Letusunderstandafewofthebenefitsoftheseboomingdomainsoftechnology.   Buildingbetterbusinessstrategies ByemployingDataScienceandMachinelearning,organizationscandevelopthebestbusinessplan.DataScienceandMachinelearningprovidesolutionstocomeupwiththebestbusinessplanthatsupportscompanies'exponentialgrowth.Today,mostofthetop-notchcompaniesareapplyingDataScienceandMachinelearninginprojectsandoperationmanagementtoobtainbetteroutcomes.   BetterResearchandInventions Organizationsmustbeconsciousofthelatesttrendsintheirmarket.Adata-drivenbusinessteamwouldshapetheirbusinessinthebestwaythatsuitstherequirementsofendcustomers.Adata-drivenorganizationwouldlearncurrenttechnologicaltrends,planabusinessstrategythatdeliversthebestservices.Businesseswithagoodvisionandwellversedindatacancomposegroundbreakingsolutions.Data-backedapproachesassistbusinessestoaddvaluetotheirproductsbyadaptingthemselvestothelatesttrendsinthemarketbyincorporatingthelatesttechnology.    CostReduction CostreductionisoneofthemajorbenefitsthatDataScienceandMachineLearningcontributetoanybusiness.Smallandmediumscalecertainlystrivefortheirenduranceconsideringtheirlimitedbudgetandresources.DataScienceandMachineLearninghelpinformulatingbusinesssolutionsthatarecost-effective. Therefore,takingupacertificationcourseindatascienceandmachinelearningwouldfetchyouthebestcareeropportunitieswithseveralindustriesinthemarket. Whatisdatascience? Datascienceisafieldofstudythatemploysascientificapproachtoextractmeaningfulinsightsfromdata.Datascienceisafieldthatfunctionsatmorethanonelevel.Meaningfulinsightsaredrawnfromdatasets,producingknowledgethathelpsinrecommendingaptactionsforbusinessgrowth.Theknowledgederivedfromdatasciencebeingatplayisacombinationoftechnology,statisticsandtrendsinthebusinessdomain. Whatismachinelearning? Machinelearningreferstoagroupoftechniquesusedbydatascientiststhatallowcomputerstolearnfromdata.ItistheunderlyingprocessallowingmachinestolearnfromdatawhichresultsinyougettingallyourrecommendationsandpredictionsfromAlexa.Fromleisuretowork,ourlivesaremadeeasierwithmachinelearning.  Theresponsibilitiesofamachinelearningspecialistconstituteaspectrumextendingfromthecreationofmachinelearningmodelstoretrainingsystems.AspecializationinmachinelearningmeansacquiringthenecessarytoolsandtechniquesforthemostcrucialAIsubset.Aholisticskillsethere,therefore,ismadeupofexceptionaltechnicalskillsaswellasaninherentlearningattitude.  WhatisthefutureofDataScience? Organizations,onunlockingthepotentialofdatascience,havebeenknowntoseeincreasedefficiencyonseveralfronts.Theseincludefindingwaystoreducecosts,expandingintonewmarkets,tappingondifferentdemographics,analysingtheeffectivenessofmarketingcampaignsandultimately,decidingonthenewproductsandservicesthatcanbelaunched.ThisresonatedinGartnerpredictingthat,“By2022,90%ofcorporatestrategieswillexplicitlymentioninformationasacriticalenterpriseassetandanalyticsasanessentialcompetency.” Whatisthedemandfordatascientists? DataScienceroleshavebeenamongthemostin-demandjobrolesinrecentyears.AccordingtoLinkedIn,hiringfordatascientistssawanincreaseof46%inthelastyear.TheU.SBureauofLaborStatisticshasalsopredictedthatthedemandfordatascientistsisfurtherexpectedtorise27.9%by2026.  So,wearewellwithintheageofBigDataandthedecisionmakingthatitdrivesacrossindustriesforbusinessgrowth.FromenhancingleisureexperiencessuchasfilteringNetflixrecommendationstosuggestingadirectionforlegalpolicies,DataScienceandMachineLearningareatthecoreofitall.Bymakingholisticconceptsnowmeasurableandpredictingsomethingasgrandas“howpeoplewilllive,”datascienceshineswithimmensepossibilities.Yet,allthisdata,withitsunparalleledpotential,isoflittleuseunlesstherightmindsareatworktoprocessit.Employersworldwidethusrealizetheimportanceofdatascientistsandmachinelearningspecialistsandevenencouragea“cultureofanalytics”thatweseeemerginginworkplacestoday HowtobecomeaDataScientist? Agoodacademicbackgroundcanalwaysbeapluswhenyouareembarkingonyourjourneybuttherearecertainspecificskillsthatareessentialtomasterthetoolsandtechniques.Therightcombinationoftechnicalandnon-technicalskillsisimperativetochiseloutacareerinDataScienceandMachineLearning.  Revisitingyourmathematicalandstatisticalabilitiescanbeagoodmotivatortofurtheryourjourneyintoadatascienceandmachinelearningspecialization.Whileyoumustbeexperiencedwithprogramming,hands-onexperiencewithPython,R,etc.willmarkthebeginningofyourtransitionintodatascience.Effectivedatascienceandmachinelearningcoursewillnotonlycommencewithfulfillingtheserequirementsforyoubutshouldbebuilttotranslateyourlearningsintoasuccessfulcareerwithacomprehensivecurriculum,alsohighlightingtheroleofaprofessionalcertificateindatascienceandmachinelearning. WhatcodingskillsareneededtobeaDataScientist? Complexalgorithmsandsophisticatedtoolsmakeupalargepartofadatascientist'sday.Inadditiontodataanalysistools,keepingupwiththelatesttoolsindataacquisition,datacleansing,datawarehousinganddatavisualizationisbecomingincreasinglyimportantasthehistoricallyseparaterolesofdatascientistandanalystbecomemergedforincreasedefficiency.PythonisthelinguafrancaofdatasciencebutknowledgeofR,SAS,SQL,andsometimesJava,Scala,Juliaamongothersmustalsobeacquiredatthefoundationallevelitself.Technicalsoundnessisamustformovingforwardtowardssolutionswhileavoidingroadblocks. HowmuchsalarycanaDataScientistandMachineLearningSpecialistearn? Varyingdataresonateswithonefact:TheaveragesalaryofaDataScientistandaMachineLearningspecialistiswelloverUSD100,000.IndeedrecordedanaverageannualsalaryofUSD142,858forMachineLearningspecialistsandUSD126,927forDataScientistsintheUS.  Thelistofindustriesincorporatingdatascienceandmachinelearningonlycontinuestogrow.ThespecializationcommandsauthorityacrossprocessesinvolvedinHealthcare,Cybersecurity,Banking,OilandGas,Transportation,Education,TalentAcquisition,InventoryManagement,RecommendationSystemsandPriceOptimizationamongotherkeybusinessinsights.Withitsinherentlyadaptivenature,theworldofdatascienceandanalyticsisheretostayandthosemasteringthetoolsandtechniquesofitsstandtoadvancetheircareerswhilebeingattheforefrontofallinnovation. Stillhavequeries?ContactUs PleasefillintheformandaProgramAdvisorfromGreatLearningwillreachouttoyou.Youcanalsoreachouttousat [email protected] +16175397216 ApplicationsClose23rdJun2022 DownloadBrochure Checkouttheprogramandfeedetailsinourbrochure Oops!!Somethingwentwrong,Pleasetryagain. Name Email MobileNumber Workexperienceinyears Selectnumberofyears Currentlyacollegestudent 0Years Lessthan1Year 1-2Years 2-3Years 3-5Years 5-8Years 8-12Years 12-15Years Morethan15Years Bysubmittingtheform,youagreeto GreatLearning's TermsandConditionsand PrivacyPolicy. YouagreetoreceivecommunicationsfromMITIDSS&GreatLearningaboutthisprogramandotherrelevantprograms. Submit Formsubmittedsuccessfully Thankyouforreachingouttous.Youcanexpecttohearfromusin1working day. 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BrowseRelatedBlogs TopDataScientistSkillsYouMustHave LearnMore> WhatisDataScience?AllYouNeedtoKnowAboutTheDomainAndIndustry LearnMore> TopAnalyticsCompaniesinIndia LearnMore> DataScienceJobs LearnMore> BusinessAnalyticsCareers LearnMore> DataAnalyticsStartups LearnMore> DifferencebetweenDataScience,MLandAI LearnMore> DataScienceInterviewQuestions LearnMore> TopDataScienceandAnalyticsStartups LearnMore> WhatisDataScience LearnMore> PrevNext Apply Now Curriculum Projects Faculty Benefits Fees Apply Now DownloadBrochure × wistiavideo DownloadBrochure ChatWithUs × Wanttogetmoredetailsaboutourprogram? 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IntroductiontotheDataScience&MachineLearningCoursefromMITforWorkingProfessionals NumerousprofessionalcoursesareavailableacrosstheglobeforDataScienceandMachineLearning.Yet,thereareseveralreasonsforworkingprofessionalstoregisterinthisMachineLearningandDataScienceprofessionalcertificateprogramfromMITIDSS,collaboratingwithGreatLearning.Thereasonsaredraftedbelow: MITisanabbreviationoftheMassachusettsInstituteofTechnology,oneoftheworld'shighest-rankedinstitutions. AccordingtorankingsbyQSWorldUniversityRankings2022,MIThasranked#1universityglobally,andaccordingtorankingsbytheU.S.NewsandWorldReport2022,MITisranked#2intheworld. TheobjectiveofMITIDSSistoextendeducationandresearchinstate-of-the-artanalyticaltechniquesinstatisticsanddatascience,informationanddecisionsystems,andthesocialsciences,andtoapplythesetechniquestoaddresscomplexsocietalchallengesinamiscellaneoussetofareaslikefinance,urbanization,socialnetworks,energysystems,andhealth. BenefitsofPursuingMITDataScienceCertificateCourse PursuetheMITDataSciencecertificatecourseandlearnthesecutting-edgetechnologiesfrom11award-winningMITfacultyandinstructors. Theseaward-winningMITfacultymembershavedesignedthecurriculumtobuildindustry-valuedskills. YoucandemonstrateyourDataScienceandMachineLearningLeadershipbycreatingaportfolioof15+casestudiesand3real-lifeprojects. YouwillworkinarobustcollaborativeenvironmenttocommunicatewithpeersinDataScienceandMachineLearning. ObtainlivementorshipsessionsandguidancefromMachineLearningandDataScienceprofessionalsonapplyingconceptstaughtbythefaculties. AlumniIDSSBenefits HaveaglanceatthebenefitsofferedbyIDSSalumni: ParticipantscanobtainexclusivediscountsonpresentandfutureonlinecoursesofferedbyMITIDSS. ParticipantscanacquireasubscriptiontotheIDSSnewsletterlist.  ParticipantscanacquiremembershiptotheMITIDSSalumnimailinglistandadvancenoticeconcerningupcomingcourses,programs,andevents.  ParticipantscanearnacertificatefromtheMITSchwarzmanCollegeofComputingandIDSSaftertheparticipantcompletetheprogram. DetailsaboutMITDataScienceCourse InthiscomprehensiveMITDataScienceonlinecourse,theparticipantswillgraspallthecriticalskillsrequiredtomasterDataScienceandMachineLearning.Let’sgothroughtheextensivedetailsaboutthecourseinDataScienceforworkingprofessionals: CourseLearnings: ObtainanunderstandingoftheintricaciesofDataSciencetools,techniques,andtheirsignificancetoreal-worldproblems. LearntheproceduretoimplementseveralMachineLearningtechniquesforsolvingcomplexproblemsandmakingdata-drivenbusinessdecisions. ExploretwonoteworthyrealmsofMachineLearning,DeepLearning&NeuralNetworks,andlearnhowtoapplythesetechniquestoareaslikeComputerVision. Choosetheprocessofrepresentingyourdatawhilemakingpredictions. Obtainanunderstandingofthetheorybehindrecommendationsystemsandanalyzetheirapplicationstonumerousindustriesandbusinesscontexts. Learnthemethodtocreateanindustry-readyportfolioofprojectsfordemonstratingyourabilitytoderivebusinessinsightsfromdata. CourseSyllabus: ItcommenceswiththefundamentalsofPythonprogramminglanguage(NumPy,Pandas,andDataVisualization)andStatisticsforDataScience. Afterwards,participantswilllearnMachineLearningtechniques,includingSupervisedandUnsupervisedLearningTechniques,Clustering,Regression,DecisionTrees,RandomForests,ClassificationandHypothesisTesting,andseveralotheralgorithms. Movingforward,participantswilllearnDeepLearning,RecommendationSystems,Networking&GraphicalModels,PredictiveAnalysis,andFeatureEngineering. [ExploreMITDataScienceCourseSyllabus] CourseEligibility: Workingprofessionals,suchasearly-careerprofessionalsorseniormanagerswhowanttopursueacareerinDataScienceandMachineLearning WorkingprofessionalslikeDataScientists,DataAnalysts,orMLEngineersinterestedinleadingDataScienceandMachineLearninginitiativesattheirfirmsorbusinesses EntrepreneursinterestedininnovationwiththeassistanceofDataScienceandMachineLearningtechniques MITDataScienceforWorkingProfessionalsCourseDuration Thisprofessionalcourseisfor10weekswithrecordedlecturesfromaward-winning,world-renownedMITfacultymembersandlivementorshipsessionsfromindustryexperts. MITDataScienceCourseFees ThisprofessionalcoursecostsUSD1700,whichtheparticipantscanpaythroughinstallments.TheparticipantscanmaketheirpaymentsthroughCredit/DebitCardsorBanktransfers.Forfurtherinformation,pleasegetintouchwithouradmissionsteam. SecureaDataScienceProfessionalCertificate,alongwithMachineLearningfromMITIDSS Aftersuccessfullypursuingthiscourse,youwillsecureaprofessionalcertificateinDataScienceandMachineLearning:MakingData-DrivenDecisionsfromMITIDSS. Readmore GreatLearning DataScienceCourses MITDataScienceandMachineLearningProgram Heythere!Welcomeback. Youarealreadyregistered.Pleaselogininstead. Forgotyourpassword?Noproblem. × Youarealreadyregistered.Pleaselogininstead. Login Forgot Password? Login ForgotPassword? Enteryourregisteredemailandwe'llsendyoualinktochangeyourpassword. Pleaseenter avalidemailaddress Send passwordreset link Backto login