Assignment 4: Data Mining
Due Week 9 and worth 75 points
The development of complex algorithms that can mine mounds of data that have been collected from people and digital devices have led to the adoption of data mining by most businesses as a means of understanding their customers better than before. Data mining takes place in retailing and sales, banking, education, manufacturing and production, health care, insurance, broadcasting, marketing, customer services, and a number of other areas. The analytical information gathered by data-mining applications has given some businesses a competitive advantage, an ability to make informed decisions, and better ways to predict the behavior of customers. Write a four to five (4-5) page paper in which you:
1. Determine the benefits of data mining to the businesses when employing:
1. Predictive analytics to understand the behavior of customers
2. Associations discovery in products sold to customers
3. Web mining to discover business intelligence from Web customers
4. Clustering to find related customer information
2. Assess the reliability of the data mining algorithms. Decide if they can be trusted and predict the errors they are likely to produce.
3. Analyze privacy concerns raised by the collection of personal data for mining purposes.
1. Choose and describe three (3) concerns raised by consumers.
2. Decide if each of these concerns is valid and explain your decision for each.
3. Describe how each concern is being allayed.
4. Provide at least three (3) examples where businesses have used predictive analysis to gain a competitive advantage and evaluate the effectiveness of each business’s strategy.
5. Use at least three (3) quality resources in this assignment. Note: Wikipedia and similar Websites do not qualify as quality resources.
Your assignment must follow these formatting requirements:
· Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.
· Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length.
The specific course learning outcomes associated with this assignment are:
· Explain how information technology systems influence organizational strategies.
· Evaluate the ethical concerns that information technologies raise in a global context.
· Outline the challenges and strategies of e-Business and e-Commerce technology.
· Use technology and information resources to research issues in information systems and technology.
· Write clearly and concisely about topics related to information systems for decision making using proper writing mechanics and technical style conventions.
Grading for this assignment will be based on answer quality, ...
Assignment 4 Data MiningDue Week 9 and worth 75 points The .docx
1. Assignment 4: Data Mining
Due Week 9 and worth 75 points
The development of complex algorithms that can mine mounds
of data that have been collected from people and digital devices
have led to the adoption of data mining by most businesses as a
means of understanding their customers better than before. Data
mining takes place in retailing and sales, banking, education,
manufacturing and production, health care, insurance,
broadcasting, marketing, customer services, and a number of
other areas. The analytical information gathered by data-mining
applications has given some businesses a competitive
advantage, an ability to make informed decisions, and better
ways to predict the behavior of customers. Write a four to five
(4-5) page paper in which you:
1. Determine the benefits of data mining to the businesses when
employing:
1. Predictive analytics to understand the behavior of customers
2. Associations discovery in products sold to customers
3. Web mining to discover business intelligence from Web
customers
4. Clustering to find related customer information
2. Assess the reliability of the data mining algorithms. Decide if
they can be trusted and predict the errors they are likely to
produce.
3. Analyze privacy concerns raised by the collection of personal
data for mining purposes.
1. Choose and describe three (3) concerns raised by consumers.
2. Decide if each of these concerns is valid and explain your
decision for each.
3. Describe how each concern is being allayed.
4. Provide at least three (3) examples where businesses have
used predictive analysis to gain a competitive advantage and
evaluate the effectiveness of each business’s strategy.
2. 5. Use at least three (3) quality resources in this assignment.
Note: Wikipedia and similar Websites do not qualify as quality
resources.
Your assignment must follow these formatting requirements:
· Be typed, double spaced, using Times New Roman font (size
12), with one-inch margins on all sides; citations and references
must follow APA or school-specific format. Check with your
professor for any additional instructions.
· Include a cover page containing the title of the assignment, the
student’s name, the professor’s name, the course title, and the
date. The cover page and the reference page are not included in
the required assignment page length.
The specific course learning outcomes associated with this
assignment are:
· Explain how information technology systems influence
organizational strategies.
· Evaluate the ethical concerns that information technologies
raise in a global context.
· Outline the challenges and strategies of e-Business and e-
Commerce technology.
· Use technology and information resources to research issues in
information systems and technology.
· Write clearly and concisely about topics related to information
systems for decision making using proper writing mechanics
and technical style conventions.
Grading for this assignment will be based on answer quality,
logic / organization of the paper, and language and writing
skills.
Points: 75
Assignment 4: Data Mining
Criteria
Unacceptable
Below 70% F
3. Fair
70-79% C
Proficient
80-89% B
Exemplary
90-100% A
1a. Determine the benefits of data mining to the businesses
when employing predictive analytics to understand the behavior
of customers.
Weight: 5%
Did not submit or incompletely determined the benefits of data
mining to the businesses when employing predictive analytics to
understand the behavior of customers.
Partially determined the benefits of data mining to the
businesses when employing predictive analytics to understand
the behavior of customers.
Satisfactorily determined the benefits of data mining to the
businesses when employing predictive analytics to understand
the behavior of customers.
Thoroughly determined the benefits of data mining to the
businesses when employing predictive analytics to understand
the behavior of customers.
1b. Determine the benefits of data mining to the businesses
when employing associations discovery in products sold to
customers.
Weight: 5%
Did not submit or incompletely determined the benefits of data
mining to the businesses when employing associations
discovery in products sold to customers.
Partially determined the benefits of data mining to the
businesses when employing associations discovery in products
sold to customers.
Satisfactorily determined the benefits of data mining to the
businesses when employing associations discovery in products
sold to customers.
4. Thoroughly determined the benefits of data mining to the
businesses when employing associations discovery in products
sold to customers.
1c. Determine the benefits of data mining to the businesses
when employing Web mining to discover business intelligence
from Web customers.
Weight: 5%
Did not submit or incompletely determined the benefits of data
mining to the businesses when employing Web mining to
discover business intelligence from Web customers.
Partially determined the benefits of data mining to the
businesses when employing Web mining to discover business
intelligence from Web customers.
Satisfactorily determined the benefits of data mining to the
businesses when employing Web mining to discover business
intelligence from Web customers.
Thoroughly determined the benefits of data mining to the
businesses when employing Web mining to discover business
intelligence from Web customers.
1d. Determine the benefits of data mining to the businesses
when employing clustering to find related customer information.
Weight: 5%
Did not submit or incompletely determined the benefits of data
mining to the businesses when employing clustering to find
related customer information.
Partially determined the benefits of data mining to the
businesses when employing clustering to find related customer
information.
Satisfactorily determined the benefits of data mining to the
businesses when employing clustering to find related customer
information.
Thoroughly determined the benefits of data mining to the
businesses when employing clustering to find related customer
information.
5. 2. Assess the reliability of the data-mining algorithms. Decide
if they can be trusted and predict the errors they are likely to
produce.
Weight: 20%
Did not submit or incompletely assessed the reliability of the
data-mining algorithms. Did not submit or incompletely
decided if they can be trusted and did not submit or
incompletely predicted the errors they are likely to produce.
Partially assessed the reliability of the data-mining algorithms.
Partially decided if they can be trusted and partially predicted
the errors they are likely to produce.
Satisfactorily assessed the reliability of the data-mining
algorithms. Satisfactorily decided if they can be trusted and
satisfactorily predicted the errors they are likely to produce.
Thoroughly assessed the reliability of the data-mining
algorithms. Thoroughly decided if they can be trusted and
thoroughly predicted the errors they are likely to produce.
3a. Choose and describe three (3) concerns raised by the
collection of personal data for mining purposes.
Weight: 5%
Did not submit or incompletely chose and described three (3)
concerns raised by the collection of personal data for mining
purposes.
Partially chose and described three (3) concerns raised by the
collection of personal data for mining purposes.
Satisfactorily chose and described three (3) concerns raised by
the collection of personal data for mining purposes.
Thoroughly chose and described three (3) concerns raised by the
collection of personal data for mining purposes.
3b. Decide if each of these concerns is valid and explain your
decision for each.
Weight: 5%
Did not submit or incompletely decided if each of the concerns
is valid and did not submit or incompletely explained your
decision for each.
6. Partially decided if each of the concerns is valid and partially
explained your decision for each.
Satisfactorily decided if each of the concerns is valid and
satisfactorily explained your decision for each.
Thoroughly decided if each of the concerns is valid and
thoroughly explained your decision for each.
3c. Describe how each concern is being allayed.
Weight: 5%
Did not submit or incompletely described how each concern is
being allayed.
Partially described how each concern is being allayed.
Satisfactorily described how each concern is being allayed.
Thoroughly described how each concern is being allayed.
4. Provide at least three (3) examples where businesses have
used predictive analysis to gain a competitive advantage and
evaluate the effectiveness of each business’s strategy.
Weight: 30%
Did not submit or incompletely provided at least three (3)
examples where businesses have used predictive analysis to gain
a competitive advantage and did not submit or incompletely
evaluated the effectiveness of each business’s strategy.
Partially provided at least three (3) examples where businesses
have used predictive analysis to gain a competitive advantage
and partially evaluated the effectiveness of each business’s
strategy.
Satisfactorily provided at least three (3) examples where
businesses have used predictive analysis to gain a competitive
advantage and satisfactorily evaluated the effectiveness of each
business’s strategy.
Thoroughly provided at least three (3) examples where
businesses have used predictive analysis to gain a competitive
advantage and thoroughly evaluated the effectiveness of each
business’s strategy.
5. 3 references
Weight: 5%
No references provided
7. Does not meet the required number of references; some or all
references poor quality choices.
Meets number of required references; all references high quality
choices.
Exceeds number of required references; all references high
quality choices.
6. Clarity, writing mechanics, and formatting requirements
Weight: 10%
More than 6 errors present
5-6 errors present
3-4 errors present
0-2 errors present
Calculating Length of StayCalculating Mortality and Autopsy
Statistics
Using the Community Hospital Death Register, compute the
following rates. Be sure to show your computations.
1. Compute the gross autopsy rate for the period.
2. Compute the gross autopsy rate for medical patients. (NOTE:
Review the definitions for medical and surgical patients above,
for this assignment)
3. Compute the gross autopsy rate for surgical patients. (NOTE:
Review the definitions for medical and surgical patients above,
for this assignment)
4. Compute the gross autopsy rate for patients who expired
within two days of surgery.
5. Compute the percentage of total deaths that were surgical.