WPC 300 FINAL COMPREHENSIVE TEST
BANK VERIFIED QUESTIONS AND
SOLUTIONS PACK
●● 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)
BANK VERIFIED QUESTIONS AND
SOLUTIONS PACK
●● 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)
