Lecture note Data visualization - Chapter 31

In this lecture we learned about: Definition of data visualization, terms related to data visualization, data mining, data recovery, data redundancy, data acquisition, data validation, data integrity, data verification, data aggregation.
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Lecture note Data visualization - Chapter 31Lecture31IntroductiontoDataVisualizationDefinitionofDataVisualizationTermsrelatedtoDataVisualization DataMining DataRecovery DataRedundancy DataAcquisition DataValidation DataIntegrity DataVerificationContinued….Datamining analyticprocessdesignedtoexploredata analyzingdatafromdifferentperspectives summarizingitintousefulinformationDatarecovery handlingthedatathroughthedatafromdamaged,failed, corrupted,orinaccessiblesecondarystoragemedia recoveryrequiredduetophysicaldamagetothestorage deviceorlogicaldamagetothefilesystemContinued….Dataredundancy additionaltotheactualdata permitscorrectionoferrorsDataacquisition processofsamplingsignals measurerealworldphysicalconditions convertingtheresultingsamplesintodigitalnumericvaluesDatavalidation processofensuringthataprogramoperatesonclean,Continued….Dataintegrity maintainingandassuringtheaccuracyandconsistencyof data ensuredataisrecordedexactlyasintendedDataverification differenttypesofdataarecheckedforaccuracyand inconsistenciesafterdatamigrationisdoneDataaggregation informationisgatheredandexpressedinasummaryformContinued….NeedfordatavisualizationImportanceofdatavisualizationLimitationofspreadsheetInterpretationthroughdatavisualization identifyareasthatneedattentionorimprovement understandwhatfactorsinfluencedesignsystem predicthowtochangesystemdesignaccordingly predicttheefficiencyofsystemInteractiveVisualizationContinued….Combinationofdisciplines datavisualizationtoprovideameaningfulsolutionrequires insightsfromdiversefieldslikestatistics,datamining, graphicdesign,andinformationvisualization softwarebasedinformationvisualizationaddsbuilding blocksforinteractingwithandrepresentingvariouskindsof abstractdata Continued….Processofdatavisualization Acquire Parse Filter Mine Represent Refine InteractAcquire Obtainthedata,whetherfromafileonadiskorasource overanetworkParse Providesomestructureforthedata’smeaning,andorderit intocategoriesFilter RemoveallbutthedataofinterestMine Applymethodsfromstatisticsordataminingasawayto discernpatternsorplacethedatainmathematicalcontextRepresent Chooseabasicvisualmodel,suchasabargraph,list,or tree.Refine Improvethebasicrepresentationtomakeitclearerand morevisuallyengaging.Interact Addmethodsformanipulatingthedataorcontrolling whatfeaturesarevisible.Continued….IterationandCombinationofstepsofdatavisualizationUniquerequirementsforeachproject eachdatasetisdifferent thepointofvisualizationistoexposethatfascinatingaspect ofthedataandmakeitselfevident readilyavailablerepresentationtoolkitsareusefulstarting points theymustbecustomizedduringanindepthstudyofthe taskContinued….AvoidusageofexcessdataAudienceofproblemQuantitativemessages TimeSeries Ranking ParttoWhole Deviation FrequencyDistribution CorrelationTimeseries: Asinglevariableiscapturedoveraperiodoftime,suchas theunemploymentrateovera10yearperiod.Alinechart maybeusedtodemonstratethetrendRanking: Categoricalsubdivisionsarerankedinascendingor descendingorder,suchasarankingofsalesperformanceby salespersonsduringasingleperiod Abarchartmaybeusedtoshowthecomparisonacrossthe salespersonsParttowhole: Categoricalsubdivisionsaremeasuredasaratiotothe whole Apiechartorbarchartcanshowthecomparisonofratios, suchasthemarketsharerepresentedbycompetitorsina marketDeviation: Categoricalsubdivisionsarecomparedagainareference, suchasacomparisonofactualvs.budgetexpensesfor severaldepartmentsofabusinessforagiventimeperiod Abarchartcanshowcomparisonoftheactualversusthe referenceamountFrequencydistribution: Showsthenumberofobservationsofaparticularvariable forgiveninterval,suchasthenumberofyearsinwhichthe stockmarketreturnisbetweenintervalssuchas010%,11 20%,etc. Ahistogram,atypeofbarchart,maybeusedforthis analysis Aboxplothelpsvisualizekeystatisticsaboutthe distribution,suchasmean,median,quartiles,etc.Correlation: Comparisonbetweenobservationsrepresentedbytwo variables(X,Y)todetermineiftheytendtomoveinthe same ...