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WPC 300 Final Exam Questions and Answers
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WPC 300 Final Exam Questions and Answers
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WPC 300 Final Exam Questions andAnswers
Identifying iia iiproblem ii- iiAnswers ii-Who iiare iiour iicustomers? iiHow iifrequently iido iithey
iishop iiat iiour iilocation?
What iiproducts iido iithey iibuy iifrom iius? iiWhat iiis iitheir iishare iiof iiwallet iifor iiour iibrand?
Who iiis iiour iimost iiloyal iicustomer? iiWho iiis iiour iimost iivaluable iicustomer?
How iishould iiwe iitreat iiour iimost iivaluable iicustomer?
How iido iiwe iilearn iimore iiabout iiour iicustomers?
What iistrategy iishould iiwe iiimplement iito iibring iicustomers iito iiour iidoor iimore
iifrequently?
What iishould iibe iithe iipromotion iistrategy iito iiget iimore iiattention iifrom iiour iicustomers?
Principles iiof iiProblem iiFraming ii- iiAnswers ii-Tell iian iiinteresting iiand iicomplete iistory
Find iian iiappropriate iisolution iiframework
Routinize iithe iiprocedure
Analytics ii- iiAnswers ii--is iithe iiprocess iiof iideveloping iiactionable iidecisions iior
iirecommendations iifor iiactions iibased iion iiinsights iigenerated iifrom iihistorical iidata
-represents iithe iicombination iiof iicomputer iitechnology, iimanagement iiscience
iitechniques, iiand iistatistics iito iisolve iireal iiproblems.
Primary iiData ii- iiAnswers ii-Survey, iiInterviews ii(marketing iifirm's iitelephone iiinterviews),
iiUsed iia iilot iiin iimarketing iiresearch
Secondary iiData ii- iiAnswers ii-Firm's iiproprietary iidatabase, iiInternet iidata ii(crawlers)
ii[scarpy, iibeautifulsoup], iiStock/capital iimarket iidata ii[compustat, iiCRSP] iiAccounting
iidisclosure iidata ii[ iifrom ii10K, ii10Q]
Simulated iiData ii- iiAnswers ii-Data iibased iion iiassumption iiand iisimulation iiUsed iia iilot
iiin iischeduling, iirouting iiand iiqueuing
Data iiExtraction ii- iiAnswers ii-Extract iidata iifrom iiprimary/secondary iisource
Data iiTransformation ii- iiAnswers ii-Transform ii/ iiclean iidata iiinto iiproper iiformat iior
iistructure iifor iithe iipurpose iiof iiquerying ii& iianalysis
Data iiLoad ii- iiAnswers ii-Load iidata iiinto iifinal iitarget iidatabase, iimore iispecifically iian
iioperational iidata iistore, iidata iimart iior iidata iiwarehouse
, ETL ii- iiAnswers ii-is iitime iiconsuming iiand iisometimes iidone iiin iiparallel. iiSeveral
iicomputation iitools iiare iiused iito iidevelop iirobust iiETL iisystems.
Descriptive iiAnalytics ii- iiAnswers ii-This iiis iia iipreliminary iistage iiof iidata iiprocessing
iithat iicreates iia iisummary iiof iihistorical iidata iito iiyield iiuseful iiinformation iiand iipossibly
iiprepare iithe iidata iifor iifurther iianalysis. ii
Questions: ii(1) iiWhat iihappened? ii(2) iiWhat iiis iihappening?
Methods: ii(1) iiStandard iireporting ii(2) iiDashboards ii(3) iiVisual iianalytics
Outcome: iiWell iidefined iibusiness iiproblems iiand iiopportunities
Diagnostic/Explanatory iiAnalytics ii- iiAnswers ii-this iiis iiabout iilooking iiinto iithe iipast iiand
iidetermining iiwhy iia iicertain iithing iihappened. iiThis iitype iiof iianalytics iiusually iirevolves
iiaround iiworking iion iia iidashboard. iiQuestion: ii(1) iiWhy iidid iiit iihappen? ii(2) iiHow iidid iiit
iihappen?
Methods ii- iiAnswers ii-Inferential iiStatistics, iiVisual iianalytics
Outcome ii- iiAnswers ii-Discover/Understand iicausal iirelationships iiof iian iioutcome
Predictive iiAnalytics ii- iiAnswers ii-Predictive iianalytics iiis iithe iiuse iiof iidata, iistatistical
iialgorithms iiand iimachine iilearning iitechniques iito iiidentify iithe iilikelihood iiof iifuture
iioutcomes iibased iion iihistorical iidata. iiThe iigoal iiis iito iigo iibeyond iiknowing iiwhat iihas
iihappened iito iiproviding iia iibest iiassessment iiof iiwhat iiwill iihappen iiin iithe iifuture.
Question: ii(1) iiWhat iiwill iihappen iinext? ii(2) iiWhy iiwill iiit iihappen iinext?
Methods: ii(1) iiData iimining ii(2) iiText iimining ii(3) iiForecasting
Outcome: iiAccurate iiprojections iiof iifuture iioutcomes iiand iievents
Prescriptive iiAnalytics ii- iiAnswers ii-Prescriptive iianalytics iianswers iithe iiquestion iiof
iiwhat iito iido iiby iiproviding iiinformation iion iioptimal iidecisions iibased iion iithe iipredicted
iifuture iiscenarios. iiThe iikey iito iiprescriptive iianalytics iiis iibeing iiable iito iiuse iibig iidata,
iicontextual iidata iiand iilots iiof iicomputing iipower iito iiproduce iianswers iiin iireal iitime.
Question: ii(1) iiWhat iishould iibe iidone iiabout iiit? ii(2) iiWhy iishould iiyou iido iiit?
Methods: ii(1) iiOptimization ii(2) iiSimulation ii(3) iiExpert iisystems
Outcome: iiBest iipossible iibusiness iidecision iiand iioutcome
Data iivisualization ii- iiAnswers ii-is iithe iigraphical iirepresentation iiof iiinformation iiand
iidata
-charts, iigraphs, iiand iimaps iiprovide iiaccess iito iisee iiand iiunderstand iitrends, iioutliers,
iiand
patterns iiin iidata
Basic iiPrinciples iiof iiVisualization- ii- iiAnswers ii-(Edward iiTufte)
The iichart iishould iitell iia iistory iiThe iichart iishould iihave iigraphical iiintegrity iiThe iichart
iishould
minimize iigraphical iicomplexity
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