WPC 300 MIDTERM 1 UPDATED ACTUAL QUESTIONS AND
CORRECT ANSWERS
Question:
1. Heuristic
Answer:
Learns by acting, uses trial and error, values experiences, relies on common sense, seeks satisfying solutions
Question:
2. Analytic
Answer:
Learns by analyzing, uses step by step procedure, values quantitative and models, builds mathematical
models, seeks optimal solution
Question:
3. Data extraction
Answer:
Extract data from primary/secondary source
Question:
4. Data transformation
Answer:
Transform/clean data into proper format or structure for the purpose of querying and analysis
Question:
5. Data load
Answer:
Load data into final target database, more specifically as operational data store, data mart or data warehouse
Question:
6. Descriptive Modeling
Answer:
Basic plots of variables Find patterns in data Descriptive statistics Correlations Outliers Sampling Some
data mining techniques - clustering
Question:
7. Explanatory modeling
Answer:
Develop model to understand a (causal) relationship
Question:
8. Predictive analysis
, Answer:
Linear regression Data mining Classification Decision tree Association rule (market basket analysis) Text
mining Simulation
Question:
9. Problem Framing Tell an interesting and complete story, find an appropriate solution framework,
Answer:
routinize the procedure
Question:
10. Agile
Answer:
Empirical process control
Question:
11. Waterfall
Answer:
Defined process control
Question:
12. Hypothesis
Answer:
Educated Guess
Question:
13. Data
Answer:
Discrete, unorganized, raw facts
Question:
14. Information
Answer:
the transformation of those facts into meaning
Question:
15. Primary Data
Answer:
Survey, Interviews (marketing firm's telephone interviews), Used a lot in marketing research
Question:
16. Secondary Data
Answer:
Firm's proprietary database, Internet data (crawlers) [scarpy, beautifulsoup], Stock/capital market data
(compustat, CRSP), Accounting disclosure data (from 10k, 10Q)
CORRECT ANSWERS
Question:
1. Heuristic
Answer:
Learns by acting, uses trial and error, values experiences, relies on common sense, seeks satisfying solutions
Question:
2. Analytic
Answer:
Learns by analyzing, uses step by step procedure, values quantitative and models, builds mathematical
models, seeks optimal solution
Question:
3. Data extraction
Answer:
Extract data from primary/secondary source
Question:
4. Data transformation
Answer:
Transform/clean data into proper format or structure for the purpose of querying and analysis
Question:
5. Data load
Answer:
Load data into final target database, more specifically as operational data store, data mart or data warehouse
Question:
6. Descriptive Modeling
Answer:
Basic plots of variables Find patterns in data Descriptive statistics Correlations Outliers Sampling Some
data mining techniques - clustering
Question:
7. Explanatory modeling
Answer:
Develop model to understand a (causal) relationship
Question:
8. Predictive analysis
, Answer:
Linear regression Data mining Classification Decision tree Association rule (market basket analysis) Text
mining Simulation
Question:
9. Problem Framing Tell an interesting and complete story, find an appropriate solution framework,
Answer:
routinize the procedure
Question:
10. Agile
Answer:
Empirical process control
Question:
11. Waterfall
Answer:
Defined process control
Question:
12. Hypothesis
Answer:
Educated Guess
Question:
13. Data
Answer:
Discrete, unorganized, raw facts
Question:
14. Information
Answer:
the transformation of those facts into meaning
Question:
15. Primary Data
Answer:
Survey, Interviews (marketing firm's telephone interviews), Used a lot in marketing research
Question:
16. Secondary Data
Answer:
Firm's proprietary database, Internet data (crawlers) [scarpy, beautifulsoup], Stock/capital market data
(compustat, CRSP), Accounting disclosure data (from 10k, 10Q)
