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TheTechnischeUniversitätBerlinFacultyIVElectricalEngineeringandComputerScienceTheDataAnalyticsTrack:AGuidanceDocumentJuanSoto,Dr.RalfDetlefKutsche,andProf.Dr.VolkerMarklDatabaseSystemsandInformationManagement(DIMA)GroupLastUpdated:October10,2017Email:[email protected]. In Fall 2013, TU Berlin’s Faculty IV Electrical Engineering and Computer Science (EECS)approvedanewtrack,whichenablesstudentspursingaM.Sc.inComputerScience,InformationSystemsManagementorComputerEngineering,tospecializeindataanalytics.Tomeetthetrackrequirements,studentsmustcompletecourses in three core competencies:(1)scalabledataanalytics,(2)scalabledatamanagement,and(3)adomain-specificapplicationarea.Thisguidancedocumentoffersstudentsgeneraladvice, intheselectionofcourses,proceduretofollowwhen identifyingathesistopic,andprospectivecareerpossibilities.StudentswhocompleteboththeirrespectiveM.Sc.degreeandtrackrequirements,willreceive–besidestheirM.Sc.degree–aDataAnalyticsMaster’sTrackCertificateissuedbyFacultyIV,inaddition.1. Motivation1Thelastdecadesweremarkedbythedigitizationofvirtuallyallaspectsofourdailylives.Today,industry,governmentinstitutionsandNGOs,and,ofcourse,scienceandengineeringfaceanavalancheofdigitaldatadaily.Partiallyduetoareductionindiskstoragecosts,aparadigmshifttowardscloudstorageservices, and the ubiquitous availability of networked devices. At first glance, this appears to befavorableforourincreasinglynetworkedsociety.However,inmanywaysitisaburden.Data(oftenappearingas‘rawdata’)isneitherinformation,norknowledge.Nonetheless,dataisofgreatvalue, once it has been refined and analyzed, to address well-formulated questions, concerningproblemsofinterest.Itisonlythenthateconomicandsocialbenefitscanbefullyrealized.Modernbigdataanalyticsquestionsareoftensolvedusingtechniquesdrawnfromvaryingfields,includinggraphandnetworkanalysis,machinelearning,mathematics,statistics,signalprocessing,andtextprocessing,amongothers.Currently,datascientists,wellversedinscalabledataanalysismethods,scalablesystemsprogramming,andknowledgeinanapplicationdomainareneededtoderiveinsightfrombigdata.Unfortunately,datascientistswithskillsinbothscalablesystemsand(potentiallydomainspecific)dataanalysismethodsarefewinnumber.Theyareexpensiveandinhigh-demand.Consequently,thislimitstheamountofvaluethatcancurrentlybegeneratedfrombigdataforsocietyasawhole.Moreover,despitetheever-increasingnumberofdata scienceprograms (atuniversitiesworldwide)andstudentenrollments, itwillstillbeimpossibletoeducatetheseso-calledJack-of-all-trades,astherequiredskillsarebothcomplexanddiverse(asdepictedinFigures1and2).Beforebigdata,only fewprogrammerswithMPIexpertise,predominantlylocatedinsupercomputingcentersweresufficientinnumber.Formanydecades,softwareengineersandgeneralusersinvaryingdomainsdidnothaveto worry about scalability issues in their computing systems, thanks in part to higher-levelprogramminglanguages,compilers,anddatabasesystems. 1ThemotivationsectionwaspredominantlydrawnfromProf.VolkerMarkl[1,2].
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Incontrast,today’sexisting technologieshavereachedtheirlimitsduetobigdatarequirements,whichinvolvedatavolume,datarateandheterogeneity,andthecomplexityoftheanalytics.Indeed,theneedformoreadvancedanalyticswillgobeyondrelationalalgebra.Theywillneedtoemploycomplexuser-definedfunctionsandsupportbothiterationsanddistributedstate.
Figure1.AdatascientistisaJack-of-All-Trades.Intheeraofmany-coreprocessors,cloudcomputing,andNoSQL,wemustensurethatwell-establisheddeclarativelanguageconcepts(inherentinrelationaldatabasesystems)maketheirwayintobigdatasystems.Tomakethisareality,theresearchcommunitywillneedtoaddresstherelatedchallenges.Forexample, (i) designing a programming language specification that does not require systemsprogramming skills, (ii)mappingprogramsexpressed in thisprogramming language toa computingplatformoftheirownchoosing,and(iii)executingtheseinascalablemanner.Thismeans devising execution strategies thataredistributed, parallelized, and supportboth in-memory technologies and out-of-core execution for data-intensive algorithms. To meet thischallengethecompiler,dataanalysis,databasesystems,distributedsystems,andmachinelearningcommunities,amongothers,willhaveto cometogether.Wewillhavetodevelopnovelscalablealgorithmsandsystemsthatcanorganizethedatadelugeanddistillinformationtocreatevalue.Furthermore,thepowerofdeclarativelanguages,toenableautomaticoptimization,parallelization,and the adaptationof aprogram tovaryingdistributed systems andnovelhardwarearchitectures(dependingondatadistribution,datasize,datarate,andsystemload)mustbepreserved.Inthisway,wewillovercomethecurrent“stoneage”inbigdataanalytics.Thatis,algorithmspecificationsinsystems thatdonotautomaticallyoptimize(e.g.,MPI,MapReduce),imperativelanguages(e.g.,C),object-orientedlanguages(e.g.,Java),andrelational-orientedlanguages(e.g.,SQL,XQuery)withnon-tunableexternaldriverprograms,andtechnicalcomputingsystems(e.g.,R,MATLAB)thatdonotscale.
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Figure2.Deepanalysisisthenameofthegame.2. DetailedDescriptionsoftheDataAnalyticsTrackRulesPleasestudythefollowingsubsectionsverycarefully,mostofyourquestionsshouldbeanswered.2.1 QualificationandMainCompetenceAreas. TheDataAnalyticsMaster’s TrackqualifiesstudentstopursuecareersasaDataScientist,DataAnalyst,orDataEngineer.Theywill learnaboutdataanalysismethods,theirapplicationtoreal-worldproblemsinvaryingdomains,learnmoreabouttheinternalsofdatabasesystems,anddevelopprogrammingskillswithafocusonmassively-paralleldataprocessingsystems.2.2 Requirements. Students followingthetrackshouldbeenrolled in one of the following TUBerlinMaster’sPrograms:Computer Science (‘Informatik’), Information SystemsManagement (‘Wirtschafts-informatik’)orComputerEngineering(‘TechnischeInformatik’).TheiracceptancetotheDataAnalyticstrackisbydefault.However,verydedicatedstudentsfromotherprogrammes(e.g.Mathematics)alsocouldbeeligible.Thiswillbedecidedonindividualrequest,carefullycheckingtheapplicationandthebackgroundfromtheCV,andfromyour(inthiscase:mandatory)motivationletter.2.3 Prerequisites: Students interested in joining the track should possess: (a) very strong Englishlanguageskills,(b)programmingskillsinfunctional(e.g.,Scala)andobjectoriented(e.g.,Java)programminglanguages,(c)fundamentalskillsindatabasemanagementsystems,and(d)knowledgeinmathematicalfoundations(e.g.,linearalgebra,probability,statistics).
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2.4CreditPointsandTrackStructure.ToearnaM.Sc.degree,studentsmustachieve120ECTScreditpoints.Ofthese,90ECTScreditpointsmustfulfilltherequirementsdescribedfurtherbelow,toqualityforthetrackcertificate.CreditPoints Competence Course Notes
24ECTS DataAnalytics(DA)
MachineLearning1orMachineIntelligenceI
mandatorycourse
DAElective1 seeAppendixA,Table1DAElective2DAElective3
18ECTSScalableDataManagement
(SDM)
DatabaseTechnology mandatorycourseSDMElective1 seeAppendixA,Table2SDMElective2
6ECTS DomainSpecificApplication(DSA)
DSAElective seeAppendixA,Table3
9ECTS Project ProjectElective seeAppendixA,Table43ECTS Seminar SeminarElective seeAppendixA,Table5
30ECTS Thesis Master’sThesisThethesismustbeadataanalyticsoriented topic, supervised by a TUBerlinDataAnalyticsLabProfessor.
Total:90ECTS 2.5EnrollingintheTrack.Toenroll in thetrack,studentsmust join the“DataAnalyticsTrack”courselocatedathttps://www.isis.tu-berlin.de/2.0/course/view.php?id=1069.
2.6MentoringProgram.TrackparticipantsareinvitedtocontactamemberoftheDataAnalyticsLabtoidentifyamentorandrequestguidance.2.7ChangestotheTrack.Trackrequirementsmaychangeannually.Therefore,studentsarerequiredtoregularlymonitorannouncementspostedontheISISDataAnalyticsTrackforum.
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AppendixA.RepresentativeListofElectiveCoursesAcrossCompetencyAreas
Table1.Arepresentative(butincomplete)listofeligible2dataanalyticscourses.
Title ECTS Prof Term Lang1. EconometricAnalysisofLongitudinalandPanelData 6 AxelWerwatz Winter EN
2. MachineIntelligenceII 6 KlausObermayer Summer EN
3. MachineLearning2 9 Klaus-RobertMüller Summer EN+DE
4. Microeconometrics 6 AxelWerwatz Winter EN
5. MonteCarloMethodsinMachineLearningandArtificialIntelligence
6 ManfredOpper Summer EN
6. MultivariateAnalysis/BusinessStatistics 6 AxelWerwatz Summer EN
7. NumerischeMathematikfürIngenieureII 10 JörgLiesen Winter DE
8. PraktikumMaschinellesLernen 9 Klaus-RobertMüller Summer EN
9. ProbabilisticandBayesianModellinginMLandAI 6 ManfredOpper Summer EN
10. StochastischeModelle 10 MichaelScheutzow Winter DE
11. TimeSeriesAnalysis 6 AxelWerwatz Winter EN
12. TreatmentEffectAnalysis 6 AxelWerwatz Summer EN
13. Ökonometrie 6 AxelWerwatz Winter EN
14. DigitaleSignalverarbeitung 12 ReinholdOrglmeister Winter DE
Table2.Arepresentative(butincomplete)listofeligible3scalabledatamanagementcourses.
Title ECTS Prof Term Lang
1. AIM1Heterogeneous&DistributedInformationSystems 6 Ralf-DetlefKutsche Winter EN
2. AIM2ManagementofDataStreams 6 VolkerMarkl WinterorSummer
DE
3. AIM3ScalableDataScience:Systems&Methods(SDSSM) 6 VolkerMarkl WinterorSummer
EN
4. CIT9-CloudComputing 6 OdejKao WinterorSummer
EN
5. IDB-PRA:ImplementationofaDatabaseEngine 6 VolkerMarkl Winter EN
6. ParallelSystems 6 Hans-UlrichHeiss Summer EN
7. TheorieVerteilterAlgorithmen 6 UweNestmann Winter EN+DE
2Alternativedataanalyticscoursesmaybeproposed,buttheyaresubjecttoapproval.3Alternativescalabledatamanagementcoursesmaybeproposed,buttheyaresubjecttoapproval.
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Table3.Arepresentative(butincomplete)listofeligible4domainspecificapplicationcourses.
Title ECTS Prof Term Lang1. AppliedEmbeddedSystemsProject 6 BernardusJuurlink Summer EN
2. Aussenwirtschaft 6 FrankHeinemann Winter DE
3. DigitalCommunities 6 AxelKüpper Winter EN
4. DigitaleNachrichtenübertragung(TechnischeInformatik) 9 ThomasSikora Summer DE
5. ElectronicCommerce 6 AxelKüpper Winter DE
6. Energiewirtschaft-Elektrizitätswirtschaft 6 ChristianHirschhausen Summer DE
7. Energiewirtschaft-TechnologieundInnovation 6 ChristianHirschhausen Summer DE
8. EnergyEconomics 6 GeorgErdmann Winter EN
9. ExperimentalandBehaviouralEconomics 6 DorotheaKübler Summer EN
10. GesundheitsökonomieII 6 MarcoRunkel Winter DE
11. IntegriertesInformationsmanagement 6 RüdigerZarnekow Summer DE
12. IntelligenteSicherheitinNetzwerken 9 SahinAlbayrak Summer DE
13. IT-Service-Management 6 RüdigerZarnekow Winter DE
14. Messtechnik 12 RolandThewes Winter DE
15. MethodsforNetworkEngineering(OR2) 6 ChristianHirschhausen WinterorSummerSummer
EN
16. OpenSourceandIPintheDigitalSociety 6 KnutBlind WinterorSummer
EN
17. PatentrechtundPatentmanagementI 6 JürgenEnsthaler Summer DE
18. Quellencodierung-Multimediasignalverarbeitung(TechnischeInformatik)
9 ThomasSikora Summer DE
19. SemanticSearch 6 SahinAlbayrak Summer DE
20. Signalverarbeitung 6 ReinholdOrglmeister Winter DE
21. SpeechSignalProcessingandSpeechTechnology 6 SebastianMöller Winter EN+DE
22. TechnischeDiagnoseI 6 ClemensGühmann Summer DE
23. TheEconomicsofClimateChange 6 OttmarGeorgEdenhofer Summer EN
24. Wirtschaftsprüfung 6 MaikLachmann Summer DE
4Alternativedomainareaapplicationcoursesmaybeproposed,buttheyaresubjecttoapproval.
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Table4.Arepresentative(butincomplete)listofeligible5projectcourses.
Title ECTS Prof Term Lang1. IMPRO3-BigDataAnalyticsProject(BDAPRO) 9 VolkerMarkl Winteror
SummerEN
2. Master-Projekt:VerteilteSysteme 9 OdejKao Winter DE
3. ProjectMachineLearning 9 Klaus-RobertMüller Winter EN
4. ProjectNeuralInformationProcessing 9 KlausObermayer Summer EN+DE
5. Project:StatisticalMethodsinAIandML 9 ManfredOpper Winter EN+DE
6. ProjektNachrichtenübertragung 6 ThomasSikora WinterorSummer
DE
Table5.Arepresentative(butincomplete)listofeligible6seminarcourses.
Title ECTS Prof Term Lang
1. AdvancesinSemanticSearch 3 SahinAlbayrak WinterorSummer
DE
2. AnwendungenKognitiverAlgorithmen 3 Klaus-RobertMüller WinterorSummer
DE
3. BDASEM-BigDataAnalyticsSeminar 3 VolkerMarkl Winter EN
4. CIT8-AktuelleThemenausdemBereichderverteiltenSysteme
3 OdejKao Winter DE
5. HotTopicsinNextGenerationNetworksandFutureInternetTechnologies
3 ThomasMagedanz WinterorSummer
EN+DE
6. HotTopicsinOperatingSystemsandDistributedSystems 3 Hans-UlrichHeiSS WinterorSummer
EN
7. IMSEM-SeminarHotTopicsinInformationManagement 3 VolkerMarkl Summer EN
8. IntroductiontoComputationalGenomics 3 ManfredOpper Summer EN
9. MasterSeminar:OperatingComplexITSystems 3 OdejKao WinterorSummer
EN+DE
10. RecentAdvancesinComputerArchitecture 3 BernardusJuurlink Winter EN
11. RecentAdvancesinMulticoreSystems 3 BernardusJuurlink Summer EN
12. SeminarMeasurementandTechnicalDiagnosis 3 ClemensGühmann WinterorSummer
EN+DE
13. SeminarSoftwareEngineeringofEmbeddedSystems 3 SabineGlesner WinterorSummer
EN
14. SynchronousandAsynchronousInteractionsinDistributedSystems
3 UweNestmann Summer EN+DE
5Alternativeprojectcoursesmaybeproposed,buttheyaresubjecttoapproval.6Alternativeseminarcoursesmaybeproposed,buttheyaresubjecttoapproval.
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AppendixB.ExampleCurriculums*(Thesewillvarydependingonanindividual’sinterests.)*Caveat:Courseavailabilityandspecifications (e.g., language)varyeachsemester.Thus,someofthecontentbelowmaybeinaccurateforthecurrentsemesterorbecomeinaccuratefor the following semester. It is the responsibility of each student to obtain the latestinformationandensurealldegreerequirementsandconstraintsaremet.ARepresentativeExampleofaComputerScience(Stats/EconometricsConcentration)CurriculumOption.
FirstSemester(WinterTerm)
Title ECTS Area Term LangMachineIntelligenceIorMachineLearningI 6 DataAnalytics
(mandatory)Winter EN
DatabaseTechnology 6 ScalableDataMgmt.(mandatory)
Winter EN
e.g.,IDB-PRA:ImplementationofaDatabaseEngine 6 ScalableDataMgmt. Winter ENe.g.,DigitalCommunities 6 Application Winter EN
e.g.,BDASEM-BigDataAnalyticsSeminar 3 Seminar Winter EN
Elective 3 Winter EN+DE
SecondSemester(SummerTerm)
Title ECTS Area Term Lange.g.,MultivariateAnalysis/BusinessStatistics 6 DataAnalytics(DA)
Summer EN
e.g.,TreatmentEffectAnalysis 6 DataAnalytics
Summer EN
e.g.,MachineIntelligenceII 6 DataAnalytics
Summer EN
e.g.,AIM-3ScalableDataScience:Systems&Methods(SDSSM) 6 ScalableDataMgmt.(SDM)
Summer EN
Elective 6 (e.g.,DA,SDM) Summer EN+DE
ThirdSemester(WinterTerm)
Title ECTS Area Term Lang
e.g.,TimeSeriesAnalysis 6 DataAnalytics(DA)
Winter EN
e.g.,Microeconometrics 6 DataAnalytics
Winter EN
e.g.,AIM-2ManagementofDataStreams 6 ScalableDataMgmt.(SDM)
Winter DE
e.g.,IMPRO3-BigDataAnalyticsProject(BDAPRO) 9 Project Winter EN
Elective 3 (e.g.,DA,SDM) Winter EN+DE
FourthSemester(SummerTerm)
Title ECTS Area Term LangMaster’sThesisintheDataAnalyticsLab 30 DataAnalyticsOriented Summer EN+DE
9
ARepresentativeExampleofaComputerScience(Mathematics Concentration)CurriculumOption.
FirstSemester(WinterTerm)
Title ECTS Area Term LangMachineIntelligenceIorMachineLearningI 6 DataAnalytics
(mandatory)Winter EN
DatabaseTechnology 6 ScalableDataMgmt.(mandatory)
Winter EN
e.g.,AIM-2ManagementofDataStreams 6 ScalableDataMgmt. Winter DE
e.g.,IDB-PRA:ImplementationofaDatabaseEngine 6 ScalableDataMgmt. Winter EN
e.g.,DigitalCommunities 6 Application Winter EN
SecondSemester(SummerTerm)
Title ECTS Area Term Lang
e.g.,MachineIntelligenceII 6 DataAnalytics(DA)
Summer EN
e.g.,AIM-3ScalableDataScience:Systems&Methods(SDSSM) 6 ScalableDataMgmt.(SDM)
Summer EN
Electives 18 (e.g.,DA,SDM) Summer EN+DE
ThirdSemester(WinterTerm)
Title ECTS Area Term Lange.g.,DigitalCommunities 6 Application Winter EN
e.g.,BDASEM-BigDataAnalyticsSeminar 3 Seminar Winter EN
e.g.,IMPRO3-BigDataAnalyticsProject(BDAPRO) 9 Project Winter EN
Electives 12 (e.g.,DA,SDM) Winter EN+DE
FourthSemester(SummerTerm)
Title ECTS Area Term LangMaster’sThesisintheDataAnalyticsLab 30 DataAnalyticsOriented Summer EN+DE
10
ARepresentativeExampleofanInformationSystemsManagementCurriculumOption.
FirstSemester(WinterTerm)
Title ECTS Area Term LangMachineIntelligenceIorMachineLearningI 6 DataAnalytics
(mandatory)Winter EN
DatabaseTechnology 6 ScalableDataMgmt.(mandatory)
Winter EN
e.g.,MultivariateAnalysis/BusinessStatistics 6 DataAnalytics
Winter EN
e.g.,OpenSourceandIPintheDigitalSociety 6 Application Winter EN
e.g.,DigitalCommunities 6 Application Winter EN
SecondSemester(SummerTerm)
Title ECTS Area Term Lang
e.g.,AIM3ScalableDataScience:Systems&Methods(SDSSM) 6 ScalableDataMgmt. Summer EN
e.g.,AIM2-ManagementofDataStreams 6 ScalableDataMgmt. Summer DE
e.g.,CIT9-CloudComputing 6 ScalableDataMgmt. Summer EN
e.g.,ApplicationSystemProject 12 Project Summer EN
ThirdSemester(WinterTerm)
Title ECTS Area Term Lang
e.g.,EconometricAnalysisofLongitudinalandPanelData 6 DataAnalytics
Winter EN
e.g.,TimeSeriesAnalysis 6 DataAnalytics
Winter EN
e.g.,Microeconometrics 6 DataAnalytics
Winter EN
e.g.,IMPRO3-BigDataAnalyticsProject(BDAPRO) 9 Project Winter EN
e.g.,IMSEM-SeminarHotTopicsinInformationManagement 3 Seminar Winter EN
FourthSemester(SummerTerm)
Title ECTS Area Term Lang
Master’sThesisintheDataAnalyticsLab 30 DataAnalyticsOriented Summer EN+DE
11
ARepresentativeExampleofaComputerEngineeringCurriculumOption.
FirstSemester(WinterTerm)
Title ECTS Area Term LangMachineIntelligenceIorMachineLearningI 6 DataAnalytics(DA)
(mandatory)Winter EN
DatabaseTechnology 6 ScalableDataMgmt.(SDM)(mandatory)
Winter EN
e.g.,HotTopicsinNextGenerationNetworksandFutureInternetTechnologies
3 Seminar Winter EN+DE
Electives 15 (e.g.,DA,SDM) Winter EN+DE
SecondSemester(SummerTerm)
Title ECTS Area Term Lang
e.g.,MachineIntelligenceII 6 DataAnalytics(DA)
Summer EN
e.g.,ProbabilisticandBayesianModellinginMLandAI 6 DataAnalytics
Summer EN
e.g.DigitaleNachrichtenübertragung(TechnischeInformatik) 9 Application Summer DE
Electives 9 (e.g.,DA,SDM) Winter EN+DE
ThirdSemester(WinterTerm)
Title ECTS Area Term Lang
e.g.,IDB-PRA:ImplementationofaDatabaseEngine 6 ScalableDataMgmt.(SDM)
Winter EN
e.g.,SpeechSignalProcessingandSpeechTechnology 6 Application Winter EN+DE
e.g.,Master-Projekt:VerteilteSysteme 9 Project Winter DE
Electives 9 (e.g.,DA,SDM) Winter EN+DE
FourthSemester(SummerTerm)
Title ECTS Area Term LangMaster’sThesisintheDataAnalyticsLab 30 DataAnalyticsOriented Summer EN+DE
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AppendixC.QuestionsandAnswersQ1.Whatisatrack?
A1.Ingeneral,atrackisasuggestedsequenceofcoursesthatprofileaspecificspecialization.StudentswhosuccessfullycompletethetrackwillbeawardedacertificatefromFacultyIV.Acertificateindicatesthatastudenthasfollowedastructuredacademicprogramwiththeintenttopursuespecializationindatascience.
Q2.Whocanfollowatrack?
A2.Bydefault,studentsenrolledintheComputerScience(“Informatik”),InformationSystemsManagement (“Wirtschaftsinformatik”) or Computer Engineering (“Technische Informatik”)Master’sprogramsareeligibletopursuethetrack.Studentsfromotherstudyprogramsshouldcontact the “Data Analytics Track Steering Board” at [email protected] to determinewhethertheycanparticipateinthetrack.
Q3.Willmystudyperiodbeextended,ifIfollowthetrack?
A3. No,neithertheamountofECTScredit points,northenumberofsemesterswillincrease.Moreover,alongerstudyperiodwillnotleadtoadisqualificationfromthetrack.
Q4.Howtogoaboutselectingathesistopic?
A4.StudentsshouldspeakwithSeniorResearchers,Postdocs,orPhDstudents,intheparticipatingresearch groups, i.e. “Chairs,” to identify an open thesis topic of mutual interest. For a list ofrepresentativedatascienceorientedpublicationshavealookat[3,4],andforMaster’sThesistopicssee[5].Foraglimpseintoongoingresearchactivitiesinbigdata/datasciencesee[6].Foropenproblemsandavisionofthefutureofcomputersciencesee[7,8,9],respectively.
Q5.Whataremyprospectivecareerpossibilities?
A5. Students who complete the data analytics track are preparedtopursuecareersasDataAnalysts,DataEngineers,orDataScientists.ForinformationaboutbigdataprojectsinindustrywithinGermanyhavealookat[10].Insomecases,studentsenteraPhDprogramwiththeaimtofurtherspecializeinaresearchtopic,suchasdeeplearningorstreamingsystems.Examplesofrecent(DIMAspecific)PhDthesistopics,include[11,12,13,14,15].Formoreinfo.aboutjobopportunitiesandearningpotentialacrossEuropehavealookat[16].
Q6.IfIstillhavequestionsordoubts,notansweredyet?
A6. This document is assumed to be comprehensive. It should address the most relevantquestions. In case of any doubt (e.g., you are enrolled in a different study programme) orconcern,pleasecontactusat [email protected],please look forannouncements(e.g.,theannual“DataAnalyticsTrackIntroPresentation”)postedontheDataAnalyticsTrackforuminISIS.
Q7.HowdoIobtainmycertificate?
A7.Youwillneedtopresentevidence (e.g.,academic transcript) thatyouhavemet the trackrequirements.Oncethishasbeenverified,DIMAstaffwillprepareyourcertificate.
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AppendixD.VersionHistoryVersion Authors Date Remarks1.1 MoritzSchubotz,HolmerHemsen,VolkerMarkl 28.06.13 InitialversioninGerman1.2 MoritzSchubotz,JuanSoto,VolkerMarkl 31.07.15 TranslationintoEnglish1.3 MoritzSchubotz,JuanSoto,VolkerMarkl 16.01.16 UpdatesandRevisions2.0 RalfDetlefKutsche,VolkerMarkl,JuanSoto 09.10.17 FullRevision,newversion2
References[1]“Breakingthechains:Ondeclarativedataanalysisanddataindependenceinthebigdataera,”VolkerMarkl,PVLDB,7(13):1730–1733,2014.URL:www.vldb.org/pvldb/vol7/p1730-markl.pdf.[2]TowardsaThrivingDataEconomy:OpenData,BigData,andEcosystems(Presentation),VolkerMarkl,EuropeanCompetitivenessCouncil,March2015.URL:goo.gl/eDRSS3.[3]DIMAweb:DataSciencePublications:http://www.dima.tu-berlin.de/menue/publications/publications/.[4]ML/IDAweb:MachineLearningPublications:http://doc.ml.tu-berlin.de/publications/.[5] Completed Master’s Theses: Many Data Science Oriented, DIMA Group. URL: http://www.dima.tu-berlin.de/menue/theses/completed_mastersdiploma_theses/.[6]BerlinBigDataCenter,http://www.bbdc.berlin/home/.[7] Future Directions in Computer Science Research (Presentation: TU Berlin, Big DataWorkshop), JohnHopcroft,CornellUniversity,September2013.URL:http://www.eecs.tu-berlin.de/index.php?id=139969.[8] 50 Years of Data Science (Version 1.00), David Donoho, Stanford University, September 2015. URL:http://courses.csail.mit.edu/18.337/2015/docs/50YearsDataScience.pdf.[9]FrontiersinMassiveDataAnalysis,NationalAcademiesPress,2013.URL:http://nap.edu/18374.[10]Germany–ExcellenceinBigData,Bitkom,2016.URL:goo.gl/wUSZWv.[11]ScalingDataMininginMassivelyParallelDataflowSystems(PhDThesis),S.Schelter,November2015.[12]SpecificationandOptimizationofAnalyticalDataFlows(PhDThesis),F.Hüske,December2015.[13]Visualization-DrivenDataAggregation(PhDThesis),U.Jugel,TUBerlin,April2017.[14]ExploratoryRelationExtractioninLargeMultilingualData(PhDThesis),A.Akbik,April2016.[15]ProgrammingAbstractions,Compilation,andExecutionTechniquesforMassivelyParallelDataAnalysis(PhDThesis),S.Ewen,TUBerlin,November2014.[16] The European Data Science Salary Survey: Tools, Trends,What Pays (andWhat Doesn’t) for DataProfessionalsinEurope,JohnKing&RogerMagoulas,O’ReillyPress,2017.
