D204 Lesson3 Questions and Answers
100% Pass
The demand for data science, as measured by the number of job ads, has gone up by 29% in just
one year. How much has the availability of data scientists, as measured by job
...
D204 Lesson3 Questions and Answers
100% Pass
The demand for data science, as measured by the number of job ads, has gone up by 29% in just
one year. How much has the availability of data scientists, as measured by job searches, gone up
in the same time? ✔✔less-about 14%
The availability of data scientists has, in fact, gone up but only by 14%, which is not enough to
match the demand.
In data science, what is meant by a "unicorn"? ✔✔a full-stack data scientist who can do everything
at a professional level
Data scientist "unicorns," or people who can do everything well, are exceptionally rare, and so it
is more common to use a diverse team.
The first step in the data science pathway is "define goals." Why is the best place to start a data
science project? ✔✔clarifying your project's goals up front will help you at every step of the
project pathway, from framing questions, to choosing data and algorithms, and interpreting and
applying your results.
Goals influence every step of the data science pathway, from planning to wrangling to modeling
to applying.
Why is substantive/domain expertise important for data scientists? ✔✔It helps them to know what
constitutes value in their field and to implement their insights.
The ability to identify domain-specific value and take steps to implement insights give critical
context and direction to data science work.
In a data science team, who is the person who generally frames the business-relevant questions
and solutions? ✔✔the manager
The second group of steps in the data science pathway are known as "wrangling." What does
wrangling mean in this context? ✔✔getting data, cleaning data, exploring data, and refining data
In data science, "wrangling" is also known as "data wrangling," "data munging," or "data
preparation." These tasks lay the groundwork for analysis and interpretation.
What is one of the rare qualities that creates such a high demand for data scientists? ✔✔the ability
to find order, meaning, and value in unstructured data
Data scientists are valuable because they are able to find value in unstructured data, but they're
also able to predict outcomes and automate processes.
When you have a data set of collected data and variables, why would you run a correlation? ✔✔to
find out whether there are any relationships between the observations and the variables
The real purpose of having the data is to find relationships, and then determine whether the
relationships are meaningful.
When you are examining a data set pertaining to recidivism that contains information on an
individual's past, which ethical question must you consider? ✔✔Does the prediction of recidivism
affect a person's autonomy?
It is both ethical and legitimate to analyze recidivism in the context of a societal good, but not at
the expense of ethical sensitivity.
At its core, what is the basis of being an ethical person? ✔✔At its core, being an ethical person is
valuing the well-being of others.
What is the position of US courts about extraterritorial reach of American law regarding privacy
in data usage? ✔✔US law considers the physical boundaries of the United States, and will not
allow the government to reach beyond these boundaries.
What are the two values involved in being an ethical person? ✔✔caring for one's own well-being
and the well-being of others
Why do data and information systems come before laws? ✔✔Laws need to be driven by specific
products, processes, or events that already took place.
Laws are created after something takes place, to control parameters.
Why is Application so important in the Issue, Rule, Application, Conclusion (IRAC) analysis?
✔✔Applying the law to a particular situation will lead to the final outcome of the situation.
Only when you do this will you be able to reach an ultimate conclusion about whether or not a law
has been violated.
How can you avoid the subjectivity in data that might provide an untrue result, even if the "lie"
was unintentional? ✔✔Ask questions before accepting conclusions.
This can reveal any underlying bias and subjectivity in the data
Perhaps the best data oath to fully apply to ethical obligations is one that includes a supererogatory
claim. What does this mean? ✔✔doing more with data work than a corporate bottom line
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