MISY 5360 | UPDATED Questions with 100% Verified
Answers
Q1: Predictive analytics
A: Type of analytics that determines what is likely to
happen in the future
Q2: Star schema
A: Special case of snowflake schema
Q3: Supervised Learning
A: Methods that use classification and regression
Q4: Nominal data
A: Description of the data field 'ethnic group'
Q5: Prescriptive analytics
A: Type of analytics that recognizes what is going
on, forecasts, and makes decisions for best
performance
Q6: Classification models
A: Models used in data mining to help in prediction
Q7: Web Usage Mining
A: Extraction of information from data generated
through web page visits and transactions
Q8: Standard Deviation
A: Measure of dispersion calculated by taking the
square root of the variations
Q9: Data granularity
A: Characteristic of data requiring variables and
data values to be defined at the lowest level of
detail
Q10: Ratio data
A: Type of data that is not categorical
, Q11: Term Dictionary
A: Collection of terms specific to a narrow field
Q12: Discretization
A: Converting continuous valued numerical
variables to ranges and categories
Q13: BI systems
A: DW-driven DSSs in the 2000s
Q14: Data source reliability
A: Data correctness and match for the analytics
problem
Q15: Three-tier architecture
A: Web client connecting to a Web server, which is
connected to a BI application server
Q16: Association rule mining
A: Finding an affinity of two products commonly
together in a shopping cart
Q17: Real-time data warehousing
A: Supports highest level of decision making
sophistication and power, enabled by speed of
data transfer
Q18: True positive rate
A: Ratio of correctly classified positives divided by
the total positive count
Q19: Six Sigma
A: Methodology aimed at reducing the number of
defects in a business process
Answers
Q1: Predictive analytics
A: Type of analytics that determines what is likely to
happen in the future
Q2: Star schema
A: Special case of snowflake schema
Q3: Supervised Learning
A: Methods that use classification and regression
Q4: Nominal data
A: Description of the data field 'ethnic group'
Q5: Prescriptive analytics
A: Type of analytics that recognizes what is going
on, forecasts, and makes decisions for best
performance
Q6: Classification models
A: Models used in data mining to help in prediction
Q7: Web Usage Mining
A: Extraction of information from data generated
through web page visits and transactions
Q8: Standard Deviation
A: Measure of dispersion calculated by taking the
square root of the variations
Q9: Data granularity
A: Characteristic of data requiring variables and
data values to be defined at the lowest level of
detail
Q10: Ratio data
A: Type of data that is not categorical
, Q11: Term Dictionary
A: Collection of terms specific to a narrow field
Q12: Discretization
A: Converting continuous valued numerical
variables to ranges and categories
Q13: BI systems
A: DW-driven DSSs in the 2000s
Q14: Data source reliability
A: Data correctness and match for the analytics
problem
Q15: Three-tier architecture
A: Web client connecting to a Web server, which is
connected to a BI application server
Q16: Association rule mining
A: Finding an affinity of two products commonly
together in a shopping cart
Q17: Real-time data warehousing
A: Supports highest level of decision making
sophistication and power, enabled by speed of
data transfer
Q18: True positive rate
A: Ratio of correctly classified positives divided by
the total positive count
Q19: Six Sigma
A: Methodology aimed at reducing the number of
defects in a business process
