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WPC 300 FINAL TEST QUESTIONS AND ANSWERS
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WPC 300 FINAL TEST QUESTIONS AND ANSWERS
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WPC 300 FINAL TEST QUESTIONS ANDANSWERS
Identifying a problem - Answer-Who are our customers? How frequently do they shop at
our location?
What products do they buy from us? What is their share of wallet for our brand?
Who is our most loyal customer? Who is our most valuable customer?
How should we treat our most valuable customer?
How do we learn more about our customers?
What strategy should we implement to bring customers to our door more frequently?
What should be the promotion strategy to get more attention from our customers?
Principles of Problem Framing - Answer-Tell an interesting and complete story
Find an appropriate solution framework
Routinize the procedure
Analytics - Answer--is the process of developing actionable decisions or
recommendations for actions based on insights generated from historical data
-represents the combination of computer technology, management science techniques,
and statistics to solve real problems.
Primary Data - Answer-Survey, Interviews (marketing firm's telephone interviews), Used
a lot in marketing research
Secondary Data - Answer-Firm's proprietary database, Internet data (crawlers) [scarpy,
beautifulsoup], Stock/capital market data [compustat, CRSP] Accounting disclosure
data [ from 10K, 10Q]
Simulated Data - Answer-Data based on assumption and simulation Used a lot in
scheduling, routing and queuing
Data Extraction - Answer-Extract data from primary/secondary source
Data Transformation - Answer-Transform / clean data into proper format or structure for
the purpose of querying & analysis
Data Load - Answer-Load data into final target database, more specifically an
operational data store, data mart or data warehouse
ETL - Answer-is time consuming and sometimes done in parallel. Several computation
tools are used to develop robust ETL systems.
, Descriptive Analytics - Answer-This is a preliminary stage of data processing that
creates a summary of historical data to yield useful information and possibly prepare the
data for further analysis.
Questions: (1) What happened? (2) What is happening?
Methods: (1) Standard reporting (2) Dashboards (3) Visual analytics
Outcome: Well defined business problems and opportunities
Diagnostic/Explanatory Analytics - Answer-this is about looking into the past and
determining why a certain thing happened. This type of analytics usually revolves
around working on a dashboard. Question: (1) Why did it happen? (2) How did it
happen?
Methods - Answer-Inferential Statistics, Visual analytics
Outcome - Answer-Discover/Understand causal relationships of an outcome
Predictive Analytics - Answer-Predictive analytics is the use of data, statistical
algorithms and machine learning techniques to identify the likelihood of future outcomes
based on historical data. The goal is to go beyond knowing what has happened to
providing a best assessment of what will happen in the future.
Question: (1) What will happen next? (2) Why will it happen next?
Methods: (1) Data mining (2) Text mining (3) Forecasting
Outcome: Accurate projections of future outcomes and events
Prescriptive Analytics - Answer-Prescriptive analytics answers the question of what to
do by providing information on optimal decisions based on the predicted future
scenarios. The key to prescriptive analytics is being able to use big data, contextual
data and lots of computing power to produce answers in real time.
Question: (1) What should be done about it? (2) Why should you do it?
Methods: (1) Optimization (2) Simulation (3) Expert systems
Outcome: Best possible business decision and outcome
Data visualization - Answer-is the graphical representation of information and data
-charts, graphs, and maps provide access to see and understand trends, outliers, and
patterns in data
Basic Principles of Visualization- - Answer-(Edward Tufte)
The chart should tell a story The chart should have graphical integrity The chart should
minimize graphical complexity
Data Visualization Principle - Answer--The depictions should enable meaningful
comparison
-The chart should yield insight beyond the text
-The chart should have graphical integrity. It should not "lie" or mislead readers. Tufte's
"Lie Factor":
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