4/15/25, 11:16 BUAL 5660 Exam 2 |
AM
BUAL 5660 EXAM 2 ACTUAL EXAM QUESTIONS AND
ANSWERS WITH COMPLETE SOLUTIONS VERIFIED
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Save
Practice questions for this set
Learn 1/7 Study using Learn
a word or phrase extracted by NLP
Select the correct term
1Data
2Slicing and Dicing
3Corpus 4Terms
Don't know?
Terms in this set (44)
most commonly used when a hypothesis is not readily apparent for
Basic Insight Analytics studying or disparate data is being examined. Common methods
include slicing and dicing. monitoring, and variance identification
breaking down data into smaller components. Need to do this to
Slicing and Dicing
work on single machines
1/
5
, 4/15/25, 11:16 BUAL 5660 Exam 2 |
AM
- Answers more complex questions or uses more complex data
- Includes sophisticated techniques such as advanced modeling,
machine learning, neural networks, text analytics, and data
Advanced Insight Analytics
mining
- This techniques are common across data environments and not
necessarily constrained to the big data environment
- Technology has enabled average users to engage with more advanced analytics
- Supports structured and unstructured data
- Includes statistical or data mining processes using advanced
algorithms or techniques
- Often includes an iterative process that starts with a large set of predictor
Predictive Modeling
variables and refines the model to those with the most impact on
(or predictive power on) the outcome variable
-- For example, what influences the purchase of a specific model of
auto over others?
- Operationalized analytics are termed that because they are
embedded (operationalized) in the business processes of the
organization
Process-Driven (Operational) Analytics
- Not only includes analytics, but often an automated action based
on prediction or decision rules
-- For example, if the credit charge seems out of place, hold it and
notify the credit card holder
These analytics techniques may include any of the above mentioned
techniques but are specific to increasing profit for the organization
- Product bundling
Revenue-Driven (Monetizing) Analytics
- Sales prediction focused on manipulable items (e.g., target marketing)
- Location specific data
- Contract evaluation
Noise Information in the data that is useless or distracts from the actual value
Algorithms ___________ideally should be run as close to the data source as possible
Data ________mining uses structured data from databases
_______mining uses unstructured data. It imposes structure to the data then mines
Text
that. Example: Word docs, PDF files, text excerpts, XML files, and so on
simple method of text mining that groups words together irrespective
Bag of Words of their order and arranges them by classification (e.g. sales,
complaint).
- take all of the words and look at the frequency of the words
Corpus a large body of text, (the whole text dataset)
Terms a word or phrase extracted by NLP
Concepts generalizations about terms
Stemming reducing words to their roots
Stop words words that are filtered out
Include words words that are preindexed
Synonyms words that mean the same thing (e.g. dog, hound, canine)
words that are spelled the same but have different meanings (e.g. lead)
Homonyms
also may simply sound the same but have different meanings (e.g.
poll, pole)
words that are spelled the same but used differently, often a
Polysemes
noun/verb combination (e.g. bank - financial institute, and bank - to
rely on)
assigning meaning to a block of text (e.g. Auburn University vs. Auburn
Tokenizing
+ University)
Term Dictionary a list of words used to narrow a focus or search
Word Frequency number of times a word is found in a specific document
2/
5
AM
BUAL 5660 EXAM 2 ACTUAL EXAM QUESTIONS AND
ANSWERS WITH COMPLETE SOLUTIONS VERIFIED
Leave the first rating
Save
Practice questions for this set
Learn 1/7 Study using Learn
a word or phrase extracted by NLP
Select the correct term
1Data
2Slicing and Dicing
3Corpus 4Terms
Don't know?
Terms in this set (44)
most commonly used when a hypothesis is not readily apparent for
Basic Insight Analytics studying or disparate data is being examined. Common methods
include slicing and dicing. monitoring, and variance identification
breaking down data into smaller components. Need to do this to
Slicing and Dicing
work on single machines
1/
5
, 4/15/25, 11:16 BUAL 5660 Exam 2 |
AM
- Answers more complex questions or uses more complex data
- Includes sophisticated techniques such as advanced modeling,
machine learning, neural networks, text analytics, and data
Advanced Insight Analytics
mining
- This techniques are common across data environments and not
necessarily constrained to the big data environment
- Technology has enabled average users to engage with more advanced analytics
- Supports structured and unstructured data
- Includes statistical or data mining processes using advanced
algorithms or techniques
- Often includes an iterative process that starts with a large set of predictor
Predictive Modeling
variables and refines the model to those with the most impact on
(or predictive power on) the outcome variable
-- For example, what influences the purchase of a specific model of
auto over others?
- Operationalized analytics are termed that because they are
embedded (operationalized) in the business processes of the
organization
Process-Driven (Operational) Analytics
- Not only includes analytics, but often an automated action based
on prediction or decision rules
-- For example, if the credit charge seems out of place, hold it and
notify the credit card holder
These analytics techniques may include any of the above mentioned
techniques but are specific to increasing profit for the organization
- Product bundling
Revenue-Driven (Monetizing) Analytics
- Sales prediction focused on manipulable items (e.g., target marketing)
- Location specific data
- Contract evaluation
Noise Information in the data that is useless or distracts from the actual value
Algorithms ___________ideally should be run as close to the data source as possible
Data ________mining uses structured data from databases
_______mining uses unstructured data. It imposes structure to the data then mines
Text
that. Example: Word docs, PDF files, text excerpts, XML files, and so on
simple method of text mining that groups words together irrespective
Bag of Words of their order and arranges them by classification (e.g. sales,
complaint).
- take all of the words and look at the frequency of the words
Corpus a large body of text, (the whole text dataset)
Terms a word or phrase extracted by NLP
Concepts generalizations about terms
Stemming reducing words to their roots
Stop words words that are filtered out
Include words words that are preindexed
Synonyms words that mean the same thing (e.g. dog, hound, canine)
words that are spelled the same but have different meanings (e.g. lead)
Homonyms
also may simply sound the same but have different meanings (e.g.
poll, pole)
words that are spelled the same but used differently, often a
Polysemes
noun/verb combination (e.g. bank - financial institute, and bank - to
rely on)
assigning meaning to a block of text (e.g. Auburn University vs. Auburn
Tokenizing
+ University)
Term Dictionary a list of words used to narrow a focus or search
Word Frequency number of times a word is found in a specific document
2/
5
