Questões de Vestibular Sobre inglês
Foram encontradas 5.992 questões
Ano: 2013
Banca:
UECE-CEV
Órgão:
UECE
Prova:
UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261833
Inglês
Texto associado
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
The sentences “Many use data sets provided
by businesses or government, and pass back their
results.” and “Because data science is so new,
universities are scrambling to define it…” contain,
respectively, a
Ano: 2013
Banca:
UECE-CEV
Órgão:
UECE
Prova:
UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261832
Inglês
Texto associado
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
The sentences “They crunch the data, use
mathematical models to analyze it and create
narratives or visualizations to explain it…” and “In
the last few years, dozens of programs under a
variety of names have sprung up in response to the
excitement about Big Data...” should be classified
respectively as
Ano: 2013
Banca:
UECE-CEV
Órgão:
UECE
Prova:
UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261831
Inglês
Texto associado
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
In terms of verb tense, the sentences
“Rachel Schutt, a senior research scientist at
Johnson Research Labs, taught ‘Introduction to
Data Science’ last semester at Columbia.”, “In the
last few years, dozens of programs under a variety
of names have sprung up in response to the
excitement about Big Data.” and “Most master’s
degree programs in data science require basic
programming skills.” are, respectively, in the
Ano: 2013
Banca:
UECE-CEV
Órgão:
UECE
Prova:
UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261830
Inglês
Texto associado
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
The functions of the words purchasing,
dealing, filings, programming and recommending
in the text are respectively
Ano: 2013
Banca:
UECE-CEV
Órgão:
UECE
Prova:
UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261829
Inglês
Texto associado
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
Considering the word shopper in the text,
an example of a word with similar meaning is
Ano: 2013
Banca:
UECE-CEV
Órgão:
UECE
Prova:
UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261828
Inglês
Texto associado
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
Some of Eurry Kim’s peers expect to use
their abilities on
Ano: 2013
Banca:
UECE-CEV
Órgão:
UECE
Prova:
UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261827
Inglês
Texto associado
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
According to the text, in terms of what is
required from a student in order to apply for a
master’s degree in the area of data science, one
must have
Ano: 2013
Banca:
UECE-CEV
Órgão:
UECE
Prova:
UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261826
Inglês
Texto associado
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
As to the way academic institutions are
reacting in response to the enormous need of
professionals in the field of data science, the text
informs that
Ano: 2013
Banca:
UECE-CEV
Órgão:
UECE
Prova:
UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261825
Inglês
Texto associado
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
According to the text, besides being
referred to as a sexy job in our century, data
science
Ano: 2013
Banca:
UECE-CEV
Órgão:
UECE
Prova:
UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261824
Inglês
Texto associado
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
Data scientists are referred to as magicians
due to the fact that, among other things, they can
Ano: 2013
Banca:
UECE-CEV
Órgão:
UECE
Prova:
UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261823
Inglês
Texto associado
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
Ethical responsibilities refer to the fact
that
Q1261545
Inglês
Texto associado
Are video games the next big college sport?
New kinds of competitors are joining student athletes at colleges and universities around the
country. But not everyone agrees that these video game players are taking part in a sport. Sports are
an established part of college life and a source of income for some schools. The football stadium at
the University of Michigan in Ann Arbor, for example, holds over 107,000 people. It is one of the
largest sports stadiums in the world!
Like most traditional college sports, video game competitions involve two or more teams of students
officially representing their schools. Team members wear clothing with their names and school
colors. They even have coaches giving them advice on how best to win.
However, there is no running or jumping or hitting other players. In fact, these new events are
different from any traditional athletic activity. They are called esports, and they take place not in
the real world, but in computers or other video game systems.
Professional video game competitions have been popular around the world for years. Teams and
individuals compete for prize money and awards in strategic military combat games like Starcraft
and one-on-one fighting games like Street Fighter.
Fonte: adaptado de < https://app.engoo.com/daily-news/article/are-video-games-the-next-big-collegesport/xAFD2HW1EeePT98egCHuDw
Mark the CORRECT alternative, according to the text.
Q1261544
Inglês
Texto associado
Are video games the next big college sport?
New kinds of competitors are joining student athletes at colleges and universities around the
country. But not everyone agrees that these video game players are taking part in a sport. Sports are
an established part of college life and a source of income for some schools. The football stadium at
the University of Michigan in Ann Arbor, for example, holds over 107,000 people. It is one of the
largest sports stadiums in the world!
Like most traditional college sports, video game competitions involve two or more teams of students
officially representing their schools. Team members wear clothing with their names and school
colors. They even have coaches giving them advice on how best to win.
However, there is no running or jumping or hitting other players. In fact, these new events are
different from any traditional athletic activity. They are called esports, and they take place not in
the real world, but in computers or other video game systems.
Professional video game competitions have been popular around the world for years. Teams and
individuals compete for prize money and awards in strategic military combat games like Starcraft
and one-on-one fighting games like Street Fighter.
Fonte: adaptado de < https://app.engoo.com/daily-news/article/are-video-games-the-next-big-collegesport/xAFD2HW1EeePT98egCHuDw
De acordo com o texto, escolha a alternativa INCORRETA.
Q1261543
Inglês
Texto associado
What Parents Can Do to Nurture Good Writers
Steve Graham, a professor at Arizona State University’s Teachers College, has been researching how young
people learn to write for more than 30 years. He is a co-author of numerous books on writing instruction,
including “Powerful Writing Strategies for All Students.”
How does reading at home help children become better writers?
is really critical, but it’s not enough. We don’t have much evidence that if you just read more, you’ll
be a better writer. But analyzing text does make a difference. So when we read to kids, we can also have
conversations with them about the author’s craft. How did this author make this place seem real in terms of
description? What words did they use? How did they present this idea or this argument?
Should a parent correct a child’s writing, or just be encouraging?
Sometimes when kids come to you to share what they’re writing, they’re not coming for feedback. They are
coming for affirmation. It’s really important we emphasize first and foremost what we really like about it.
And if you’re going to give feedback, just pick one or two things. English teachers — and parents are guilty
of this, too — sometimes overwhelm kids with more feedback than they can absorb all at once. The other
thing that’s really important, particularly for parents, is to remember that they don’t own this piece. It’s their
child’s.
What should parents look for to assess the writing instruction at their child’s school?
After about third grade, very little time is devoted to explicit writing instruction. It’s like we’ve imagined
that kids have acquired what they need to know to be good writers by then! In middle and high school, the
most common activities are fill-in-the-blanks on worksheets, writing single sentences, making lists or writing
a paragraph summary. When you start talking about persuasive essays or an informative paper, those things
occur infrequently in English class and even less so in social studies and science.
So the first questions are: “Is my kid writing at school, and was he given writing assignments to work on at
home? Do those require writing more extended thoughts for the purposes of analysis and interpretation?”
That’s what they need to be able to do for college.
Fonte: adaptado de < https://www.nytimes.com/2017/08/02/education/edlife/parents-children-writing.html>
No texto, o autor afirma que
Q1261542
Inglês
Texto associado
What Parents Can Do to Nurture Good Writers
Steve Graham, a professor at Arizona State University’s Teachers College, has been researching how young
people learn to write for more than 30 years. He is a co-author of numerous books on writing instruction,
including “Powerful Writing Strategies for All Students.”
How does reading at home help children become better writers?
is really critical, but it’s not enough. We don’t have much evidence that if you just read more, you’ll
be a better writer. But analyzing text does make a difference. So when we read to kids, we can also have
conversations with them about the author’s craft. How did this author make this place seem real in terms of
description? What words did they use? How did they present this idea or this argument?
Should a parent correct a child’s writing, or just be encouraging?
Sometimes when kids come to you to share what they’re writing, they’re not coming for feedback. They are
coming for affirmation. It’s really important we emphasize first and foremost what we really like about it.
And if you’re going to give feedback, just pick one or two things. English teachers — and parents are guilty
of this, too — sometimes overwhelm kids with more feedback than they can absorb all at once. The other
thing that’s really important, particularly for parents, is to remember that they don’t own this piece. It’s their
child’s.
What should parents look for to assess the writing instruction at their child’s school?
After about third grade, very little time is devoted to explicit writing instruction. It’s like we’ve imagined
that kids have acquired what they need to know to be good writers by then! In middle and high school, the
most common activities are fill-in-the-blanks on worksheets, writing single sentences, making lists or writing
a paragraph summary. When you start talking about persuasive essays or an informative paper, those things
occur infrequently in English class and even less so in social studies and science.
So the first questions are: “Is my kid writing at school, and was he given writing assignments to work on at
home? Do those require writing more extended thoughts for the purposes of analysis and interpretation?”
That’s what they need to be able to do for college.
Fonte: adaptado de < https://www.nytimes.com/2017/08/02/education/edlife/parents-children-writing.html>
Mark the INCORRECT alternative.
Q1261541
Inglês
Texto associado
What Parents Can Do to Nurture Good Writers
Steve Graham, a professor at Arizona State University’s Teachers College, has been researching how young
people learn to write for more than 30 years. He is a co-author of numerous books on writing instruction,
including “Powerful Writing Strategies for All Students.”
How does reading at home help children become better writers?
is really critical, but it’s not enough. We don’t have much evidence that if you just read more, you’ll
be a better writer. But analyzing text does make a difference. So when we read to kids, we can also have
conversations with them about the author’s craft. How did this author make this place seem real in terms of
description? What words did they use? How did they present this idea or this argument?
Should a parent correct a child’s writing, or just be encouraging?
Sometimes when kids come to you to share what they’re writing, they’re not coming for feedback. They are
coming for affirmation. It’s really important we emphasize first and foremost what we really like about it.
And if you’re going to give feedback, just pick one or two things. English teachers — and parents are guilty
of this, too — sometimes overwhelm kids with more feedback than they can absorb all at once. The other
thing that’s really important, particularly for parents, is to remember that they don’t own this piece. It’s their
child’s.
What should parents look for to assess the writing instruction at their child’s school?
After about third grade, very little time is devoted to explicit writing instruction. It’s like we’ve imagined
that kids have acquired what they need to know to be good writers by then! In middle and high school, the
most common activities are fill-in-the-blanks on worksheets, writing single sentences, making lists or writing
a paragraph summary. When you start talking about persuasive essays or an informative paper, those things
occur infrequently in English class and even less so in social studies and science.
So the first questions are: “Is my kid writing at school, and was he given writing assignments to work on at
home? Do those require writing more extended thoughts for the purposes of analysis and interpretation?”
That’s what they need to be able to do for college.
Fonte: adaptado de < https://www.nytimes.com/2017/08/02/education/edlife/parents-children-writing.html>
A partir do texto acima, assinale a alternativa que contém a ideia central do texto.
Q1261540
Inglês
Texto associado
How does Hurricane Harvey compare with Katrina? Here’s what we know
Although it is still unfolding, Harvey, now a tropical storm, evokes comparisons to Hurricane Katrina in
2005. Here’s a quick rundown of what we know about similarities and differences between the two.
• The Cities
Katrina: Before the storm, New Orleans was a small city of about 455,000 people that lay in large part
below sea level, ostensibly protected by a system of levee walls. Its population never fully recovered from
the evacuation and destruction and remains below 400,000.
Harvey: Houston is a sprawling, car-dependent, low-lying but not below sea level city. It has a population
of more than two million people, with a system of bayous and waterways to manage flooding.
• The Storms
Katrina: It made landfall near the Louisiana/Mississippi border on Aug. 29, 2005, as a Category 3 storm
and measured 350 miles across. However, the relatively low classification, was deceptive because Katrina
produced the highest storm surge ever recorded in the U.S.
Harvey: It made landfall in Rockport, Tex., on Friday as a Category 4 storm, measuring 200 miles across,
but was quickly downgraded. As of Monday, it was expected to linger for days, causing the National Weather
Service to warn, “This event is unprecedented and all impacts are unknown.”
• Deaths and Damage
Katrina: One of the deadliest hurricanes ever to strike the U.S., Katrina was responsible for 1,833 deaths,
and some bodies were untouched for days. The storm inflicted more than $100 billion in damage, with most
of it caused by wind, storm surge and the failure of the levees.
Harvey: Local officials have reported at least 10 deaths in Texas since the storm began, but heavy rains and
flooding are expected to continue at least through Friday. Most of the damage could be caused by flooding.
As for the economy, the Gulf region’s capacity as an oil and gas does not appear to have been seriously
compromised.
• Assistance
Katrina: The storm displaced over a million people and damaged or destroyed 275,000 homes. Almost a
million households received individual assistance from the Federal Emergency Management Agency.
Harvey: We don’t know yet how many people will be forced out of their homes. But the vast majority of
homes in Harvey’s path are not insured against flooding, according to figures from the National Flood
Insurance Program. It is estimated that 450,000 people were likely to seek federal aid.
Fonte: adaptado de < https://www.nytimes.com/2017/08/28/us/hurricane-katrina-harvey.html>
Mark the INCORRECT alternative, according to the text.
Q1261539
Inglês
Texto associado
How does Hurricane Harvey compare with Katrina? Here’s what we know
Although it is still unfolding, Harvey, now a tropical storm, evokes comparisons to Hurricane Katrina in
2005. Here’s a quick rundown of what we know about similarities and differences between the two.
• The Cities
Katrina: Before the storm, New Orleans was a small city of about 455,000 people that lay in large part
below sea level, ostensibly protected by a system of levee walls. Its population never fully recovered from
the evacuation and destruction and remains below 400,000.
Harvey: Houston is a sprawling, car-dependent, low-lying but not below sea level city. It has a population
of more than two million people, with a system of bayous and waterways to manage flooding.
• The Storms
Katrina: It made landfall near the Louisiana/Mississippi border on Aug. 29, 2005, as a Category 3 storm
and measured 350 miles across. However, the relatively low classification, was deceptive because Katrina
produced the highest storm surge ever recorded in the U.S.
Harvey: It made landfall in Rockport, Tex., on Friday as a Category 4 storm, measuring 200 miles across,
but was quickly downgraded. As of Monday, it was expected to linger for days, causing the National Weather
Service to warn, “This event is unprecedented and all impacts are unknown.”
• Deaths and Damage
Katrina: One of the deadliest hurricanes ever to strike the U.S., Katrina was responsible for 1,833 deaths,
and some bodies were untouched for days. The storm inflicted more than $100 billion in damage, with most
of it caused by wind, storm surge and the failure of the levees.
Harvey: Local officials have reported at least 10 deaths in Texas since the storm began, but heavy rains and
flooding are expected to continue at least through Friday. Most of the damage could be caused by flooding.
As for the economy, the Gulf region’s capacity as an oil and gas does not appear to have been seriously
compromised.
• Assistance
Katrina: The storm displaced over a million people and damaged or destroyed 275,000 homes. Almost a
million households received individual assistance from the Federal Emergency Management Agency.
Harvey: We don’t know yet how many people will be forced out of their homes. But the vast majority of
homes in Harvey’s path are not insured against flooding, according to figures from the National Flood
Insurance Program. It is estimated that 450,000 people were likely to seek federal aid.
Fonte: adaptado de < https://www.nytimes.com/2017/08/28/us/hurricane-katrina-harvey.html>
Considerando o texto, assinale a alternativa que melhor traduz o trecho: Houston is a sprawling,
car-dependent, low-lying but not below sea level city
Q1261531
Inglês
Texto associado
REMNICK, D. Leonard Cohen makes it Darker. Available
at: www.TAGARCHIVES: Leonard Cohen – Bob Dylan
Interface. Accessed on Nov. 9th, 2016.
Select the alternative in which the word dare (l. 63) presents the same meaning and grammatical function as used in the text.
Q1261530
Inglês
Texto associado
REMNICK, D. Leonard Cohen makes it Darker. Available
at: www.TAGARCHIVES: Leonard Cohen – Bob Dylan
Interface. Accessed on Nov. 9th, 2016.
Consider the following propositions of rewriting the segment the verses are four elemental lines which change and move at predictable intervals (l. 20-22).
I - the verses are four elemental lines, changing and moving at predictable intervals. II - the verses are four basic lines which can change and move at predictable intervals. III- the verses are four elemental lines, subject to be changed and moved at predictable intervals.
Which are correct?
I - the verses are four elemental lines, changing and moving at predictable intervals. II - the verses are four basic lines which can change and move at predictable intervals. III- the verses are four elemental lines, subject to be changed and moved at predictable intervals.
Which are correct?
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