IIBM
CASE STUDY
What Are Different Stages Of “data Mining”?
Data Mining and
Predictive Analytics
a) Additional
acquaintance used by a learning algorithm to facilitate the learning process
b) A neural network that
makes use of a hidden layer
c) It is a form of
automatic learning.
d) None of these
Querying of unstructured
textual data is referred to as
a) Information access
b) Information updation
c) Information
manipulation
d) Information retrieval
III. A manual component
to data mining, consists of preprocessing data to a form acceptable to
a) Variables
b) Algorithms
c) Rules
d) Processes
A manual component to
data mining, consists t processing data in form of
a) Discovered processes
b) Discovered algorithms
c) Discovered features
d) Discovered patterns
Patterns that can be
discovered from a given database, can be of
a) One type only
b) No specific type
c) More than one type
d) Multiple type always
Analysis tools
precompute summaries of very large amounts of data, in order to give
a) Queries response
b) Data access
c) Authorization
d) Consistency
VII. Data can be store ,
retrieve and updated in …
a) SMTOP
b) OLTP
c) FTP
d) OLAP
VIII. Which of the
following is a good alternative to the star schema?
a) snow flake schema
b) star schema
c) star snow flake
schema
d) fact constellation
Background knowledge is…
a) It is a form of
automatic learning.
b) A neural network that
makes use of a hidden layer
c) The additional
acquaintance used by a learning algorithm to facilitate the learning process
d) None of these
Which of the following
is true for Classification?
a) A subdivision of a
set
b) A measure of the
accuracy
c) The task of assigning
a classification
d) All of these
Part Two:
What are data mining
techniques? (5)
What are the
applications of data mining? (5)
Why is data mining
important? (5)
Differentiate Between
Data Mining And Data Warehousing? (5)
Section B: Caselets (40
marks)
Caselet 1
User-generated content
is an indispensable part of today’s industry as every other company needs user data
to sell and buy products and provide the best possible support to its users and
clients. While user data is important, it needs to be processed to make it
relevant for the company. Data mining is the most important tool to process
such data and make it relevant and useful.
The decision tree
algorithm with the apriori algorithm can be used to support the needs of the
client.
To explain this problem,
we will turn to smart technology –something that makes our lives easier.
Whenever we install any application in our smartphone, we are asked for
permission for the installation, but we do not pay too much attention to the
information these application require to be installed. In the process, we
unknowingly disseminate varied information on maps, massages, contacts, etc.
With the help of this information the application, besides collating customer
data, also tries to support the users to make their life easier and at the same
time makes them dependent on the application in the near future.
Once the user information
is gathered, the data is analysed to get the required information so as to give
the best information to the algorithm at different times. This type of analysis
starts from data pre-processing steps, steps that have already been explained
in Chapters 1 and 2. However, for this type of data pre-processing the
information gain happens by designing the decision tree at different levels-the
depth decision tree or 2-10 level decision tree as well.
Each data gives a valid
point of information and these points are used in designing the clusters among
different types of data but they are very centric in information as they
provide the information of different users according to same contents. The
frequency of the matching data is processed by means of decision tree under
info gain and Apriori.
It is a common
experience nowadays for different applications to recommend the same item for
buying from different applications or portals, Users are also able to exercise
their choices when it comes to reading the news by selecting the content that
is more liked. Through their preferences, they provide the application
information about the cognitive behavior of users. This allows prediction of
the way a particular4 consumer behaves and recommendations are accordingly tweaked.
Most studies of systems or online reviews so far have used only numeric
information about sellers or products to examine their economic impact. The
understanding that text matters has not been fully realized in electronic
markets or in online communities. Insights derived from text mining of
user-generated feedback can thus provide substantial benefits to businesses
looking for competitive advantages.
Let us summarise some of
the chief benefits utiling user-centric data:
It saves money:
Since the users themselves provide relevant content for prediction and subsequent
recommendations, users data need not be bought and efficiency in terms of time
and costs in increased.
It provides variety:
By using the user data, the customer can be apprised of various new features or
upgrades to the existing product. Further, the user gets to know about the discounts
being offered and can avail the support extended to the end user.
It offers a voice to
the user: The company is in a position to offer individual customers different
products as per individual preferences and a user can provide any specific information
of the item he /she wants to use
These benefits of
user-centric data should be firmly kept in mind to make such data more predictive
and relevant in our fast-paced technological era.
Questions
What do you understand
by user generated content? (10)
Do you really think user
generated content is effective? (10)
Caselet 2
Big data is the
collection and cross-referencing of large numbers and varieties of data sets
that allows organizations to identify patterns and categories of cardholders
through a multitude of attributes and variables. Every time customers use their
cards, big data suggests the products that can be offered to the customers.
These days many credit card users receive calls from different companies
offering them new credit cards as per their needs and expenses on the existing
cards. This information is gathered on the basis of available data provided by
vendors.
There are quite a few
option available to customers to choose from. Sometimes customers even switch
their existing credit card companies. But competition may not always work in
the best interests of consumers. It also involves bank’s profit. Competition
may also be focused on particular features of credit cards that may not
represent long-term value or sustainability.
Those paying interest on
balances may be paying more than they realize or expect. Some consumers use up
their credit limits quickly or repeatedly make minimum payments without
considering how they will repay their credit card debt. A proportion of
consumers may also be over-borrowing and taking on too much debt, and there are
signs that some issuers may profit more from higher risk borrowers (by which we
mean customers at greater risk of credit default).
With the launch of this
credit card market study, we intend to build up a detailed picture of the
market and assess the potential identified issues. We plan to focus on credit
card services offered to retail consumers by credit card providers, including
banks, mono-line issuers and their affinity and co-brand partners.
While mass marketing
continues to dominate most retailers’ advertising budgets, one-to-one marketing
is growing rapidly too. In this case study, you will learn how to improve
performance by communicating directly with customers and delighting them with
relevant offers. Personalised communication is becoming a norm. Shoppers now
expect retailers to provide them with product information and promotional
offers that match their needs and desires. They count on you to know their
likes, dislikes and preferred communication method-mobile device, email or
print media.
On the surface,
generating customer-specific offers and communications seems like an unnerving
task for many retailers, but like many business problems, when broken into
manageable pieces, each process step or analytical procedure is attainable.
First, let’s assume you have assembled promotions that you intend to extend as
a group of offers (commonly called “offer bank”) to individual customers. Each
offer should have a business goal or objective, such as:
Category void for
cross or up-selling of a particular product or product group
Basket builder to
increase the customer’s basket size
Trip builder to
create an additional trip or visit to the store or an additional e-commerce session
Reward to offer an
incentive to loyal customers
Questions
How Big data used in
this case study- Define? (20)
Section C: Applied
Theory (30 marks)
What Are Olap And Oltp?
(15)
What Are Different
Stages Of “data Mining”? (15)
IIBM EMBA CASE LET ANSWER SHEETS – What Are Olap And Oltp
Data Mining and
Predictive Analytics
1.
a) Additional acquaintance used
by a learning algorithm to facilitate the learning process
2.
b) A neural network that makes
use of a hidden layer
3.
c) It is a form of automatic
learning.
4.
d) None of these
1.
Querying of unstructured
textual data is referred to as
2.
a) Information access
3.
b) Information updation
4.
c) Information manipulation
5.
d) Information retrieval
III. A manual component to data
mining, consists of preprocessing data to a form acceptable to
1.
a) Variables
2.
b) Algorithms
3.
c) Rules
4.
d) Processes
1.
A manual component to data
mining, consists t processing data in form of
2.
a) Discovered processes
3.
b) Discovered algorithms
4.
c) Discovered features
5.
d) Discovered patterns
1.
Patterns that can be discovered
from a given database, can be of
2.
a) One type only
3.
b) No specific type
4.
c) More than one type
5.
d) Multiple type always
1.
Analysis tools precompute
summaries of very large amounts of data, in order to give
2.
a) Queries response
3.
b) Data access
4.
c) Authorization
5.
d) Consistency
VII. Data can be store ,
retrieve and updated in …
1.
a) SMTOP
2.
b) OLTP
3.
c) FTP
4.
d) OLAP
VIII. Which of the following is
a good alternative to the star schema?
1.
a) snow flake schema
2.
b) star schema
3.
c) star snow flake schema
4.
d) fact constellation
1.
Background knowledge is…
2.
a) It is a form of automatic
learning.
3.
b) A neural network that makes
use of a hidden layer
4.
c) The additional acquaintance
used by a learning algorithm to facilitate the learning process
5.
d) None of these
1.
Which of the following is true
for Classification?
2.
a) A subdivision of a set
3.
b) A measure of the accuracy
4.
c) The task of assigning a
classification
5.
d) All of these
Part Two:
1.
What are data mining
techniques? (5)
2.
What are the applications of
data mining? (5)
3.
Why is data mining important?
(5)
4.
Differentiate Between Data
Mining And Data Warehousing? (5)
Section B: Caselets (40 marks)
Caselet 1
User-generated content is an
indispensable part of today’s industry as every other company needs user data
to sell and buy products and provide the best possible support to its users and
clients. While user data is important, it needs to be processed to make it
relevant for the company. Data mining is the most important tool to process
such data and make it relevant and useful.
The decision tree algorithm
with the apriori algorithm can be used to support the needs of the client.
To explain this problem, we
will turn to smart technology –something that makes our lives easier. Whenever
we install any application in our smartphone, we are asked for permission for
the installation, but we do not pay too much attention to the information these
application require to be installed. In the process, we unknowingly disseminate
varied information on maps, massages, contacts, etc. With the help of this
information the application, besides collating customer data, also tries to support
the users to make their life easier and at the same time makes them dependent
on the application in the near future.
Once the user information is
gathered, the data is analysed to get the required information so as to give
the best information to the algorithm at different times. This type of analysis
starts from data pre-processing steps, steps that have already been explained
in Chapters 1 and 2. However, for this type of data pre-processing the
information gain happens by designing the decision tree at different levels-the
depth decision tree or 2-10 level decision tree as well.
Each data gives a valid point
of information and these points are used in designing the clusters among
different types of data but they are very centric in information as they provide
the information of different users according to same contents. The frequency of
the matching data is processed by means of decision tree under info gain and
Apriori.
It is a common experience
nowadays for different applications to recommend the same item for buying from
different applications or portals, Users are also able to exercise their
choices when it comes to reading the news by selecting the content that is more
liked. Through their preferences, they provide the application information about
the cognitive behavior of users. This allows prediction of the way a
particular4 consumer behaves and recommendations are accordingly tweaked. Most
studies of systems or online reviews so far have used only numeric information
about sellers or products to examine their economic impact. The understanding
that text matters has not been fully realized in electronic markets or in
online communities. Insights derived from text mining of user-generated
feedback can thus provide substantial benefits to businesses looking for
competitive advantages.
Let us summarise some of the
chief benefits utiling user-centric data:
It saves money: Since the users themselves
provide relevant content for prediction and subsequent recommendations, users
data need not be bought and efficiency in terms of time and costs in increased.·
It provides variety: By using the user data,
the customer can be apprised of various new features or upgrades to the
existing product. Further, the user gets to know about the discounts being
offered and can avail the support extended to the end user.·
It offers a voice to the user: The company is
in a position to offer individual customers different products as per
individual preferences and a user can provide any specific information of the
item he /she wants to use·
These benefits of user-centric
data should be firmly kept in mind to make such data more predictive and
relevant in our fast-paced technological era.
Questions
1.
What do you understand by user
generated content? (10)
2.
Do you really think user
generated content is effective? (10)
Caselet 2
Big data is the collection and
cross-referencing of large numbers and varieties of data sets that allows
organizations to identify patterns and categories of cardholders through a
multitude of attributes and variables. Every time customers use their cards, big
data suggests the products that can be offered to the customers. These days
many credit card users receive calls from different companies offering them new
credit cards as per their needs and expenses on the existing cards. This
information is gathered on the basis of available data provided by vendors.
There are quite a few option
available to customers to choose from. Sometimes customers even switch their
existing credit card companies. But competition may not always work in the best
interests of consumers. It also involves bank’s profit. Competition may also be
focused on particular features of credit cards that may not represent long-term
value or sustainability.
Those paying interest on
balances may be paying more than they realize or expect. Some consumers use up
their credit limits quickly or repeatedly make minimum payments without
considering how they will repay their credit card debt. A proportion of
consumers may also be over-borrowing and taking on too much debt, and there are
signs that some issuers may profit more from higher risk borrowers (by which we
mean customers at greater risk of credit default).
With the launch of this credit
card market study, we intend to build up a detailed picture of the market and
assess the potential identified issues. We plan to focus on credit card
services offered to retail consumers by credit card providers, including banks,
mono-line issuers and their affinity and co-brand partners.
While mass marketing continues
to dominate most retailers’ advertising budgets, one-to-one marketing is
growing rapidly too. In this case study, you will learn how to improve
performance by communicating directly with customers and delighting them with
relevant offers. Personalised communication is becoming a norm. Shoppers now
expect retailers to provide them with product information and promotional
offers that match their needs and desires. They count on you to know their
likes, dislikes and preferred communication method-mobile device, email or
print media.
On the surface, generating
customer-specific offers and communications seems like an unnerving task for
many retailers, but like many business problems, when broken into manageable
pieces, each process step or analytical procedure is attainable. First, let’s
assume you have assembled promotions that you intend to extend as a group of
offers (commonly called “offer bank”) to individual customers. Each offer
should have a business goal or objective, such as:
Category void for cross or up-selling of a
particular product or product group·
Basket builder to increase the customer’s
basket size·
Trip builder to create an additional trip or
visit to the store or an additional e-commerce session·
Reward to offer an incentive to loyal
customers·
Questions
1.
How Big data used in this case
study- Define? (20)
Section C: Applied Theory (30
marks)
1.
What Are Olap And Oltp? (15)
2.
What Are Different Stages Of
“data Mining”? (15)
Data Mining and
Predictive Analytics
1.
a) Additional acquaintance used
by a learning algorithm to facilitate the learning process
2.
b) A neural network that makes
use of a hidden layer
3.
c) It is a form of automatic
learning.
4.
d) None of these
1.
Querying of unstructured
textual data is referred to as
2.
a) Information access
3.
b) Information updation
4.
c) Information manipulation
5.
d) Information retrieval
III. A manual component to data
mining, consists of preprocessing data to a form acceptable to
1.
a) Variables
2.
b) Algorithms
3.
c) Rules
4.
d) Processes
1.
A manual component to data
mining, consists t processing data in form of
2.
a) Discovered processes
3.
b) Discovered algorithms
4.
c) Discovered features
5.
d) Discovered patterns
1.
Patterns that can be discovered
from a given database, can be of
2.
a) One type only
3.
b) No specific type
4.
c) More than one type
5.
d) Multiple type always
1.
Analysis tools precompute
summaries of very large amounts of data, in order to give
2.
a) Queries response
3.
b) Data access
4.
c) Authorization
5.
d) Consistency
VII. Data can be store ,
retrieve and updated in …
1.
a) SMTOP
2.
b) OLTP
3.
c) FTP
4.
d) OLAP
VIII. Which of the following is
a good alternative to the star schema?
1.
a) snow flake schema
2.
b) star schema
3.
c) star snow flake schema
4.
d) fact constellation
1.
Background knowledge is…
2.
a) It is a form of automatic
learning.
3.
b) A neural network that makes
use of a hidden layer
4.
c) The additional acquaintance used
by a learning algorithm to facilitate the learning process
5.
d) None of these
1.
Which of the following is true
for Classification?
2.
a) A subdivision of a set
3.
b) A measure of the accuracy
4.
c) The task of assigning a
classification
5.
d) All of these
Part Two:
1.
What are data mining
techniques? (5)
2.
What are the applications of
data mining? (5)
3.
Why is data mining important?
(5)
4.
Differentiate Between Data
Mining And Data Warehousing? (5)
Section B: Caselets (40 marks)
Caselet 1
User-generated content is an indispensable
part of today’s industry as every other company needs user data to sell and buy
products and provide the best possible support to its users and clients. While
user data is important, it needs to be processed to make it relevant for the
company. Data mining is the most important tool to process such data and make
it relevant and useful.
The decision tree algorithm
with the apriori algorithm can be used to support the needs of the client.
To explain this problem, we
will turn to smart technology –something that makes our lives easier. Whenever
we install any application in our smartphone, we are asked for permission for
the installation, but we do not pay too much attention to the information these
application require to be installed. In the process, we unknowingly disseminate
varied information on maps, massages, contacts, etc. With the help of this
information the application, besides collating customer data, also tries to
support the users to make their life easier and at the same time makes them
dependent on the application in the near future.
Once the user information is
gathered, the data is analysed to get the required information so as to give
the best information to the algorithm at different times. This type of analysis
starts from data pre-processing steps, steps that have already been explained
in Chapters 1 and 2. However, for this type of data pre-processing the
information gain happens by designing the decision tree at different levels-the
depth decision tree or 2-10 level decision tree as well.
Each data gives a valid point
of information and these points are used in designing the clusters among
different types of data but they are very centric in information as they
provide the information of different users according to same contents. The
frequency of the matching data is processed by means of decision tree under
info gain and Apriori.
It is a common experience
nowadays for different applications to recommend the same item for buying from
different applications or portals, Users are also able to exercise their
choices when it comes to reading the news by selecting the content that is more
liked. Through their preferences, they provide the application information
about the cognitive behavior of users. This allows prediction of the way a
particular4 consumer behaves and recommendations are accordingly tweaked. Most
studies of systems or online reviews so far have used only numeric information
about sellers or products to examine their economic impact. The understanding
that text matters has not been fully realized in electronic markets or in
online communities. Insights derived from text mining of user-generated
feedback can thus provide substantial benefits to businesses looking for
competitive advantages.
Let us summarise some of the chief
benefits utiling user-centric data:
It saves money: Since the users themselves
provide relevant content for prediction and subsequent recommendations, users
data need not be bought and efficiency in terms of time and costs in increased.·
It provides variety: By using the user data,
the customer can be apprised of various new features or upgrades to the
existing product. Further, the user gets to know about the discounts being
offered and can avail the support extended to the end user.·
It offers a voice to the user: The company is
in a position to offer individual customers different products as per
individual preferences and a user can provide any specific information of the
item he /she wants to use·
These benefits of user-centric
data should be firmly kept in mind to make such data more predictive and
relevant in our fast-paced technological era.
Questions
1.
What do you understand by user
generated content? (10)
2.
Do you really think user
generated content is effective? (10)
Caselet 2
Big data is the collection and
cross-referencing of large numbers and varieties of data sets that allows
organizations to identify patterns and categories of cardholders through a
multitude of attributes and variables. Every time customers use their cards,
big data suggests the products that can be offered to the customers. These days
many credit card users receive calls from different companies offering them new
credit cards as per their needs and expenses on the existing cards. This
information is gathered on the basis of available data provided by vendors.
There are quite a few option
available to customers to choose from. Sometimes customers even switch their
existing credit card companies. But competition may not always work in the best
interests of consumers. It also involves bank’s profit. Competition may also be
focused on particular features of credit cards that may not represent long-term
value or sustainability.
Those paying interest on
balances may be paying more than they realize or expect. Some consumers use up
their credit limits quickly or repeatedly make minimum payments without
considering how they will repay their credit card debt. A proportion of
consumers may also be over-borrowing and taking on too much debt, and there are
signs that some issuers may profit more from higher risk borrowers (by which we
mean customers at greater risk of credit default).
With the launch of this credit
card market study, we intend to build up a detailed picture of the market and
assess the potential identified issues. We plan to focus on credit card
services offered to retail consumers by credit card providers, including banks,
mono-line issuers and their affinity and co-brand partners.
While mass marketing continues
to dominate most retailers’ advertising budgets, one-to-one marketing is
growing rapidly too. In this case study, you will learn how to improve
performance by communicating directly with customers and delighting them with
relevant offers. Personalised communication is becoming a norm. Shoppers now
expect retailers to provide them with product information and promotional
offers that match their needs and desires. They count on you to know their
likes, dislikes and preferred communication method-mobile device, email or
print media.
On the surface, generating
customer-specific offers and communications seems like an unnerving task for
many retailers, but like many business problems, when broken into manageable
pieces, each process step or analytical procedure is attainable. First, let’s
assume you have assembled promotions that you intend to extend as a group of
offers (commonly called “offer bank”) to individual customers. Each offer
should have a business goal or objective, such as:
Category void for cross or up-selling of a
particular product or product group·
Basket builder to increase the customer’s
basket size·
Trip builder to create an additional trip or
visit to the store or an additional e-commerce session·
Reward to offer an incentive to loyal
customers·
Questions
1.
How Big data used in this case
study- Define? (20)
Section C: Applied Theory (30
marks)
1.
What Are Olap And Oltp? (15)
2.
What Are Different Stages Of
“data Mining”? (15)
MIB IIBM CASE STUDY
Big data is the collection and cross-referencing of large numbers
and varieties of data sets that allows organizations to identify patterns and
categories of cardholders through a multitude of attributes and variables.
Data Mining and
Predictive Analytics
1.
a) Additional acquaintance used
by a learning algorithm to facilitate the learning process
2.
b) A neural network that makes
use of a hidden layer
3.
c) It is a form of automatic
learning.
4.
d) None of these
1.
Querying of unstructured
textual data is referred to as
2.
a) Information access
3.
b) Information updation
4.
c) Information manipulation
5.
d) Information retrieval
III. A manual component to data
mining, consists of preprocessing data to a form acceptable to
1.
a) Variables
2.
b) Algorithms
3.
c) Rules
4.
d) Processes
1.
A manual component to data
mining, consists t processing data in form of
2.
a) Discovered processes
3.
b) Discovered algorithms
4.
c) Discovered features
5.
d) Discovered patterns
1.
Patterns that can be discovered
from a given database, can be of
2.
a) One type only
3.
b) No specific type
4.
c) More than one type
5.
d) Multiple type always
1.
Analysis tools precompute summaries
of very large amounts of data, in order to give
2.
a) Queries response
3.
b) Data access
4.
c) Authorization
5.
d) Consistency
VII. Data can be store ,
retrieve and updated in …
1.
a) SMTOP
2.
b) OLTP
3.
c) FTP
4.
d) OLAP
VIII. Which of the following is
a good alternative to the star schema?
1.
a) snow flake schema
2.
b) star schema
3.
c) star snow flake schema
4.
d) fact constellation
1.
Background knowledge is…
2.
a) It is a form of automatic
learning.
3.
b) A neural network that makes
use of a hidden layer
4.
c) The additional acquaintance
used by a learning algorithm to facilitate the learning process
5.
d) None of these
1.
Which of the following is true
for Classification?
2.
a) A subdivision of a set
3.
b) A measure of the accuracy
4.
c) The task of assigning a
classification
5.
d) All of these
Part Two:
1.
What are data mining
techniques? (5)
2.
What are the applications of
data mining? (5)
3.
Why is data mining important?
(5)
4.
Differentiate Between Data
Mining And Data Warehousing? (5)
Section B: Caselets (40 marks)
Caselet 1
User-generated content is an
indispensable part of today’s industry as every other company needs user data
to sell and buy products and provide the best possible support to its users and
clients. While user data is important, it needs to be processed to make it
relevant for the company. Data mining is the most important tool to process
such data and make it relevant and useful.
The decision tree algorithm
with the apriori algorithm can be used to support the needs of the client.
To explain this problem, we
will turn to smart technology –something that makes our lives easier. Whenever
we install any application in our smartphone, we are asked for permission for
the installation, but we do not pay too much attention to the information these
application require to be installed. In the process, we unknowingly disseminate
varied information on maps, massages, contacts, etc. With the help of this
information the application, besides collating customer data, also tries to
support the users to make their life easier and at the same time makes them
dependent on the application in the near future.
Once the user information is
gathered, the data is analysed to get the required information so as to give
the best information to the algorithm at different times. This type of analysis
starts from data pre-processing steps, steps that have already been explained
in Chapters 1 and 2. However, for this type of data pre-processing the
information gain happens by designing the decision tree at different levels-the
depth decision tree or 2-10 level decision tree as well.
Each data gives a valid point
of information and these points are used in designing the clusters among
different types of data but they are very centric in information as they
provide the information of different users according to same contents. The
frequency of the matching data is processed by means of decision tree under
info gain and Apriori.
It is a common experience
nowadays for different applications to recommend the same item for buying from
different applications or portals, Users are also able to exercise their
choices when it comes to reading the news by selecting the content that is more
liked. Through their preferences, they provide the application information
about the cognitive behavior of users. This allows prediction of the way a
particular4 consumer behaves and recommendations are accordingly tweaked. Most
studies of systems or online reviews so far have used only numeric information
about sellers or products to examine their economic impact. The understanding
that text matters has not been fully realized in electronic markets or in
online communities. Insights derived from text mining of user-generated
feedback can thus provide substantial benefits to businesses looking for
competitive advantages.
Let us summarise some of the chief
benefits utiling user-centric data:
It saves money: Since the users themselves
provide relevant content for prediction and subsequent recommendations, users
data need not be bought and efficiency in terms of time and costs in increased.·
It provides variety: By using the user data,
the customer can be apprised of various new features or upgrades to the
existing product. Further, the user gets to know about the discounts being
offered and can avail the support extended to the end user.·
It offers a voice to the user: The company is
in a position to offer individual customers different products as per
individual preferences and a user can provide any specific information of the
item he /she wants to use·
These benefits of user-centric
data should be firmly kept in mind to make such data more predictive and
relevant in our fast-paced technological era.
Questions
1.
What do you understand by user
generated content? (10)
2.
Do you really think user
generated content is effective? (10)
Caselet 2
Big data is the collection and
cross-referencing of large numbers and varieties of data sets that allows
organizations to identify patterns and categories of cardholders through a
multitude of attributes and variables. Every time customers use their cards, big
data suggests the products that can be offered to the customers. These days
many credit card users receive calls from different companies offering them new
credit cards as per their needs and expenses on the existing cards. This
information is gathered on the basis of available data provided by vendors.
There are quite a few option
available to customers to choose from. Sometimes customers even switch their
existing credit card companies. But competition may not always work in the best
interests of consumers. It also involves bank’s profit. Competition may also be
focused on particular features of credit cards that may not represent long-term
value or sustainability.
Those paying interest on
balances may be paying more than they realize or expect. Some consumers use up
their credit limits quickly or repeatedly make minimum payments without
considering how they will repay their credit card debt. A proportion of
consumers may also be over-borrowing and taking on too much debt, and there are
signs that some issuers may profit more from higher risk borrowers (by which we
mean customers at greater risk of credit default).
With the launch of this credit
card market study, we intend to build up a detailed picture of the market and
assess the potential identified issues. We plan to focus on credit card
services offered to retail consumers by credit card providers, including banks,
mono-line issuers and their affinity and co-brand partners.
While mass marketing continues
to dominate most retailers’ advertising budgets, one-to-one marketing is
growing rapidly too. In this case study, you will learn how to improve
performance by communicating directly with customers and delighting them with
relevant offers. Personalised communication is becoming a norm. Shoppers now
expect retailers to provide them with product information and promotional
offers that match their needs and desires. They count on you to know their
likes, dislikes and preferred communication method-mobile device, email or
print media.
On the surface, generating
customer-specific offers and communications seems like an unnerving task for
many retailers, but like many business problems, when broken into manageable
pieces, each process step or analytical procedure is attainable. First, let’s
assume you have assembled promotions that you intend to extend as a group of
offers (commonly called “offer bank”) to individual customers. Each offer
should have a business goal or objective, such as:
Category void for cross or up-selling of a
particular product or product group·
Basket builder to increase the customer’s
basket size·
Trip builder to create an additional trip or
visit to the store or an additional e-commerce session·
Reward to offer an incentive to loyal
customers·
Questions
1.
How Big data used in this case
study- Define? (20)
Section C: Applied Theory (30
marks)
1.
What Are Olap And Oltp? (15)
2.
What Are Different Stages Of
“data Mining”? (15)
IIBM EMBA CASE LET ANSWER SHEETS – What do you understand by user
generated content
Data Mining and
Predictive Analytics
1.
a) Additional acquaintance used
by a learning algorithm to facilitate the learning process
2.
b) A neural network that makes
use of a hidden layer
3.
c) It is a form of automatic
learning.
4.
d) None of these
1.
Querying of unstructured
textual data is referred to as
2.
a) Information access
3.
b) Information updation
4.
c) Information manipulation
5.
d) Information retrieval
III. A manual component to data
mining, consists of preprocessing data to a form acceptable to
1.
a) Variables
2.
b) Algorithms
3.
c) Rules
4.
d) Processes
1.
A manual component to data
mining, consists t processing data in form of
2.
a) Discovered processes
3.
b) Discovered algorithms
4.
c) Discovered features
5.
d) Discovered patterns
1.
Patterns that can be discovered
from a given database, can be of
2.
a) One type only
3.
b) No specific type
4.
c) More than one type
5.
d) Multiple type always
1.
Analysis tools precompute
summaries of very large amounts of data, in order to give
2.
a) Queries response
3.
b) Data access
4.
c) Authorization
5.
d) Consistency
VII. Data can be store , retrieve
and updated in …
1.
a) SMTOP
2.
b) OLTP
3.
c) FTP
4.
d) OLAP
VIII. Which of the following is
a good alternative to the star schema?
1.
a) snow flake schema
2.
b) star schema
3.
c) star snow flake schema
4.
d) fact constellation
1.
Background knowledge is…
2.
a) It is a form of automatic
learning.
3.
b) A neural network that makes
use of a hidden layer
4.
c) The additional acquaintance
used by a learning algorithm to facilitate the learning process
5.
d) None of these
1.
Which of the following is true
for Classification?
2.
a) A subdivision of a set
3.
b) A measure of the accuracy
4.
c) The task of assigning a
classification
5.
d) All of these
Part Two:
1.
What are data mining
techniques? (5)
2.
What are the applications of
data mining? (5)
3.
Why is data mining important?
(5)
4.
Differentiate Between Data
Mining And Data Warehousing? (5)
Section B: Caselets (40 marks)
Caselet 1
User-generated content is an
indispensable part of today’s industry as every other company needs user data
to sell and buy products and provide the best possible support to its users and
clients. While user data is important, it needs to be processed to make it
relevant for the company. Data mining is the most important tool to process
such data and make it relevant and useful.
The decision tree algorithm
with the apriori algorithm can be used to support the needs of the client.
To explain this problem, we
will turn to smart technology –something that makes our lives easier. Whenever
we install any application in our smartphone, we are asked for permission for
the installation, but we do not pay too much attention to the information these
application require to be installed. In the process, we unknowingly disseminate
varied information on maps, massages, contacts, etc. With the help of this
information the application, besides collating customer data, also tries to
support the users to make their life easier and at the same time makes them
dependent on the application in the near future.
Once the user information is
gathered, the data is analysed to get the required information so as to give the
best information to the algorithm at different times. This type of analysis
starts from data pre-processing steps, steps that have already been explained
in Chapters 1 and 2. However, for this type of data pre-processing the
information gain happens by designing the decision tree at different levels-the
depth decision tree or 2-10 level decision tree as well.
Each data gives a valid point
of information and these points are used in designing the clusters among
different types of data but they are very centric in information as they
provide the information of different users according to same contents. The
frequency of the matching data is processed by means of decision tree under
info gain and Apriori.
It is a common experience
nowadays for different applications to recommend the same item for buying from
different applications or portals, Users are also able to exercise their
choices when it comes to reading the news by selecting the content that is more
liked. Through their preferences, they provide the application information
about the cognitive behavior of users. This allows prediction of the way a
particular4 consumer behaves and recommendations are accordingly tweaked. Most
studies of systems or online reviews so far have used only numeric information about
sellers or products to examine their economic impact. The understanding that
text matters has not been fully realized in electronic markets or in online
communities. Insights derived from text mining of user-generated feedback can
thus provide substantial benefits to businesses looking for competitive
advantages.
Let us summarise some of the
chief benefits utiling user-centric data:
It saves money: Since the users themselves
provide relevant content for prediction and subsequent recommendations, users
data need not be bought and efficiency in terms of time and costs in increased.·
It provides variety: By using the user data,
the customer can be apprised of various new features or upgrades to the
existing product. Further, the user gets to know about the discounts being
offered and can avail the support extended to the end user.·
It offers a voice to the user: The company is
in a position to offer individual customers different products as per
individual preferences and a user can provide any specific information of the
item he /she wants to use·
These benefits of user-centric
data should be firmly kept in mind to make such data more predictive and
relevant in our fast-paced technological era.
Questions
1.
What do you understand by user
generated content? (10)
2.
Do you really think user
generated content is effective? (10)
Caselet 2
Big data is the collection and
cross-referencing of large numbers and varieties of data sets that allows
organizations to identify patterns and categories of cardholders through a
multitude of attributes and variables. Every time customers use their cards,
big data suggests the products that can be offered to the customers. These days
many credit card users receive calls from different companies offering them new
credit cards as per their needs and expenses on the existing cards. This
information is gathered on the basis of available data provided by vendors.
There are quite a few option
available to customers to choose from. Sometimes customers even switch their
existing credit card companies. But competition may not always work in the best
interests of consumers. It also involves bank’s profit. Competition may also be
focused on particular features of credit cards that may not represent long-term
value or sustainability.
Those paying interest on
balances may be paying more than they realize or expect. Some consumers use up
their credit limits quickly or repeatedly make minimum payments without
considering how they will repay their credit card debt. A proportion of
consumers may also be over-borrowing and taking on too much debt, and there are
signs that some issuers may profit more from higher risk borrowers (by which we
mean customers at greater risk of credit default).
With the launch of this credit
card market study, we intend to build up a detailed picture of the market and
assess the potential identified issues. We plan to focus on credit card
services offered to retail consumers by credit card providers, including banks,
mono-line issuers and their affinity and co-brand partners.
While mass marketing continues
to dominate most retailers’ advertising budgets, one-to-one marketing is
growing rapidly too. In this case study, you will learn how to improve
performance by communicating directly with customers and delighting them with
relevant offers. Personalised communication is becoming a norm. Shoppers now expect
retailers to provide them with product information and promotional offers that
match their needs and desires. They count on you to know their likes, dislikes
and preferred communication method-mobile device, email or print media.
On the surface, generating
customer-specific offers and communications seems like an unnerving task for
many retailers, but like many business problems, when broken into manageable
pieces, each process step or analytical procedure is attainable. First, let’s
assume you have assembled promotions that you intend to extend as a group of
offers (commonly called “offer bank”) to individual customers. Each offer
should have a business goal or objective, such as:
Category void for cross or up-selling of a
particular product or product group·
Basket builder to increase the customer’s
basket size·
Trip builder to create an additional trip or
visit to the store or an additional e-commerce session·
Reward to offer an incentive to loyal
customers·
Questions
1.
How Big data used in this case
study- Define? (20)
Section C: Applied Theory (30
marks)
1.
What Are Olap And Oltp? (15)
2.
What Are Different Stages Of
“data Mining”? (15)
EMBA IIBM ANSWER SHEETS – Differentiate Between Data Mining And
Data Warehousing
Data Mining and
Predictive Analytics
1.
a) Additional acquaintance used
by a learning algorithm to facilitate the learning process
2.
b) A neural network that makes
use of a hidden layer
3.
c) It is a form of automatic
learning.
4.
d) None of these
1.
Querying of unstructured textual
data is referred to as
2.
a) Information access
3.
b) Information updation
4.
c) Information manipulation
5.
d) Information retrieval
III. A manual component to data
mining, consists of preprocessing data to a form acceptable to
1.
a) Variables
2.
b) Algorithms
3.
c) Rules
4.
d) Processes
1.
A manual component to data
mining, consists t processing data in form of
2.
a) Discovered processes
3.
b) Discovered algorithms
4.
c) Discovered features
5.
d) Discovered patterns
1.
Patterns that can be discovered
from a given database, can be of
2.
a) One type only
3.
b) No specific type
4.
c) More than one type
5.
d) Multiple type always
1.
Analysis tools precompute
summaries of very large amounts of data, in order to give
2.
a) Queries response
3.
b) Data access
4.
c) Authorization
5.
d) Consistency
VII. Data can be store ,
retrieve and updated in …
1.
a) SMTOP
2.
b) OLTP
3.
c) FTP
4.
d) OLAP
VIII. Which of the following is
a good alternative to the star schema?
1.
a) snow flake schema
2.
b) star schema
3.
c) star snow flake schema
4.
d) fact constellation
1.
Background knowledge is…
2.
a) It is a form of automatic
learning.
3.
b) A neural network that makes
use of a hidden layer
4.
c) The additional acquaintance
used by a learning algorithm to facilitate the learning process
5.
d) None of these
1.
Which of the following is true
for Classification?
2.
a) A subdivision of a set
3.
b) A measure of the accuracy
4.
c) The task of assigning a
classification
5.
d) All of these
Part Two:
1.
What are data mining
techniques? (5)
2.
What are the applications of
data mining? (5)
3.
Why is data mining important?
(5)
4.
Differentiate Between Data
Mining And Data Warehousing? (5)
Section B: Caselets (40 marks)
Caselet 1
User-generated content is an
indispensable part of today’s industry as every other company needs user data
to sell and buy products and provide the best possible support to its users and
clients. While user data is important, it needs to be processed to make it
relevant for the company. Data mining is the most important tool to process
such data and make it relevant and useful.
The decision tree algorithm
with the apriori algorithm can be used to support the needs of the client.
To explain this problem, we
will turn to smart technology –something that makes our lives easier. Whenever
we install any application in our smartphone, we are asked for permission for
the installation, but we do not pay too much attention to the information these
application require to be installed. In the process, we unknowingly disseminate
varied information on maps, massages, contacts, etc. With the help of this
information the application, besides collating customer data, also tries to
support the users to make their life easier and at the same time makes them
dependent on the application in the near future.
Once the user information is
gathered, the data is analysed to get the required information so as to give the
best information to the algorithm at different times. This type of analysis
starts from data pre-processing steps, steps that have already been explained
in Chapters 1 and 2. However, for this type of data pre-processing the
information gain happens by designing the decision tree at different levels-the
depth decision tree or 2-10 level decision tree as well.
Each data gives a valid point
of information and these points are used in designing the clusters among
different types of data but they are very centric in information as they
provide the information of different users according to same contents. The
frequency of the matching data is processed by means of decision tree under
info gain and Apriori.
It is a common experience
nowadays for different applications to recommend the same item for buying from
different applications or portals, Users are also able to exercise their
choices when it comes to reading the news by selecting the content that is more
liked. Through their preferences, they provide the application information
about the cognitive behavior of users. This allows prediction of the way a
particular4 consumer behaves and recommendations are accordingly tweaked. Most
studies of systems or online reviews so far have used only numeric information
about sellers or products to examine their economic impact. The understanding
that text matters has not been fully realized in electronic markets or in
online communities. Insights derived from text mining of user-generated
feedback can thus provide substantial benefits to businesses looking for
competitive advantages.
Let us summarise some of the
chief benefits utiling user-centric data:
It saves money: Since the users themselves
provide relevant content for prediction and subsequent recommendations, users
data need not be bought and efficiency in terms of time and costs in increased.·
It provides variety: By using the user data,
the customer can be apprised of various new features or upgrades to the
existing product. Further, the user gets to know about the discounts being
offered and can avail the support extended to the end user.·
It offers a voice to the user: The company is
in a position to offer individual customers different products as per
individual preferences and a user can provide any specific information of the
item he /she wants to use·
These benefits of user-centric
data should be firmly kept in mind to make such data more predictive and
relevant in our fast-paced technological era.
Questions
1.
What do you understand by user
generated content? (10)
2.
Do you really think user
generated content is effective? (10)
Caselet 2
Big data is the collection and
cross-referencing of large numbers and varieties of data sets that allows
organizations to identify patterns and categories of cardholders through a
multitude of attributes and variables. Every time customers use their cards,
big data suggests the products that can be offered to the customers. These days
many credit card users receive calls from different companies offering them new
credit cards as per their needs and expenses on the existing cards. This
information is gathered on the basis of available data provided by vendors.
There are quite a few option
available to customers to choose from. Sometimes customers even switch their
existing credit card companies. But competition may not always work in the best
interests of consumers. It also involves bank’s profit. Competition may also be
focused on particular features of credit cards that may not represent long-term
value or sustainability.
Those paying interest on
balances may be paying more than they realize or expect. Some consumers use up
their credit limits quickly or repeatedly make minimum payments without
considering how they will repay their credit card debt. A proportion of
consumers may also be over-borrowing and taking on too much debt, and there are
signs that some issuers may profit more from higher risk borrowers (by which we
mean customers at greater risk of credit default).
With the launch of this credit
card market study, we intend to build up a detailed picture of the market and
assess the potential identified issues. We plan to focus on credit card
services offered to retail consumers by credit card providers, including banks,
mono-line issuers and their affinity and co-brand partners.
While mass marketing continues
to dominate most retailers’ advertising budgets, one-to-one marketing is
growing rapidly too. In this case study, you will learn how to improve
performance by communicating directly with customers and delighting them with
relevant offers. Personalised communication is becoming a norm. Shoppers now
expect retailers to provide them with product information and promotional
offers that match their needs and desires. They count on you to know their
likes, dislikes and preferred communication method-mobile device, email or
print media.
On the surface, generating
customer-specific offers and communications seems like an unnerving task for
many retailers, but like many business problems, when broken into manageable
pieces, each process step or analytical procedure is attainable. First, let’s
assume you have assembled promotions that you intend to extend as a group of
offers (commonly called “offer bank”) to individual customers. Each offer
should have a business goal or objective, such as:
Category void for cross or up-selling of a
particular product or product group·
Basket builder to increase the customer’s
basket size·
Trip builder to create an additional trip or
visit to the store or an additional e-commerce session·
Reward to offer an incentive to loyal
customers·
Questions
1.
How Big data used in this case
study- Define? (20)
Section C: Applied Theory (30
marks)
1.
What Are Olap And Oltp? (15)
2.
What Are Different Stages Of
“data Mining”? (15)
IIBM EMBA CASE LET ANSWER SHEETS – Why is data mining important
Data Mining and
Predictive Analytics
1.
a) Additional acquaintance used
by a learning algorithm to facilitate the learning process
2.
b) A neural network that makes
use of a hidden layer
3.
c) It is a form of automatic
learning.
4.
d) None of these
1.
Querying of unstructured
textual data is referred to as
2.
a) Information access
3.
b) Information updation
4.
c) Information manipulation
5.
d) Information retrieval
III. A manual component to data
mining, consists of preprocessing data to a form acceptable to
1.
a) Variables
2.
b) Algorithms
3.
c) Rules
4.
d) Processes
1.
A manual component to data
mining, consists t processing data in form of
2.
a) Discovered processes
3.
b) Discovered algorithms
4.
c) Discovered features
5.
d) Discovered patterns
1.
Patterns that can be discovered
from a given database, can be of
2.
a) One type only
3.
b) No specific type
4.
c) More than one type
5.
d) Multiple type always
1.
Analysis tools precompute
summaries of very large amounts of data, in order to give
2.
a) Queries response
3.
b) Data access
4.
c) Authorization
5.
d) Consistency
VII. Data can be store ,
retrieve and updated in …
1.
a) SMTOP
2.
b) OLTP
3.
c) FTP
4.
d) OLAP
VIII. Which of the following is
a good alternative to the star schema?
1.
a) snow flake schema
2.
b) star schema
3.
c) star snow flake schema
4.
d) fact constellation
1.
Background knowledge is…
2.
a) It is a form of automatic
learning.
3.
b) A neural network that makes
use of a hidden layer
4.
c) The additional acquaintance
used by a learning algorithm to facilitate the learning process
5.
d) None of these
1.
Which of the following is true
for Classification?
2.
a) A subdivision of a set
3.
b) A measure of the accuracy
4.
c) The task of assigning a
classification
5.
d) All of these
Part Two:
1.
What are data mining
techniques? (5)
2.
What are the applications of
data mining? (5)
3.
Why is data mining important?
(5)
4.
Differentiate Between Data
Mining And Data Warehousing? (5)
Section B: Caselets (40 marks)
Caselet 1
User-generated content is an
indispensable part of today’s industry as every other company needs user data
to sell and buy products and provide the best possible support to its users and
clients. While user data is important, it needs to be processed to make it
relevant for the company. Data mining is the most important tool to process
such data and make it relevant and useful.
The decision tree algorithm
with the apriori algorithm can be used to support the needs of the client.
To explain this problem, we
will turn to smart technology –something that makes our lives easier. Whenever
we install any application in our smartphone, we are asked for permission for
the installation, but we do not pay too much attention to the information these
application require to be installed. In the process, we unknowingly disseminate
varied information on maps, massages, contacts, etc. With the help of this
information the application, besides collating customer data, also tries to
support the users to make their life easier and at the same time makes them
dependent on the application in the near future.
Once the user information is
gathered, the data is analysed to get the required information so as to give
the best information to the algorithm at different times. This type of analysis
starts from data pre-processing steps, steps that have already been explained
in Chapters 1 and 2. However, for this type of data pre-processing the
information gain happens by designing the decision tree at different levels-the
depth decision tree or 2-10 level decision tree as well.
Each data gives a valid point
of information and these points are used in designing the clusters among
different types of data but they are very centric in information as they
provide the information of different users according to same contents. The
frequency of the matching data is processed by means of decision tree under
info gain and Apriori.
It is a common experience
nowadays for different applications to recommend the same item for buying from
different applications or portals, Users are also able to exercise their
choices when it comes to reading the news by selecting the content that is more
liked. Through their preferences, they provide the application information
about the cognitive behavior of users. This allows prediction of the way a
particular4 consumer behaves and recommendations are accordingly tweaked. Most
studies of systems or online reviews so far have used only numeric information
about sellers or products to examine their economic impact. The understanding
that text matters has not been fully realized in electronic markets or in
online communities. Insights derived from text mining of user-generated
feedback can thus provide substantial benefits to businesses looking for
competitive advantages.
Let us summarise some of the
chief benefits utiling user-centric data:
It saves money: Since the users themselves
provide relevant content for prediction and subsequent recommendations, users
data need not be bought and efficiency in terms of time and costs in increased.·
It provides variety: By using the user data,
the customer can be apprised of various new features or upgrades to the
existing product. Further, the user gets to know about the discounts being
offered and can avail the support extended to the end user.·
It offers a voice to the user: The company is
in a position to offer individual customers different products as per
individual preferences and a user can provide any specific information of the
item he /she wants to use·
These benefits of user-centric
data should be firmly kept in mind to make such data more predictive and
relevant in our fast-paced technological era.
Questions
1.
What do you understand by user
generated content? (10)
2.
Do you really think user
generated content is effective? (10)
Caselet 2
Big data is the collection and
cross-referencing of large numbers and varieties of data sets that allows
organizations to identify patterns and categories of cardholders through a
multitude of attributes and variables. Every time customers use their cards, big
data suggests the products that can be offered to the customers. These days
many credit card users receive calls from different companies offering them new
credit cards as per their needs and expenses on the existing cards. This
information is gathered on the basis of available data provided by vendors.
There are quite a few option
available to customers to choose from. Sometimes customers even switch their
existing credit card companies. But competition may not always work in the best
interests of consumers. It also involves bank’s profit. Competition may also be
focused on particular features of credit cards that may not represent long-term
value or sustainability.
Those paying interest on
balances may be paying more than they realize or expect. Some consumers use up
their credit limits quickly or repeatedly make minimum payments without
considering how they will repay their credit card debt. A proportion of
consumers may also be over-borrowing and taking on too much debt, and there are
signs that some issuers may profit more from higher risk borrowers (by which we
mean customers at greater risk of credit default).
With the launch of this credit
card market study, we intend to build up a detailed picture of the market and
assess the potential identified issues. We plan to focus on credit card
services offered to retail consumers by credit card providers, including banks,
mono-line issuers and their affinity and co-brand partners.
While mass marketing continues
to dominate most retailers’ advertising budgets, one-to-one marketing is
growing rapidly too. In this case study, you will learn how to improve
performance by communicating directly with customers and delighting them with
relevant offers. Personalised communication is becoming a norm. Shoppers now
expect retailers to provide them with product information and promotional
offers that match their needs and desires. They count on you to know their
likes, dislikes and preferred communication method-mobile device, email or
print media.
On the surface, generating
customer-specific offers and communications seems like an unnerving task for
many retailers, but like many business problems, when broken into manageable
pieces, each process step or analytical procedure is attainable. First, let’s
assume you have assembled promotions that you intend to extend as a group of
offers (commonly called “offer bank”) to individual customers. Each offer
should have a business goal or objective, such as:
Category void for cross or up-selling of a
particular product or product group·
Basket builder to increase the customer’s
basket size·
Trip builder to create an additional trip or
visit to the store or an additional e-commerce session·
Reward to offer an incentive to loyal
customers·
Questions
1.
How Big data used in this case
study- Define? (20)
Section C: Applied Theory (30
marks)
1.
What Are Olap And Oltp? (15)
2.
What Are Different Stages Of
“data Mining”? (15)
IIBM MBA CASE LET ANSWER SHEETS – What are the applications of
data mining
Data Mining and Predictive
Analytics
1.
a) Additional acquaintance used
by a learning algorithm to facilitate the learning process
2.
b) A neural network that makes
use of a hidden layer
3.
c) It is a form of automatic
learning.
4.
d) None of these
1.
Querying of unstructured
textual data is referred to as
2.
a) Information access
3.
b) Information updation
4.
c) Information manipulation
5.
d) Information retrieval
III. A manual component to data
mining, consists of preprocessing data to a form acceptable to
1.
a) Variables
2.
b) Algorithms
3.
c) Rules
4.
d) Processes
1.
A manual component to data
mining, consists t processing data in form of
2.
a) Discovered processes
3.
b) Discovered algorithms
4.
c) Discovered features
5.
d) Discovered patterns
1.
Patterns that can be discovered
from a given database, can be of
2.
a) One type only
3.
b) No specific type
4.
c) More than one type
5.
d) Multiple type always
1.
Analysis tools precompute
summaries of very large amounts of data, in order to give
2.
a) Queries response
3.
b) Data access
4.
c) Authorization
5.
d) Consistency
VII. Data can be store ,
retrieve and updated in …
1.
a) SMTOP
2.
b) OLTP
3.
c) FTP
4.
d) OLAP
VIII. Which of the following is
a good alternative to the star schema?
1.
a) snow flake schema
2.
b) star schema
3.
c) star snow flake schema
4.
d) fact constellation
1.
Background knowledge is…
2.
a) It is a form of automatic
learning.
3.
b) A neural network that makes
use of a hidden layer
4.
c) The additional acquaintance
used by a learning algorithm to facilitate the learning process
5.
d) None of these
1.
Which of the following is true
for Classification?
2.
a) A subdivision of a set
3.
b) A measure of the accuracy
4.
c) The task of assigning a
classification
5.
d) All of these
Part Two:
1.
What are data mining
techniques? (5)
2.
What are the applications of
data mining? (5)
3.
Why is data mining important?
(5)
4.
Differentiate Between Data
Mining And Data Warehousing? (5)
Section B: Caselets (40 marks)
Caselet 1
User-generated content is an
indispensable part of today’s industry as every other company needs user data
to sell and buy products and provide the best possible support to its users and
clients. While user data is important, it needs to be processed to make it
relevant for the company. Data mining is the most important tool to process
such data and make it relevant and useful.
The decision tree algorithm
with the apriori algorithm can be used to support the needs of the client.
To explain this problem, we
will turn to smart technology –something that makes our lives easier. Whenever
we install any application in our smartphone, we are asked for permission for
the installation, but we do not pay too much attention to the information these
application require to be installed. In the process, we unknowingly disseminate
varied information on maps, massages, contacts, etc. With the help of this
information the application, besides collating customer data, also tries to
support the users to make their life easier and at the same time makes them
dependent on the application in the near future.
Once the user information is
gathered, the data is analysed to get the required information so as to give the
best information to the algorithm at different times. This type of analysis
starts from data pre-processing steps, steps that have already been explained
in Chapters 1 and 2. However, for this type of data pre-processing the
information gain happens by designing the decision tree at different levels-the
depth decision tree or 2-10 level decision tree as well.
Each data gives a valid point
of information and these points are used in designing the clusters among
different types of data but they are very centric in information as they
provide the information of different users according to same contents. The
frequency of the matching data is processed by means of decision tree under
info gain and Apriori.
It is a common experience
nowadays for different applications to recommend the same item for buying from
different applications or portals, Users are also able to exercise their
choices when it comes to reading the news by selecting the content that is more
liked. Through their preferences, they provide the application information
about the cognitive behavior of users. This allows prediction of the way a
particular4 consumer behaves and recommendations are accordingly tweaked. Most
studies of systems or online reviews so far have used only numeric information about
sellers or products to examine their economic impact. The understanding that
text matters has not been fully realized in electronic markets or in online
communities. Insights derived from text mining of user-generated feedback can
thus provide substantial benefits to businesses looking for competitive
advantages.
Let us summarise some of the
chief benefits utiling user-centric data:
It saves money: Since the users themselves
provide relevant content for prediction and subsequent recommendations, users
data need not be bought and efficiency in terms of time and costs in increased.·
It provides variety: By using the user data,
the customer can be apprised of various new features or upgrades to the
existing product. Further, the user gets to know about the discounts being
offered and can avail the support extended to the end user.·
It offers a voice to the user: The company is
in a position to offer individual customers different products as per
individual preferences and a user can provide any specific information of the
item he /she wants to use·
These benefits of user-centric
data should be firmly kept in mind to make such data more predictive and
relevant in our fast-paced technological era.
Questions
1.
What do you understand by user
generated content? (10)
2.
Do you really think user
generated content is effective? (10)
Caselet 2
Big data is the collection and
cross-referencing of large numbers and varieties of data sets that allows
organizations to identify patterns and categories of cardholders through a
multitude of attributes and variables. Every time customers use their cards,
big data suggests the products that can be offered to the customers. These days
many credit card users receive calls from different companies offering them new
credit cards as per their needs and expenses on the existing cards. This
information is gathered on the basis of available data provided by vendors.
There are quite a few option
available to customers to choose from. Sometimes customers even switch their
existing credit card companies. But competition may not always work in the best
interests of consumers. It also involves bank’s profit. Competition may also be
focused on particular features of credit cards that may not represent long-term
value or sustainability.
Those paying interest on
balances may be paying more than they realize or expect. Some consumers use up
their credit limits quickly or repeatedly make minimum payments without
considering how they will repay their credit card debt. A proportion of
consumers may also be over-borrowing and taking on too much debt, and there are
signs that some issuers may profit more from higher risk borrowers (by which we
mean customers at greater risk of credit default).
With the launch of this credit
card market study, we intend to build up a detailed picture of the market and
assess the potential identified issues. We plan to focus on credit card
services offered to retail consumers by credit card providers, including banks,
mono-line issuers and their affinity and co-brand partners.
While mass marketing continues
to dominate most retailers’ advertising budgets, one-to-one marketing is
growing rapidly too. In this case study, you will learn how to improve
performance by communicating directly with customers and delighting them with
relevant offers. Personalised communication is becoming a norm. Shoppers now expect
retailers to provide them with product information and promotional offers that
match their needs and desires. They count on you to know their likes, dislikes
and preferred communication method-mobile device, email or print media.
On the surface, generating
customer-specific offers and communications seems like an unnerving task for
many retailers, but like many business problems, when broken into manageable
pieces, each process step or analytical procedure is attainable. First, let’s
assume you have assembled promotions that you intend to extend as a group of
offers (commonly called “offer bank”) to individual customers. Each offer
should have a business goal or objective, such as:
Category void for cross or up-selling of a
particular product or product group·
Basket builder to increase the customer’s
basket size·
Trip builder to create an additional trip or
visit to the store or an additional e-commerce session·
Reward to offer an incentive to loyal
customers·
Questions
1.
How Big data used in this case
study- Define? (20)
Section C: Applied Theory (30
marks)
1.
What Are Olap And Oltp? (15)
2.
What Are Different Stages Of
“data Mining”? (15)
MIB IIBM CASE STUDY ANSWER SHEETS – What are data mining
techniques
Data Mining and
Predictive Analytics
1.
a) Additional acquaintance used
by a learning algorithm to facilitate the learning process
2.
b) A neural network that makes
use of a hidden layer
3.
c) It is a form of automatic
learning.
4.
d) None of these
1.
Querying of unstructured
textual data is referred to as
2.
a) Information access
3.
b) Information updation
4.
c) Information manipulation
5.
d) Information retrieval
III. A manual component to data
mining, consists of preprocessing data to a form acceptable to
1.
a) Variables
2.
b) Algorithms
3.
c) Rules
4.
d) Processes
1.
A manual component to data
mining, consists t processing data in form of
2.
a) Discovered processes
3.
b) Discovered algorithms
4.
c) Discovered features
5.
d) Discovered patterns
1.
Patterns that can be discovered
from a given database, can be of
2.
a) One type only
3.
b) No specific type
4.
c) More than one type
5.
d) Multiple type always
1.
Analysis tools precompute
summaries of very large amounts of data, in order to give
2.
a) Queries response
3.
b) Data access
4.
c) Authorization
5.
d) Consistency
VII. Data can be store ,
retrieve and updated in …
1.
a) SMTOP
2.
b) OLTP
3.
c) FTP
4.
d) OLAP
VIII. Which of the following is
a good alternative to the star schema?
1.
a) snow flake schema
2.
b) star schema
3.
c) star snow flake schema
4.
d) fact constellation
1.
Background knowledge is…
2.
a) It is a form of automatic
learning.
3.
b) A neural network that makes
use of a hidden layer
4.
c) The additional acquaintance
used by a learning algorithm to facilitate the learning process
5.
d) None of these
1.
Which of the following is true
for Classification?
2.
a) A subdivision of a set
3.
b) A measure of the accuracy
4.
c) The task of assigning a
classification
5.
d) All of these
Part Two:
1.
What are data mining
techniques? (5)
2.
What are the applications of
data mining? (5)
3.
Why is data mining important?
(5)
4.
Differentiate Between Data
Mining And Data Warehousing? (5)
Section B: Caselets (40 marks)
Caselet 1
User-generated content is an
indispensable part of today’s industry as every other company needs user data
to sell and buy products and provide the best possible support to its users and
clients. While user data is important, it needs to be processed to make it
relevant for the company. Data mining is the most important tool to process
such data and make it relevant and useful.
The decision tree algorithm
with the apriori algorithm can be used to support the needs of the client.
To explain this problem, we
will turn to smart technology –something that makes our lives easier. Whenever
we install any application in our smartphone, we are asked for permission for
the installation, but we do not pay too much attention to the information these
application require to be installed. In the process, we unknowingly disseminate
varied information on maps, massages, contacts, etc. With the help of this
information the application, besides collating customer data, also tries to
support the users to make their life easier and at the same time makes them
dependent on the application in the near future.
Once the user information is
gathered, the data is analysed to get the required information so as to give the
best information to the algorithm at different times. This type of analysis
starts from data pre-processing steps, steps that have already been explained
in Chapters 1 and 2. However, for this type of data pre-processing the
information gain happens by designing the decision tree at different levels-the
depth decision tree or 2-10 level decision tree as well.
Each data gives a valid point
of information and these points are used in designing the clusters among
different types of data but they are very centric in information as they
provide the information of different users according to same contents. The
frequency of the matching data is processed by means of decision tree under
info gain and Apriori.
It is a common experience
nowadays for different applications to recommend the same item for buying from
different applications or portals, Users are also able to exercise their
choices when it comes to reading the news by selecting the content that is more
liked. Through their preferences, they provide the application information
about the cognitive behavior of users. This allows prediction of the way a
particular4 consumer behaves and recommendations are accordingly tweaked. Most
studies of systems or online reviews so far have used only numeric information
about sellers or products to examine their economic impact. The understanding
that text matters has not been fully realized in electronic markets or in
online communities. Insights derived from text mining of user-generated
feedback can thus provide substantial benefits to businesses looking for
competitive advantages.
Let us summarise some of the
chief benefits utiling user-centric data:
It saves money: Since the users themselves
provide relevant content for prediction and subsequent recommendations, users
data need not be bought and efficiency in terms of time and costs in increased.·
It provides variety: By using the user data,
the customer can be apprised of various new features or upgrades to the
existing product. Further, the user gets to know about the discounts being
offered and can avail the support extended to the end user.·
It offers a voice to the user: The company is
in a position to offer individual customers different products as per
individual preferences and a user can provide any specific information of the
item he /she wants to use·
These benefits of user-centric
data should be firmly kept in mind to make such data more predictive and
relevant in our fast-paced technological era.
Questions
1.
What do you understand by user
generated content? (10)
2.
Do you really think user
generated content is effective? (10)
Caselet 2
Big data is the collection and
cross-referencing of large numbers and varieties of data sets that allows
organizations to identify patterns and categories of cardholders through a
multitude of attributes and variables. Every time customers use their cards,
big data suggests the products that can be offered to the customers. These days
many credit card users receive calls from different companies offering them new
credit cards as per their needs and expenses on the existing cards. This
information is gathered on the basis of available data provided by vendors.
There are quite a few option
available to customers to choose from. Sometimes customers even switch their
existing credit card companies. But competition may not always work in the best
interests of consumers. It also involves bank’s profit. Competition may also be
focused on particular features of credit cards that may not represent long-term
value or sustainability.
Those paying interest on
balances may be paying more than they realize or expect. Some consumers use up
their credit limits quickly or repeatedly make minimum payments without
considering how they will repay their credit card debt. A proportion of
consumers may also be over-borrowing and taking on too much debt, and there are
signs that some issuers may profit more from higher risk borrowers (by which we
mean customers at greater risk of credit default).
With the launch of this credit
card market study, we intend to build up a detailed picture of the market and
assess the potential identified issues. We plan to focus on credit card
services offered to retail consumers by credit card providers, including banks,
mono-line issuers and their affinity and co-brand partners.
While mass marketing continues
to dominate most retailers’ advertising budgets, one-to-one marketing is
growing rapidly too. In this case study, you will learn how to improve
performance by communicating directly with customers and delighting them with
relevant offers. Personalised communication is becoming a norm. Shoppers now
expect retailers to provide them with product information and promotional
offers that match their needs and desires. They count on you to know their
likes, dislikes and preferred communication method-mobile device, email or
print media.
On the surface, generating
customer-specific offers and communications seems like an unnerving task for
many retailers, but like many business problems, when broken into manageable
pieces, each process step or analytical procedure is attainable. First, let’s
assume you have assembled promotions that you intend to extend as a group of
offers (commonly called “offer bank”) to individual customers. Each offer
should have a business goal or objective, such as:
Category void for cross or up-selling of a
particular product or product group·
Basket builder to increase the customer’s
basket size·
Trip builder to create an additional trip or
visit to the store or an additional e-commerce session·
Reward to offer an incentive to loyal
customers·
Questions
1.
How Big data used in this case
study- Define? (20)
Section C: Applied Theory (30
marks)
1.
What Are Olap And Oltp? (15)
2.
What Are Different Stages Of
“data Mining”? (15)
IIBM CASE STUDY ANSWER SHEETS – Which of the following is true for
Classification? a) A subdivision of a set b) A measure of the accuracy c) The
task of assigning a classification d) All of these
Data Mining and
Predictive Analytics
1.
a) Additional acquaintance used
by a learning algorithm to facilitate the learning process
2.
b) A neural network that makes
use of a hidden layer
3.
c) It is a form of automatic
learning.
4.
d) None of these
1.
Querying of unstructured
textual data is referred to as
2.
a) Information access
3.
b) Information updation
4.
c) Information manipulation
5.
d) Information retrieval
III. A manual component to data
mining, consists of preprocessing data to a form acceptable to
1.
a) Variables
2.
b) Algorithms
3.
c) Rules
4.
d) Processes
1.
A manual component to data
mining, consists t processing data in form of
2.
a) Discovered processes
3.
b) Discovered algorithms
4.
c) Discovered features
5.
d) Discovered patterns
1.
Patterns that can be discovered
from a given database, can be of
2.
a) One type only
3.
b) No specific type
4.
c) More than one type
5.
d) Multiple type always
1.
Analysis tools precompute
summaries of very large amounts of data, in order to give
2.
a) Queries response
3.
b) Data access
4.
c) Authorization
5.
d) Consistency
VII. Data can be store ,
retrieve and updated in …
1.
a) SMTOP
2.
b) OLTP
3.
c) FTP
4.
d) OLAP
VIII. Which of the following is
a good alternative to the star schema?
1.
a) snow flake schema
2.
b) star schema
3.
c) star snow flake schema
4.
d) fact constellation
1.
Background knowledge is…
2.
a) It is a form of automatic
learning.
3.
b) A neural network that makes
use of a hidden layer
4.
c) The additional acquaintance
used by a learning algorithm to facilitate the learning process
5.
d) None of these
1.
Which of the following is true
for Classification?
2.
a) A subdivision of a set
3.
b) A measure of the accuracy
4.
c) The task of assigning a
classification
5.
d) All of these
Part Two:
1.
What are data mining
techniques? (5)
2.
What are the applications of
data mining? (5)
3.
Why is data mining important?
(5)
4.
Differentiate Between Data
Mining And Data Warehousing? (5)
Section B: Caselets (40 marks)
Caselet 1
User-generated content is an
indispensable part of today’s industry as every other company needs user data
to sell and buy products and provide the best possible support to its users and
clients. While user data is important, it needs to be processed to make it
relevant for the company. Data mining is the most important tool to process
such data and make it relevant and useful.
The decision tree algorithm
with the apriori algorithm can be used to support the needs of the client.
To explain this problem, we will
turn to smart technology –something that makes our lives easier. Whenever we
install any application in our smartphone, we are asked for permission for the
installation, but we do not pay too much attention to the information these
application require to be installed. In the process, we unknowingly disseminate
varied information on maps, massages, contacts, etc. With the help of this
information the application, besides collating customer data, also tries to
support the users to make their life easier and at the same time makes them
dependent on the application in the near future.
Once the user information is
gathered, the data is analysed to get the required information so as to give
the best information to the algorithm at different times. This type of analysis
starts from data pre-processing steps, steps that have already been explained
in Chapters 1 and 2. However, for this type of data pre-processing the
information gain happens by designing the decision tree at different levels-the
depth decision tree or 2-10 level decision tree as well.
Each data gives a valid point
of information and these points are used in designing the clusters among
different types of data but they are very centric in information as they
provide the information of different users according to same contents. The
frequency of the matching data is processed by means of decision tree under
info gain and Apriori.
It is a common experience
nowadays for different applications to recommend the same item for buying from
different applications or portals, Users are also able to exercise their
choices when it comes to reading the news by selecting the content that is more
liked. Through their preferences, they provide the application information
about the cognitive behavior of users. This allows prediction of the way a
particular4 consumer behaves and recommendations are accordingly tweaked. Most
studies of systems or online reviews so far have used only numeric information
about sellers or products to examine their economic impact. The understanding
that text matters has not been fully realized in electronic markets or in
online communities. Insights derived from text mining of user-generated
feedback can thus provide substantial benefits to businesses looking for
competitive advantages.
Let us summarise some of the
chief benefits utiling user-centric data:
It saves money: Since the users themselves
provide relevant content for prediction and subsequent recommendations, users
data need not be bought and efficiency in terms of time and costs in increased.·
It provides variety: By using the user data,
the customer can be apprised of various new features or upgrades to the
existing product. Further, the user gets to know about the discounts being
offered and can avail the support extended to the end user.·
It offers a voice to the user: The company is
in a position to offer individual customers different products as per
individual preferences and a user can provide any specific information of the
item he /she wants to use·
These benefits of user-centric
data should be firmly kept in mind to make such data more predictive and
relevant in our fast-paced technological era.
Questions
1.
What do you understand by user
generated content? (10)
2.
Do you really think user
generated content is effective? (10)
Caselet 2
Big data is the collection and
cross-referencing of large numbers and varieties of data sets that allows
organizations to identify patterns and categories of cardholders through a
multitude of attributes and variables. Every time customers use their cards, big
data suggests the products that can be offered to the customers. These days
many credit card users receive calls from different companies offering them new
credit cards as per their needs and expenses on the existing cards. This
information is gathered on the basis of available data provided by vendors.
There are quite a few option
available to customers to choose from. Sometimes customers even switch their
existing credit card companies. But competition may not always work in the best
interests of consumers. It also involves bank’s profit. Competition may also be
focused on particular features of credit cards that may not represent long-term
value or sustainability.
Those paying interest on
balances may be paying more than they realize or expect. Some consumers use up
their credit limits quickly or repeatedly make minimum payments without
considering how they will repay their credit card debt. A proportion of
consumers may also be over-borrowing and taking on too much debt, and there are
signs that some issuers may profit more from higher risk borrowers (by which we
mean customers at greater risk of credit default).
With the launch of this credit
card market study, we intend to build up a detailed picture of the market and
assess the potential identified issues. We plan to focus on credit card
services offered to retail consumers by credit card providers, including banks,
mono-line issuers and their affinity and co-brand partners.
While mass marketing continues
to dominate most retailers’ advertising budgets, one-to-one marketing is
growing rapidly too. In this case study, you will learn how to improve
performance by communicating directly with customers and delighting them with
relevant offers. Personalised communication is becoming a norm. Shoppers now
expect retailers to provide them with product information and promotional
offers that match their needs and desires. They count on you to know their
likes, dislikes and preferred communication method-mobile device, email or
print media.
On the surface, generating
customer-specific offers and communications seems like an unnerving task for
many retailers, but like many business problems, when broken into manageable
pieces, each process step or analytical procedure is attainable. First, let’s
assume you have assembled promotions that you intend to extend as a group of
offers (commonly called “offer bank”) to individual customers. Each offer
should have a business goal or objective, such as:
Category void for cross or up-selling of a
particular product or product group·
Basket builder to increase the customer’s
basket size·
Trip builder to create an additional trip or
visit to the store or an additional e-commerce session·
Reward to offer an incentive to loyal
customers·
Questions
1.
How Big data used in this case
study- Define? (20)
Section C: Applied Theory (30
marks)
1.
What Are Olap And Oltp? (15)
2.
What Are Different Stages Of
“data Mining”? (15)
IIBM CASE STUDY ANSWER SHEETS – Which of the following is a good
alternative to the star schema? a) snow flake schema b) star schema c) star
snow flake schema d) fact constellation
Data Mining and
Predictive Analytics
1.
a) Additional acquaintance used
by a learning algorithm to facilitate the learning process
2.
b) A neural network that makes
use of a hidden layer
3.
c) It is a form of automatic
learning.
4.
d) None of these
1.
Querying of unstructured
textual data is referred to as
2.
a) Information access
3.
b) Information updation
4.
c) Information manipulation
5.
d) Information retrieval
III. A manual component to data
mining, consists of preprocessing data to a form acceptable to
1.
a) Variables
2.
b) Algorithms
3.
c) Rules
4.
d) Processes
1.
A manual component to data
mining, consists t processing data in form of
2.
a) Discovered processes
3.
b) Discovered algorithms
4.
c) Discovered features
5.
d) Discovered patterns
1.
Patterns that can be discovered
from a given database, can be of
2.
a) One type only
3.
b) No specific type
4.
c) More than one type
5.
d) Multiple type always
1.
Analysis tools precompute
summaries of very large amounts of data, in order to give
2.
a) Queries response
3.
b) Data access
4.
c) Authorization
5.
d) Consistency
VII. Data can be store ,
retrieve and updated in …
1.
a) SMTOP
2.
b) OLTP
3.
c) FTP
4.
d) OLAP
VIII. Which of the following is
a good alternative to the star schema?
1.
a) snow flake schema
2.
b) star schema
3.
c) star snow flake schema
4.
d) fact constellation
1.
Background knowledge is…
2.
a) It is a form of automatic
learning.
3.
b) A neural network that makes
use of a hidden layer
4.
c) The additional acquaintance
used by a learning algorithm to facilitate the learning process
5.
d) None of these
1.
Which of the following is true
for Classification?
2.
a) A subdivision of a set
3.
b) A measure of the accuracy
4.
c) The task of assigning a
classification
5.
d) All of these
Part Two:
1.
What are data mining
techniques? (5)
2.
What are the applications of
data mining? (5)
3.
Why is data mining important?
(5)
4.
Differentiate Between Data
Mining And Data Warehousing? (5)
Section B: Caselets (40 marks)
Caselet 1
User-generated content is an
indispensable part of today’s industry as every other company needs user data
to sell and buy products and provide the best possible support to its users and
clients. While user data is important, it needs to be processed to make it
relevant for the company. Data mining is the most important tool to process
such data and make it relevant and useful.
The decision tree algorithm
with the apriori algorithm can be used to support the needs of the client.
To explain this problem, we
will turn to smart technology –something that makes our lives easier. Whenever
we install any application in our smartphone, we are asked for permission for
the installation, but we do not pay too much attention to the information these
application require to be installed. In the process, we unknowingly disseminate
varied information on maps, massages, contacts, etc. With the help of this
information the application, besides collating customer data, also tries to
support the users to make their life easier and at the same time makes them
dependent on the application in the near future.
Once the user information is
gathered, the data is analysed to get the required information so as to give
the best information to the algorithm at different times. This type of analysis
starts from data pre-processing steps, steps that have already been explained
in Chapters 1 and 2. However, for this type of data pre-processing the
information gain happens by designing the decision tree at different levels-the
depth decision tree or 2-10 level decision tree as well.
Each data gives a valid point
of information and these points are used in designing the clusters among
different types of data but they are very centric in information as they
provide the information of different users according to same contents. The
frequency of the matching data is processed by means of decision tree under
info gain and Apriori.
It is a common experience
nowadays for different applications to recommend the same item for buying from
different applications or portals, Users are also able to exercise their
choices when it comes to reading the news by selecting the content that is more
liked. Through their preferences, they provide the application information
about the cognitive behavior of users. This allows prediction of the way a
particular4 consumer behaves and recommendations are accordingly tweaked. Most
studies of systems or online reviews so far have used only numeric information
about sellers or products to examine their economic impact. The understanding
that text matters has not been fully realized in electronic markets or in
online communities. Insights derived from text mining of user-generated
feedback can thus provide substantial benefits to businesses looking for
competitive advantages.
Let us summarise some of the
chief benefits utiling user-centric data:
It saves money: Since the users themselves
provide relevant content for prediction and subsequent recommendations, users
data need not be bought and efficiency in terms of time and costs in increased.·
It provides variety: By using the user data,
the customer can be apprised of various new features or upgrades to the
existing product. Further, the user gets to know about the discounts being
offered and can avail the support extended to the end user.·
It offers a voice to the user: The company is
in a position to offer individual customers different products as per
individual preferences and a user can provide any specific information of the
item he /she wants to use·
These benefits of user-centric
data should be firmly kept in mind to make such data more predictive and
relevant in our fast-paced technological era.
Questions
1.
What do you understand by user
generated content? (10)
2.
Do you really think user
generated content is effective? (10)
Caselet 2
Big data is the collection and
cross-referencing of large numbers and varieties of data sets that allows
organizations to identify patterns and categories of cardholders through a
multitude of attributes and variables. Every time customers use their cards,
big data suggests the products that can be offered to the customers. These days
many credit card users receive calls from different companies offering them new
credit cards as per their needs and expenses on the existing cards. This
information is gathered on the basis of available data provided by vendors.
There are quite a few option
available to customers to choose from. Sometimes customers even switch their
existing credit card companies. But competition may not always work in the best
interests of consumers. It also involves bank’s profit. Competition may also be
focused on particular features of credit cards that may not represent long-term
value or sustainability.
Those paying interest on
balances may be paying more than they realize or expect. Some consumers use up
their credit limits quickly or repeatedly make minimum payments without
considering how they will repay their credit card debt. A proportion of
consumers may also be over-borrowing and taking on too much debt, and there are
signs that some issuers may profit more from higher risk borrowers (by which we
mean customers at greater risk of credit default).
With the launch of this credit
card market study, we intend to build up a detailed picture of the market and
assess the potential identified issues. We plan to focus on credit card
services offered to retail consumers by credit card providers, including banks,
mono-line issuers and their affinity and co-brand partners.
While mass marketing continues
to dominate most retailers’ advertising budgets, one-to-one marketing is
growing rapidly too. In this case study, you will learn how to improve
performance by communicating directly with customers and delighting them with
relevant offers. Personalised communication is becoming a norm. Shoppers now expect
retailers to provide them with product information and promotional offers that
match their needs and desires. They count on you to know their likes, dislikes
and preferred communication method-mobile device, email or print media.
On the surface, generating
customer-specific offers and communications seems like an unnerving task for
many retailers, but like many business problems, when broken into manageable
pieces, each process step or analytical procedure is attainable. First, let’s
assume you have assembled promotions that you intend to extend as a group of
offers (commonly called “offer bank”) to individual customers. Each offer
should have a business goal or objective, such as:
Category void for cross or up-selling of a
particular product or product group·
Basket builder to increase the customer’s
basket size·
Trip builder to create an additional trip or
visit to the store or an additional e-commerce session·
Reward to offer an incentive to loyal
customers·
Questions
1.
How Big data used in this case
study- Define? (20)
Section C: Applied Theory (30
marks)
1.
What Are Olap And Oltp? (15)
2.
What Are Different Stages Of
“data Mining”? (15)
IIBM DMS CASE STUDY SOLUTIONS PAPERS – Data can be store ,
retrieve and updated in … a) SMTOP b) OLTP c) FTP d) OLAP
Data Mining and
Predictive Analytics
1.
a) Additional acquaintance used
by a learning algorithm to facilitate the learning process
2.
b) A neural network that makes
use of a hidden layer
3.
c) It is a form of automatic
learning.
4.
d) None of these
1.
Querying of unstructured
textual data is referred to as
2.
a) Information access
3.
b) Information updation
4.
c) Information manipulation
5.
d) Information retrieval
III. A manual component to data
mining, consists of preprocessing data to a form acceptable to
1.
a) Variables
2.
b) Algorithms
3.
c) Rules
4.
d) Processes
1.
A manual component to data
mining, consists t processing data in form of
2.
a) Discovered processes
3.
b) Discovered algorithms
4.
c) Discovered features
5.
d) Discovered patterns
1.
Patterns that can be discovered
from a given database, can be of
2.
a) One type only
3.
b) No specific type
4.
c) More than one type
5.
d) Multiple type always
1.
Analysis tools precompute
summaries of very large amounts of data, in order to give
2.
a) Queries response
3.
b) Data access
4.
c) Authorization
5.
d) Consistency
VII. Data can be store ,
retrieve and updated in …
1.
a) SMTOP
2.
b) OLTP
3.
c) FTP
4.
d) OLAP
VIII. Which of the following is
a good alternative to the star schema?
1.
a) snow flake schema
2.
b) star schema
3.
c) star snow flake schema
4.
d) fact constellation
1.
Background knowledge is…
2.
a) It is a form of automatic
learning.
3.
b) A neural network that makes
use of a hidden layer
4.
c) The additional acquaintance
used by a learning algorithm to facilitate the learning process
5.
d) None of these
1.
Which of the following is true
for Classification?
2.
a) A subdivision of a set
3.
b) A measure of the accuracy
4.
c) The task of assigning a
classification
5.
d) All of these
Part Two:
1.
What are data mining
techniques? (5)
2.
What are the applications of
data mining? (5)
3.
Why is data mining important?
(5)
4.
Differentiate Between Data
Mining And Data Warehousing? (5)
Section B: Caselets (40 marks)
Caselet 1
User-generated content is an
indispensable part of today’s industry as every other company needs user data
to sell and buy products and provide the best possible support to its users and
clients. While user data is important, it needs to be processed to make it
relevant for the company. Data mining is the most important tool to process
such data and make it relevant and useful.
The decision tree algorithm
with the apriori algorithm can be used to support the needs of the client.
To explain this problem, we
will turn to smart technology –something that makes our lives easier. Whenever
we install any application in our smartphone, we are asked for permission for
the installation, but we do not pay too much attention to the information these
application require to be installed. In the process, we unknowingly disseminate
varied information on maps, massages, contacts, etc. With the help of this
information the application, besides collating customer data, also tries to
support the users to make their life easier and at the same time makes them
dependent on the application in the near future.
Once the user information is
gathered, the data is analysed to get the required information so as to give
the best information to the algorithm at different times. This type of analysis
starts from data pre-processing steps, steps that have already been explained
in Chapters 1 and 2. However, for this type of data pre-processing the
information gain happens by designing the decision tree at different levels-the
depth decision tree or 2-10 level decision tree as well.
Each data gives a valid point
of information and these points are used in designing the clusters among
different types of data but they are very centric in information as they
provide the information of different users according to same contents. The
frequency of the matching data is processed by means of decision tree under
info gain and Apriori.
It is a common experience
nowadays for different applications to recommend the same item for buying from
different applications or portals, Users are also able to exercise their
choices when it comes to reading the news by selecting the content that is more
liked. Through their preferences, they provide the application information
about the cognitive behavior of users. This allows prediction of the way a
particular4 consumer behaves and recommendations are accordingly tweaked. Most
studies of systems or online reviews so far have used only numeric information
about sellers or products to examine their economic impact. The understanding
that text matters has not been fully realized in electronic markets or in
online communities. Insights derived from text mining of user-generated
feedback can thus provide substantial benefits to businesses looking for
competitive advantages.
Let us summarise some of the
chief benefits utiling user-centric data:
It saves money: Since the users themselves
provide relevant content for prediction and subsequent recommendations, users
data need not be bought and efficiency in terms of time and costs in increased.·
It provides variety: By using the user data,
the customer can be apprised of various new features or upgrades to the
existing product. Further, the user gets to know about the discounts being
offered and can avail the support extended to the end user.·
It offers a voice to the user: The company is
in a position to offer individual customers different products as per
individual preferences and a user can provide any specific information of the
item he /she wants to use·
These benefits of user-centric
data should be firmly kept in mind to make such data more predictive and
relevant in our fast-paced technological era.
Questions
1.
What do you understand by user
generated content? (10)
2.
Do you really think user
generated content is effective? (10)
Caselet 2
Big data is the collection and
cross-referencing of large numbers and varieties of data sets that allows organizations
to identify patterns and categories of cardholders through a multitude of
attributes and variables. Every time customers use their cards, big data
suggests the products that can be offered to the customers. These days many
credit card users receive calls from different companies offering them new
credit cards as per their needs and expenses on the existing cards. This
information is gathered on the basis of available data provided by vendors.
There are quite a few option
available to customers to choose from. Sometimes customers even switch their
existing credit card companies. But competition may not always work in the best
interests of consumers. It also involves bank’s profit. Competition may also be
focused on particular features of credit cards that may not represent long-term
value or sustainability.
Those paying interest on
balances may be paying more than they realize or expect. Some consumers use up
their credit limits quickly or repeatedly make minimum payments without
considering how they will repay their credit card debt. A proportion of
consumers may also be over-borrowing and taking on too much debt, and there are
signs that some issuers may profit more from higher risk borrowers (by which we
mean customers at greater risk of credit default).
With the launch of this credit
card market study, we intend to build up a detailed picture of the market and
assess the potential identified issues. We plan to focus on credit card
services offered to retail consumers by credit card providers, including banks,
mono-line issuers and their affinity and co-brand partners.
While mass marketing continues
to dominate most retailers’ advertising budgets, one-to-one marketing is
growing rapidly too. In this case study, you will learn how to improve performance
by communicating directly with customers and delighting them with relevant
offers. Personalised communication is becoming a norm. Shoppers now expect
retailers to provide them with product information and promotional offers that
match their needs and desires. They count on you to know their likes, dislikes
and preferred communication method-mobile device, email or print media.
On the surface, generating
customer-specific offers and communications seems like an unnerving task for
many retailers, but like many business problems, when broken into manageable
pieces, each process step or analytical procedure is attainable. First, let’s
assume you have assembled promotions that you intend to extend as a group of
offers (commonly called “offer bank”) to individual customers. Each offer
should have a business goal or objective, such as:
Category void for cross or up-selling of a
particular product or product group·
Basket builder to increase the customer’s
basket size·
Trip builder to create an additional trip or
visit to the store or an additional e-commerce session·
Reward to offer an incentive to loyal
customers·
Questions
1.
How Big data used in this case
study- Define? (20)
Section C: Applied Theory (30
marks)
1.
What Are Olap And Oltp? (15)
2.
What Are Different Stages Of
“data Mining”? (15)
DMS IIBM ANSWER SHEETS – Analysis tools precompute summaries of
very large amounts of data, in order to give a) Queries response b) Data access
c) Authorization d) Consistency
Data Mining and
Predictive Analytics
1.
a) Additional acquaintance used
by a learning algorithm to facilitate the learning process
2.
b) A neural network that makes
use of a hidden layer
3.
c) It is a form of automatic
learning.
4.
d) None of these
1.
Querying of unstructured
textual data is referred to as
2.
a) Information access
3.
b) Information updation
4.
c) Information manipulation
5.
d) Information retrieval
III. A manual component to data
mining, consists of preprocessing data to a form acceptable to
1.
a) Variables
2.
b) Algorithms
3.
c) Rules
4.
d) Processes
1.
A manual component to data
mining, consists t processing data in form of
2.
a) Discovered processes
3.
b) Discovered algorithms
4.
c) Discovered features
5.
d) Discovered patterns
1.
Patterns that can be discovered
from a given database, can be of
2.
a) One type only
3.
b) No specific type
4.
c) More than one type
5.
d) Multiple type always
1.
Analysis tools precompute
summaries of very large amounts of data, in order to give
2.
a) Queries response
3.
b) Data access
4.
c) Authorization
5.
d) Consistency
VII. Data can be store ,
retrieve and updated in …
1.
a) SMTOP
2.
b) OLTP
3.
c) FTP
4.
d) OLAP
VIII. Which of the following is
a good alternative to the star schema?
1.
a) snow flake schema
2.
b) star schema
3.
c) star snow flake schema
4.
d) fact constellation
1.
Background knowledge is…
2.
a) It is a form of automatic
learning.
3.
b) A neural network that makes
use of a hidden layer
4.
c) The additional acquaintance
used by a learning algorithm to facilitate the learning process
5.
d) None of these
1.
Which of the following is true
for Classification?
2.
a) A subdivision of a set
3.
b) A measure of the accuracy
4.
c) The task of assigning a
classification
5.
d) All of these
Part Two:
1.
What are data mining
techniques? (5)
2.
What are the applications of
data mining? (5)
3.
Why is data mining important?
(5)
4.
Differentiate Between Data
Mining And Data Warehousing? (5)
Section B: Caselets (40 marks)
Caselet 1
User-generated content is an
indispensable part of today’s industry as every other company needs user data
to sell and buy products and provide the best possible support to its users and
clients. While user data is important, it needs to be processed to make it
relevant for the company. Data mining is the most important tool to process
such data and make it relevant and useful.
The decision tree algorithm
with the apriori algorithm can be used to support the needs of the client.
To explain this problem, we
will turn to smart technology –something that makes our lives easier. Whenever
we install any application in our smartphone, we are asked for permission for
the installation, but we do not pay too much attention to the information these
application require to be installed. In the process, we unknowingly disseminate
varied information on maps, massages, contacts, etc. With the help of this
information the application, besides collating customer data, also tries to
support the users to make their life easier and at the same time makes them
dependent on the application in the near future.
Once the user information is
gathered, the data is analysed to get the required information so as to give
the best information to the algorithm at different times. This type of analysis
starts from data pre-processing steps, steps that have already been explained
in Chapters 1 and 2. However, for this type of data pre-processing the
information gain happens by designing the decision tree at different levels-the
depth decision tree or 2-10 level decision tree as well.
Each data gives a valid point
of information and these points are used in designing the clusters among
different types of data but they are very centric in information as they
provide the information of different users according to same contents. The
frequency of the matching data is processed by means of decision tree under
info gain and Apriori.
It is a common experience
nowadays for different applications to recommend the same item for buying from
different applications or portals, Users are also able to exercise their
choices when it comes to reading the news by selecting the content that is more
liked. Through their preferences, they provide the application information
about the cognitive behavior of users. This allows prediction of the way a
particular4 consumer behaves and recommendations are accordingly tweaked. Most
studies of systems or online reviews so far have used only numeric information
about sellers or products to examine their economic impact. The understanding
that text matters has not been fully realized in electronic markets or in
online communities. Insights derived from text mining of user-generated
feedback can thus provide substantial benefits to businesses looking for
competitive advantages.
Let us summarise some of the
chief benefits utiling user-centric data:
It saves money: Since the users themselves
provide relevant content for prediction and subsequent recommendations, users
data need not be bought and efficiency in terms of time and costs in increased.·
It provides variety: By using the user data,
the customer can be apprised of various new features or upgrades to the
existing product. Further, the user gets to know about the discounts being
offered and can avail the support extended to the end user.·
It offers a voice to the user: The company is
in a position to offer individual customers different products as per
individual preferences and a user can provide any specific information of the
item he /she wants to use·
These benefits of user-centric
data should be firmly kept in mind to make such data more predictive and
relevant in our fast-paced technological era.
Questions
1.
What do you understand by user
generated content? (10)
2.
Do you really think user
generated content is effective? (10)
Caselet 2
Big data is the collection and
cross-referencing of large numbers and varieties of data sets that allows
organizations to identify patterns and categories of cardholders through a
multitude of attributes and variables. Every time customers use their cards, big
data suggests the products that can be offered to the customers. These days
many credit card users receive calls from different companies offering them new
credit cards as per their needs and expenses on the existing cards. This
information is gathered on the basis of available data provided by vendors.
There are quite a few option
available to customers to choose from. Sometimes customers even switch their
existing credit card companies. But competition may not always work in the best
interests of consumers. It also involves bank’s profit. Competition may also be
focused on particular features of credit cards that may not represent long-term
value or sustainability.
Those paying interest on
balances may be paying more than they realize or expect. Some consumers use up
their credit limits quickly or repeatedly make minimum payments without
considering how they will repay their credit card debt. A proportion of
consumers may also be over-borrowing and taking on too much debt, and there are
signs that some issuers may profit more from higher risk borrowers (by which we
mean customers at greater risk of credit default).
With the launch of this credit
card market study, we intend to build up a detailed picture of the market and
assess the potential identified issues. We plan to focus on credit card
services offered to retail consumers by credit card providers, including banks,
mono-line issuers and their affinity and co-brand partners.
While mass marketing continues
to dominate most retailers’ advertising budgets, one-to-one marketing is
growing rapidly too. In this case study, you will learn how to improve
performance by communicating directly with customers and delighting them with
relevant offers. Personalised communication is becoming a norm. Shoppers now
expect retailers to provide them with product information and promotional
offers that match their needs and desires. They count on you to know their
likes, dislikes and preferred communication method-mobile device, email or
print media.
On the surface, generating
customer-specific offers and communications seems like an unnerving task for
many retailers, but like many business problems, when broken into manageable
pieces, each process step or analytical procedure is attainable. First, let’s
assume you have assembled promotions that you intend to extend as a group of
offers (commonly called “offer bank”) to individual customers. Each offer
should have a business goal or objective, such as:
Category void for cross or up-selling of a
particular product or product group·
Basket builder to increase the customer’s
basket size·
Trip builder to create an additional trip or
visit to the store or an additional e-commerce session·
Reward to offer an incentive to loyal
customers·
Questions
1.
How Big data used in this case
study- Define? (20)
Section C: Applied Theory (30
marks)
1.
What Are Olap And Oltp? (15)
2.
What Are Different Stages Of
“data Mining”? (15)
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