Questão 4e6a702c-af
Prova:UECE 2013
Disciplina:Inglês
Assunto:Análise sintática | Syntax Parsing
In the following question, some sentences from the text may have been modified to fit certain grammatical structures.
The sentences “…they know it will make them
employable.” and “…Amazon tells them what books
they should read” contain, respectively, a/an
In the following question, some sentences from the text may have been modified to fit certain grammatical structures.
The sentences “…they know it will make them employable.” and “…Amazon tells them what books they should read” contain, respectively, a/an
TEXT
HARVARD BUSINESS REVIEW calls data
science “the sexiest job in the 21st century,” and by
most accounts this hot new field promises to
revolutionize industries from business to
government, health care to academia. The field has been spawned by the
enormous amounts of data that modern technologies
create — be it the online behavior of Facebook
users, tissue samples of cancer patients,
purchasing habits of grocery shoppers or crime
statistics of cities. Data scientists are the magicians
of the Big Data era. They crunch the data, use
mathematical models to analyze it and create
narratives or visualizations to explain it, then
suggest how to use the information to make
decisions. In the last few years, dozens of programs
under a variety of names have sprung up in
response to the excitement about Big Data, not to
mention the six-figure salaries for some recent
graduates. In the fall, Columbia will offer new
master’s and certificate programs heavy on data.
The University of San Francisco will soon graduate
its charter class of students with a master’s in
analytics. Rachel Schutt, a senior research scientist at
Johnson Research Labs, taught “Introduction to Data
Science” last semester at Columbia (its first course
with “data science” in the title). She described the
data scientist this way: “a hybrid computer scientist
software engineer statistician.” And added: “The
best tend to be really curious people, thinkers who
ask good questions and are O.K. dealing with
unstructured situations and trying to find structure
in them.” Eurry Kim, a 30-year-old “wannabe data
scientist,” is studying at Columbia for a master’s in
quantitative methods in the social sciences and
plans to use her degree for government service. She
discovered the possibilities while working as a
corporate tax analyst at the Internal Revenue
Service. She might, for example, analyze tax return
data to develop algorithms that flag fraudulent
filings, or cull national security databases to spot
suspicious activity. Some of her classmates are hoping to apply
their skills to e-commerce, where data about users’
browsing history is gold. “This is a generation of kids that grew up
with data science around them — Netflix telling
them what movies they should watch, Amazon
telling them what books they should read — so this
is an academic interest with real-world applications,”
said Chris Wiggins, a professor of applied
mathematics at Columbia who is involved in its new
Institute for Data Sciences and Engineering. “And,”
he added, “they know it will make them
employable.” Universities can hardly turn out data
scientists fast enough. To meet demand from employers, the United States will need to increase
the number of graduates with skills handling large
amounts of data by as much as 60 percent,
according to a report by McKinsey Global Institute.
There will be almost half a million jobs in five
years, and a shortage of up to 190,000 qualified
data scientists, plus a need for 1.5 million
executives and support staff who have an
understanding of data. Because data science is so new,
universities are scrambling to define it and develop
curriculums. As an academic field, it cuts across
disciplines, with courses in statistics, analytics,
computer science and math, coupled with the
specialty a student wants to analyze, from patterns
in marine life to historical texts. With the sheer volume, variety and speed
of data today, as well as developing technologies,
programs are more than a repackaging of existing
courses. “Data science is emerging as an academic
discipline, defined not by a mere amalgamation of
interdisciplinary fields but as a body of knowledge,
a set of professional practices, a professional
organization and a set of ethical responsibilities,”
said Christopher Starr, chairman of the computer
science department at the College of Charleston,
one of a few institutions offering data science at
the undergraduate level. Most master’s degree programs in data
science require basic programming skills. They
start with what Ms. Schutt describes as the
“boring” part — scraping and cleaning raw data
and “getting it into a nice table where you can
actually analyze it.” Many use data sets provided
by businesses or government, and pass back their
results. Some host competitions to see which
student can come up with the best solution to a
company’s problem. Studying a Web user’s data has privacy
implications. Using data to decide someone’s
eligibility for a line of credit or health insurance, or
even recommending who they friend on
Facebook, can affect their lives. “We’re building
these models that have impact on human life,” Ms.
Schutt said. “How can we do that carefully?” Ethics
classes address these questions. Finally, students have to learn to
communicate their findings, visually and orally,
and they need business know-how, perhaps to
develop new products.
From: www.nytimes.com
TEXT
HARVARD BUSINESS REVIEW calls data
science “the sexiest job in the 21st century,” and by
most accounts this hot new field promises to
revolutionize industries from business to
government, health care to academia.
The field has been spawned by the
enormous amounts of data that modern technologies
create — be it the online behavior of Facebook
users, tissue samples of cancer patients,
purchasing habits of grocery shoppers or crime
statistics of cities. Data scientists are the magicians
of the Big Data era. They crunch the data, use
mathematical models to analyze it and create
narratives or visualizations to explain it, then
suggest how to use the information to make
decisions.
In the last few years, dozens of programs
under a variety of names have sprung up in
response to the excitement about Big Data, not to
mention the six-figure salaries for some recent
graduates. In the fall, Columbia will offer new
master’s and certificate programs heavy on data.
The University of San Francisco will soon graduate
its charter class of students with a master’s in
analytics.
Rachel Schutt, a senior research scientist at
Johnson Research Labs, taught “Introduction to Data
Science” last semester at Columbia (its first course
with “data science” in the title). She described the
data scientist this way: “a hybrid computer scientist
software engineer statistician.” And added: “The
best tend to be really curious people, thinkers who
ask good questions and are O.K. dealing with
unstructured situations and trying to find structure
in them.”
Eurry Kim, a 30-year-old “wannabe data
scientist,” is studying at Columbia for a master’s in
quantitative methods in the social sciences and
plans to use her degree for government service. She
discovered the possibilities while working as a
corporate tax analyst at the Internal Revenue
Service. She might, for example, analyze tax return
data to develop algorithms that flag fraudulent
filings, or cull national security databases to spot
suspicious activity.
Some of her classmates are hoping to apply
their skills to e-commerce, where data about users’
browsing history is gold.
“This is a generation of kids that grew up
with data science around them — Netflix telling
them what movies they should watch, Amazon
telling them what books they should read — so this
is an academic interest with real-world applications,”
said Chris Wiggins, a professor of applied
mathematics at Columbia who is involved in its new
Institute for Data Sciences and Engineering. “And,”
he added, “they know it will make them
employable.”
Universities can hardly turn out data
scientists fast enough. To meet demand from employers, the United States will need to increase
the number of graduates with skills handling large
amounts of data by as much as 60 percent,
according to a report by McKinsey Global Institute.
There will be almost half a million jobs in five
years, and a shortage of up to 190,000 qualified
data scientists, plus a need for 1.5 million
executives and support staff who have an
understanding of data.
Because data science is so new,
universities are scrambling to define it and develop
curriculums. As an academic field, it cuts across
disciplines, with courses in statistics, analytics,
computer science and math, coupled with the
specialty a student wants to analyze, from patterns
in marine life to historical texts.
With the sheer volume, variety and speed
of data today, as well as developing technologies,
programs are more than a repackaging of existing
courses. “Data science is emerging as an academic
discipline, defined not by a mere amalgamation of
interdisciplinary fields but as a body of knowledge,
a set of professional practices, a professional
organization and a set of ethical responsibilities,”
said Christopher Starr, chairman of the computer
science department at the College of Charleston,
one of a few institutions offering data science at
the undergraduate level.
Most master’s degree programs in data
science require basic programming skills. They
start with what Ms. Schutt describes as the
“boring” part — scraping and cleaning raw data
and “getting it into a nice table where you can
actually analyze it.” Many use data sets provided
by businesses or government, and pass back their
results. Some host competitions to see which
student can come up with the best solution to a
company’s problem.
Studying a Web user’s data has privacy
implications. Using data to decide someone’s
eligibility for a line of credit or health insurance, or
even recommending who they friend on
Facebook, can affect their lives. “We’re building
these models that have impact on human life,” Ms.
Schutt said. “How can we do that carefully?” Ethics
classes address these questions.
Finally, students have to learn to
communicate their findings, visually and orally,
and they need business know-how, perhaps to
develop new products.
From: www.nytimes.com
A
subject noun clause and an object noun
clause.
B
object noun clause and a subject noun clause.
C
subject noun clause and a subject noun
clause.
D
object noun clause and an object noun clause.
Gabarito comentado
R
Renata Fernandes,Monitor com apoio de IA
Gabarito: D
Fundamento decisivo: O critério decisivo é a função sintática da noun clause na oração principal: em “they know it will make them employable”, “it will make them employable” é complemento de “know”; em “Amazon tells them what books they should read”, “what books they should read” é complemento de “tells”. Em ambos os casos, a classificação correta é object noun clause, o que confirma a alternativa D.
Tema central: função sintática de noun clauses
Análise das alternativas
A
Errada
Está errada porque classifica a primeira oração como subject noun clause, mas o sujeito da oração principal já está expresso em “they”. Assim, “it will make them employable” não ocupa posição de sujeito; ela completa o verbo “know”, funcionando como objeto. A segunda classificação como objeto é compatível com a base, mas a alternativa falha na primeira metade.
B
Errada
Está errada porque, embora acerte a primeira classificação, erra a segunda. Em “Amazon tells them what books they should read”, o sujeito da oração principal é “Amazon”, não a oração introduzida por “what”. A presença de “what” não muda a função sintática da oração subordinada, que é o conteúdo de “tells”, portanto objeto.
C
Errada
Está errada porque atribui função de sujeito às duas orações subordinadas, mas em ambas as sentenças o sujeito da oração principal já aparece de forma explícita: “they” e “Amazon”. Logo, nenhuma das duas noun clauses pode ser classificada como subject noun clause; ambas são complementos verbais.
D
Certa
A alternativa D está correta porque, nas duas sentenças, a oração subordinada exerce função de complemento verbal na oração principal. Na primeira, o sujeito já é “they” e o verbo principal é “know”; portanto, “it will make them employable” não pode ser sujeito, mas sim o objeto desse verbo. Na segunda, o sujeito é “Amazon” e o verbo principal é “tells”; “what books they should read” corresponde ao conteúdo comunicado, logo também é object noun clause. O “respectively” confirma que essa classificação vale nessa mesma ordem para as duas sentenças.
Pegadinha da questão
A banca explora a confusão entre identificar uma noun clause e identificar sua função. O erro típico é ver uma oração encaixada e chamá-la de subject clause, ou tomar o “what” da segunda sentença como sinal automático de sujeito, ignorando que a classificação depende da função em relação ao verbo principal. Também era necessário respeitar o “respectively”.
Dica para questões semelhantes
- Identifique primeiro o sujeito e o verbo da oração principal; se eles já estiverem expressos, a subordinada não é subject noun clause.
- Classifique a noun clause pela função que exerce na oração principal, não pelo conectivo que a introduz ou pela ausência dele.
- Em estruturas como “verb + someone + clause”, verifique se a oração subordinada é o conteúdo do verbo; se for, trata-se de object noun clause.
- Quando houver “respectively”, mantenha a correspondência exata entre a primeira sentença e a primeira classificação, e entre a segunda sentença e a segunda classificação.
