ISM 3541 EXAM 3 KERWIN | COMPLETE
SOLUTION | 2025| GRADED A+
How does a business ensure that it gets the most from the wealth of data?" and the
answer is two-fold - The two important factors are (a.) the firm's management must
think data-analytically,
(b) the management must create a culture where data science, and data scientists, will
thrive
Data science ultimately may lead to - Knowledge management objects, where the
customer is both a consumer of product/service and a supplier of valuable information
Knowing the history in the case of the Amazon business case shows that entrepreneurs
and investors should consider a data strategy that resolves the following questions -
What historical circumstances now exist that may not continue indefinitely?
What historical circumstances allow me to gain access to or to build a data asset more
cheaply than will be possible in the future?
Or which circumstances will allow me to build a data science team now that would be
very costly (or impossible) to build in the future if the company waits?
Amazon on the other hand developed a strategy with data science that became a
competitive advantage - created a model of being a low-cost reseller of books and
during this time(dot.com bubble burst) where investors kept rewarding their behavior,
Amazon gathered huge amounts of data assets such as consumers' buying preferences
and online product reviews which translated into "recommendations and product
ratings" that we see other companies replicating in their products/services today.
*The take away is customers value the data-driven recommendations and product
reviews/ratings that Amazon provides even though the initial idea was to be the low-cost
provider of books*
Protect Unique Intellectual Property - Data science intellectual property can include
novel techniques for mining the data or for using the results
Protect Unique Intangible Collateral Assets - Your model is not what your data
scientists design, it's what your engineers implement; therefore, the actual source of
good performance of a successful data science solution is unclear to competitors
Big Data - interested in connecting with their customer base and gaining insight into
what motivates their behavior, what they think and what they value. Big Data is
conceptually the data store, or the data base, that is decentralized and complex.
, Data Science - relies on data mining techniques (see section Data Science Terms and
Concepts) and life cycles such as CRISP-DM to bring solutions to life.
Key Point: Writing through Big Data occurs through networks
Data Literacy - The tools, techniques, and rhetorical practices of data-driven
arguments.
Focusing on the pervasive and ubiquitous uses of data visualization.
Good data science managers also must possess a set of other abilities that are rare in a
single individual - Understanding the needs of the business is critical to interacting with
colleagues across the organization so that authentic collaboration across functional
areas can occur with the hope of generating new ideas
Communicating well with and be respected by both "techies" and "suits"
Coordinating technically complex activities which translates into have a basic
understanding of the technical architectures of the business such as the data systems
or production software systems
Connect science to company vison and culture
Information Technology Infrastructure Library - Continous improvement is major tenant
of framework and is designed to sharpen effectiveness and efficiency of delivery
service, information, and technology to companies
The data gathered to monitor and control processes is a great data source for creating
Key Performance Indicators and measuring the "people-process-technology" of a
business.
Emotional Intelligence - awareness of others and being able to seek to understand in
order to be as neutral as possible which is hard because we are human.
As a data scientist, we need to be - 'agnostic'; ensure my clients that I'm more
considered about the "people - process" of their business and that the technology is
where the business needs to drive the decision based on technical strategy and budget.
Classification - Supervised method focused on predicting the target variable of new,
unseen instance by determining which segment that the data point resides.
Having space between data sets supports classification as a method.
We can even track the predictions and build snapshots which will provide an
opportunity to tell a data story through trends
(Dataset split by 4 leaf nodes)
Logistical Regression - Supervised method focused on predicting and applying
probability to our model.
A probability typically ranges from zero to one Odds: applying the notion of the
likelihood of an event
When applying the odds, data scientists are interested in the interpretation of the
distance from the separating boundary by taking the logarithm of the odds called the
"log-odds"
SOLUTION | 2025| GRADED A+
How does a business ensure that it gets the most from the wealth of data?" and the
answer is two-fold - The two important factors are (a.) the firm's management must
think data-analytically,
(b) the management must create a culture where data science, and data scientists, will
thrive
Data science ultimately may lead to - Knowledge management objects, where the
customer is both a consumer of product/service and a supplier of valuable information
Knowing the history in the case of the Amazon business case shows that entrepreneurs
and investors should consider a data strategy that resolves the following questions -
What historical circumstances now exist that may not continue indefinitely?
What historical circumstances allow me to gain access to or to build a data asset more
cheaply than will be possible in the future?
Or which circumstances will allow me to build a data science team now that would be
very costly (or impossible) to build in the future if the company waits?
Amazon on the other hand developed a strategy with data science that became a
competitive advantage - created a model of being a low-cost reseller of books and
during this time(dot.com bubble burst) where investors kept rewarding their behavior,
Amazon gathered huge amounts of data assets such as consumers' buying preferences
and online product reviews which translated into "recommendations and product
ratings" that we see other companies replicating in their products/services today.
*The take away is customers value the data-driven recommendations and product
reviews/ratings that Amazon provides even though the initial idea was to be the low-cost
provider of books*
Protect Unique Intellectual Property - Data science intellectual property can include
novel techniques for mining the data or for using the results
Protect Unique Intangible Collateral Assets - Your model is not what your data
scientists design, it's what your engineers implement; therefore, the actual source of
good performance of a successful data science solution is unclear to competitors
Big Data - interested in connecting with their customer base and gaining insight into
what motivates their behavior, what they think and what they value. Big Data is
conceptually the data store, or the data base, that is decentralized and complex.
, Data Science - relies on data mining techniques (see section Data Science Terms and
Concepts) and life cycles such as CRISP-DM to bring solutions to life.
Key Point: Writing through Big Data occurs through networks
Data Literacy - The tools, techniques, and rhetorical practices of data-driven
arguments.
Focusing on the pervasive and ubiquitous uses of data visualization.
Good data science managers also must possess a set of other abilities that are rare in a
single individual - Understanding the needs of the business is critical to interacting with
colleagues across the organization so that authentic collaboration across functional
areas can occur with the hope of generating new ideas
Communicating well with and be respected by both "techies" and "suits"
Coordinating technically complex activities which translates into have a basic
understanding of the technical architectures of the business such as the data systems
or production software systems
Connect science to company vison and culture
Information Technology Infrastructure Library - Continous improvement is major tenant
of framework and is designed to sharpen effectiveness and efficiency of delivery
service, information, and technology to companies
The data gathered to monitor and control processes is a great data source for creating
Key Performance Indicators and measuring the "people-process-technology" of a
business.
Emotional Intelligence - awareness of others and being able to seek to understand in
order to be as neutral as possible which is hard because we are human.
As a data scientist, we need to be - 'agnostic'; ensure my clients that I'm more
considered about the "people - process" of their business and that the technology is
where the business needs to drive the decision based on technical strategy and budget.
Classification - Supervised method focused on predicting the target variable of new,
unseen instance by determining which segment that the data point resides.
Having space between data sets supports classification as a method.
We can even track the predictions and build snapshots which will provide an
opportunity to tell a data story through trends
(Dataset split by 4 leaf nodes)
Logistical Regression - Supervised method focused on predicting and applying
probability to our model.
A probability typically ranges from zero to one Odds: applying the notion of the
likelihood of an event
When applying the odds, data scientists are interested in the interpretation of the
distance from the separating boundary by taking the logarithm of the odds called the
"log-odds"
