Questões de Vestibular Comentadas sobre inglês

Foram encontradas 2.761 questões

Ano: 2013 Banca: UECE-CEV Órgão: UECE Prova: UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261832 Inglês
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
Alternativas
Ano: 2013 Banca: UECE-CEV Órgão: UECE Prova: UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261831 Inglês
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
Alternativas
Ano: 2013 Banca: UECE-CEV Órgão: UECE Prova: UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261830 Inglês
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
Alternativas
Ano: 2013 Banca: UECE-CEV Órgão: UECE Prova: UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261829 Inglês
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
Alternativas
Ano: 2013 Banca: UECE-CEV Órgão: UECE Prova: UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261828 Inglês
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
Alternativas
Ano: 2013 Banca: UECE-CEV Órgão: UECE Prova: UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261827 Inglês
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
Alternativas
Ano: 2013 Banca: UECE-CEV Órgão: UECE Prova: UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261826 Inglês
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
Alternativas
Ano: 2013 Banca: UECE-CEV Órgão: UECE Prova: UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261825 Inglês
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
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Ano: 2013 Banca: UECE-CEV Órgão: UECE Prova: UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261824 Inglês
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
Alternativas
Ano: 2013 Banca: UECE-CEV Órgão: UECE Prova: UECE-CEV - 2013 - UECE - Vestibular - Inglês - 1º Dia |
Q1261823 Inglês
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
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Ano: 2013 Banca: UECE-CEV Órgão: UECE Prova: UECE-CEV - 2013 - UECE - Vestibular - Segundo Semestre |
Q1260554 Inglês

TEXT


      Hundreds of studies have assessed leadership styles, mainly by having employees report on how their managers typically behave. Researchers have also collected information on how effective managers are. After large numbers of such studies became available, reviewers aggregated them quantitatively to discover what kinds of leadership are effective.

      One conclusion that has emerged based on the research of the past 30 years is that a hybrid style known as transformational leadership is highly effective in most contemporary organizational contexts.

      A transformational leader acts as an inspirational role model, motivates others to go beyond the confines of their job descriptions, encourages creativity and innovation, fosters good human relationships, and develops the skills of followers. This type of leadership is effective because it fosters strong interpersonal bonds based on a leader’s charisma and consideration of others. These bonds enable leaders to promote high-quality performance by encouraging workers rather than threatening them, thus motivating them to exceed basic expectations.

      By bringing out the best in others, transformational leaders enhance the performance of groups and organizations.

      Transformational leadership is androgynous because it incorporates culturally masculine and feminine behaviors. This androgynous mixing of the masculine and feminine means that skill in this contemporary way of leading does not necessarily come naturally. It may require some effort and thought.

      Men often have to work on their social skills and women on being assertive enough to inspire others. It is nonetheless clear that both women and men can adapt to the demands of leadership in the transformational mode.

      One of the surprises of research on transformational leadership is that female managers are somewhat more transformational than male managers. In particular, they exceed men in their attention to human relationships. Also, in delivering incentives, women lean toward a more positive, reward-based approach and men toward a more negative and less effective, threat-based approach. In these respects, women appear to be better leaders than men, despite the double standard that can close women out of these roles.

      Why are women leaders more transformational when they are less likely to become leaders in the first place? One reason is that the double standard that slows women’s rise would work against mediocre women while allowing mediocre men to rise. As a consequence, the women who attain leadership roles really are better than the men on average.

      It is also true women generally avoid more domineering, “command and control” behavior because of the backlash they receive if they lead in this way. Men can often get away with autocratic behavior that is roundly disliked in women. Ironically, this backlash against domineering women may foster good leadership because the androgynous middle ground is more likely to bring success. Leaders gain less from ordering others about than from forming teams of smart, motivated collaborators who together figure out how to solve problems and get work done.

From: http://www.nytimes.com/ 2013/03/20  

Women usually refuse to behave in a domineering way due to the fact that they
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Ano: 2013 Banca: UECE-CEV Órgão: UECE Prova: UECE-CEV - 2013 - UECE - Vestibular - Segundo Semestre |
Q1260553 Inglês

TEXT


      Hundreds of studies have assessed leadership styles, mainly by having employees report on how their managers typically behave. Researchers have also collected information on how effective managers are. After large numbers of such studies became available, reviewers aggregated them quantitatively to discover what kinds of leadership are effective.

      One conclusion that has emerged based on the research of the past 30 years is that a hybrid style known as transformational leadership is highly effective in most contemporary organizational contexts.

      A transformational leader acts as an inspirational role model, motivates others to go beyond the confines of their job descriptions, encourages creativity and innovation, fosters good human relationships, and develops the skills of followers. This type of leadership is effective because it fosters strong interpersonal bonds based on a leader’s charisma and consideration of others. These bonds enable leaders to promote high-quality performance by encouraging workers rather than threatening them, thus motivating them to exceed basic expectations.

      By bringing out the best in others, transformational leaders enhance the performance of groups and organizations.

      Transformational leadership is androgynous because it incorporates culturally masculine and feminine behaviors. This androgynous mixing of the masculine and feminine means that skill in this contemporary way of leading does not necessarily come naturally. It may require some effort and thought.

      Men often have to work on their social skills and women on being assertive enough to inspire others. It is nonetheless clear that both women and men can adapt to the demands of leadership in the transformational mode.

      One of the surprises of research on transformational leadership is that female managers are somewhat more transformational than male managers. In particular, they exceed men in their attention to human relationships. Also, in delivering incentives, women lean toward a more positive, reward-based approach and men toward a more negative and less effective, threat-based approach. In these respects, women appear to be better leaders than men, despite the double standard that can close women out of these roles.

      Why are women leaders more transformational when they are less likely to become leaders in the first place? One reason is that the double standard that slows women’s rise would work against mediocre women while allowing mediocre men to rise. As a consequence, the women who attain leadership roles really are better than the men on average.

      It is also true women generally avoid more domineering, “command and control” behavior because of the backlash they receive if they lead in this way. Men can often get away with autocratic behavior that is roundly disliked in women. Ironically, this backlash against domineering women may foster good leadership because the androgynous middle ground is more likely to bring success. Leaders gain less from ordering others about than from forming teams of smart, motivated collaborators who together figure out how to solve problems and get work done.

From: http://www.nytimes.com/ 2013/03/20  

As to the effectiveness of managers, researchers have found, after many years of study, that
Alternativas
Ano: 2013 Banca: UECE-CEV Órgão: UECE Prova: UECE-CEV - 2013 - UECE - Vestibular - Segundo Semestre |
Q1260552 Inglês

TEXT


      Hundreds of studies have assessed leadership styles, mainly by having employees report on how their managers typically behave. Researchers have also collected information on how effective managers are. After large numbers of such studies became available, reviewers aggregated them quantitatively to discover what kinds of leadership are effective.

      One conclusion that has emerged based on the research of the past 30 years is that a hybrid style known as transformational leadership is highly effective in most contemporary organizational contexts.

      A transformational leader acts as an inspirational role model, motivates others to go beyond the confines of their job descriptions, encourages creativity and innovation, fosters good human relationships, and develops the skills of followers. This type of leadership is effective because it fosters strong interpersonal bonds based on a leader’s charisma and consideration of others. These bonds enable leaders to promote high-quality performance by encouraging workers rather than threatening them, thus motivating them to exceed basic expectations.

      By bringing out the best in others, transformational leaders enhance the performance of groups and organizations.

      Transformational leadership is androgynous because it incorporates culturally masculine and feminine behaviors. This androgynous mixing of the masculine and feminine means that skill in this contemporary way of leading does not necessarily come naturally. It may require some effort and thought.

      Men often have to work on their social skills and women on being assertive enough to inspire others. It is nonetheless clear that both women and men can adapt to the demands of leadership in the transformational mode.

      One of the surprises of research on transformational leadership is that female managers are somewhat more transformational than male managers. In particular, they exceed men in their attention to human relationships. Also, in delivering incentives, women lean toward a more positive, reward-based approach and men toward a more negative and less effective, threat-based approach. In these respects, women appear to be better leaders than men, despite the double standard that can close women out of these roles.

      Why are women leaders more transformational when they are less likely to become leaders in the first place? One reason is that the double standard that slows women’s rise would work against mediocre women while allowing mediocre men to rise. As a consequence, the women who attain leadership roles really are better than the men on average.

      It is also true women generally avoid more domineering, “command and control” behavior because of the backlash they receive if they lead in this way. Men can often get away with autocratic behavior that is roundly disliked in women. Ironically, this backlash against domineering women may foster good leadership because the androgynous middle ground is more likely to bring success. Leaders gain less from ordering others about than from forming teams of smart, motivated collaborators who together figure out how to solve problems and get work done.

From: http://www.nytimes.com/ 2013/03/20  

Among the factors that make transformational leadership effective, the text mentions
Alternativas
Ano: 2013 Banca: UECE-CEV Órgão: UECE Prova: UECE-CEV - 2013 - UECE - Vestibular - Segundo Semestre |
Q1260551 Inglês

TEXT


      Hundreds of studies have assessed leadership styles, mainly by having employees report on how their managers typically behave. Researchers have also collected information on how effective managers are. After large numbers of such studies became available, reviewers aggregated them quantitatively to discover what kinds of leadership are effective.

      One conclusion that has emerged based on the research of the past 30 years is that a hybrid style known as transformational leadership is highly effective in most contemporary organizational contexts.

      A transformational leader acts as an inspirational role model, motivates others to go beyond the confines of their job descriptions, encourages creativity and innovation, fosters good human relationships, and develops the skills of followers. This type of leadership is effective because it fosters strong interpersonal bonds based on a leader’s charisma and consideration of others. These bonds enable leaders to promote high-quality performance by encouraging workers rather than threatening them, thus motivating them to exceed basic expectations.

      By bringing out the best in others, transformational leaders enhance the performance of groups and organizations.

      Transformational leadership is androgynous because it incorporates culturally masculine and feminine behaviors. This androgynous mixing of the masculine and feminine means that skill in this contemporary way of leading does not necessarily come naturally. It may require some effort and thought.

      Men often have to work on their social skills and women on being assertive enough to inspire others. It is nonetheless clear that both women and men can adapt to the demands of leadership in the transformational mode.

      One of the surprises of research on transformational leadership is that female managers are somewhat more transformational than male managers. In particular, they exceed men in their attention to human relationships. Also, in delivering incentives, women lean toward a more positive, reward-based approach and men toward a more negative and less effective, threat-based approach. In these respects, women appear to be better leaders than men, despite the double standard that can close women out of these roles.

      Why are women leaders more transformational when they are less likely to become leaders in the first place? One reason is that the double standard that slows women’s rise would work against mediocre women while allowing mediocre men to rise. As a consequence, the women who attain leadership roles really are better than the men on average.

      It is also true women generally avoid more domineering, “command and control” behavior because of the backlash they receive if they lead in this way. Men can often get away with autocratic behavior that is roundly disliked in women. Ironically, this backlash against domineering women may foster good leadership because the androgynous middle ground is more likely to bring success. Leaders gain less from ordering others about than from forming teams of smart, motivated collaborators who together figure out how to solve problems and get work done.

From: http://www.nytimes.com/ 2013/03/20  

Further exploring the apparently paradoxical reasons why women leaders are more successful in transformational leadership than men, the text mentions the fact that
Alternativas
Ano: 2013 Banca: UECE-CEV Órgão: UECE Prova: UECE-CEV - 2013 - UECE - Vestibular - Segundo Semestre |
Q1260550 Inglês

TEXT


      Hundreds of studies have assessed leadership styles, mainly by having employees report on how their managers typically behave. Researchers have also collected information on how effective managers are. After large numbers of such studies became available, reviewers aggregated them quantitatively to discover what kinds of leadership are effective.

      One conclusion that has emerged based on the research of the past 30 years is that a hybrid style known as transformational leadership is highly effective in most contemporary organizational contexts.

      A transformational leader acts as an inspirational role model, motivates others to go beyond the confines of their job descriptions, encourages creativity and innovation, fosters good human relationships, and develops the skills of followers. This type of leadership is effective because it fosters strong interpersonal bonds based on a leader’s charisma and consideration of others. These bonds enable leaders to promote high-quality performance by encouraging workers rather than threatening them, thus motivating them to exceed basic expectations.

      By bringing out the best in others, transformational leaders enhance the performance of groups and organizations.

      Transformational leadership is androgynous because it incorporates culturally masculine and feminine behaviors. This androgynous mixing of the masculine and feminine means that skill in this contemporary way of leading does not necessarily come naturally. It may require some effort and thought.

      Men often have to work on their social skills and women on being assertive enough to inspire others. It is nonetheless clear that both women and men can adapt to the demands of leadership in the transformational mode.

      One of the surprises of research on transformational leadership is that female managers are somewhat more transformational than male managers. In particular, they exceed men in their attention to human relationships. Also, in delivering incentives, women lean toward a more positive, reward-based approach and men toward a more negative and less effective, threat-based approach. In these respects, women appear to be better leaders than men, despite the double standard that can close women out of these roles.

      Why are women leaders more transformational when they are less likely to become leaders in the first place? One reason is that the double standard that slows women’s rise would work against mediocre women while allowing mediocre men to rise. As a consequence, the women who attain leadership roles really are better than the men on average.

      It is also true women generally avoid more domineering, “command and control” behavior because of the backlash they receive if they lead in this way. Men can often get away with autocratic behavior that is roundly disliked in women. Ironically, this backlash against domineering women may foster good leadership because the androgynous middle ground is more likely to bring success. Leaders gain less from ordering others about than from forming teams of smart, motivated collaborators who together figure out how to solve problems and get work done.

From: http://www.nytimes.com/ 2013/03/20  

According to the research results, women tend to do better in terms of the application of the transformational type of leadership because of their
Alternativas
Ano: 2013 Banca: UECE-CEV Órgão: UECE Prova: UECE-CEV - 2013 - UECE - Vestibular - Segundo Semestre |
Q1260549 Inglês

TEXT


      Hundreds of studies have assessed leadership styles, mainly by having employees report on how their managers typically behave. Researchers have also collected information on how effective managers are. After large numbers of such studies became available, reviewers aggregated them quantitatively to discover what kinds of leadership are effective.

      One conclusion that has emerged based on the research of the past 30 years is that a hybrid style known as transformational leadership is highly effective in most contemporary organizational contexts.

      A transformational leader acts as an inspirational role model, motivates others to go beyond the confines of their job descriptions, encourages creativity and innovation, fosters good human relationships, and develops the skills of followers. This type of leadership is effective because it fosters strong interpersonal bonds based on a leader’s charisma and consideration of others. These bonds enable leaders to promote high-quality performance by encouraging workers rather than threatening them, thus motivating them to exceed basic expectations.

      By bringing out the best in others, transformational leaders enhance the performance of groups and organizations.

      Transformational leadership is androgynous because it incorporates culturally masculine and feminine behaviors. This androgynous mixing of the masculine and feminine means that skill in this contemporary way of leading does not necessarily come naturally. It may require some effort and thought.

      Men often have to work on their social skills and women on being assertive enough to inspire others. It is nonetheless clear that both women and men can adapt to the demands of leadership in the transformational mode.

      One of the surprises of research on transformational leadership is that female managers are somewhat more transformational than male managers. In particular, they exceed men in their attention to human relationships. Also, in delivering incentives, women lean toward a more positive, reward-based approach and men toward a more negative and less effective, threat-based approach. In these respects, women appear to be better leaders than men, despite the double standard that can close women out of these roles.

      Why are women leaders more transformational when they are less likely to become leaders in the first place? One reason is that the double standard that slows women’s rise would work against mediocre women while allowing mediocre men to rise. As a consequence, the women who attain leadership roles really are better than the men on average.

      It is also true women generally avoid more domineering, “command and control” behavior because of the backlash they receive if they lead in this way. Men can often get away with autocratic behavior that is roundly disliked in women. Ironically, this backlash against domineering women may foster good leadership because the androgynous middle ground is more likely to bring success. Leaders gain less from ordering others about than from forming teams of smart, motivated collaborators who together figure out how to solve problems and get work done.

From: http://www.nytimes.com/ 2013/03/20  

As to the leadership pattern that requires attitudes based on features of both male and female behaviors, one may infer that it

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Q524693 Inglês
Medical science has achieved great feats, improved and saved the lives of many. ...... when it comes to assisted reproductive technologies, science fails far more often than is generally believed.
A palavra que, no contexto acima, preenche corretamente a lacuna é:
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Q524689 Inglês
Atenção: A  questão refere-se ao texto apresentado abaixo.

The stories behind the black opera stars of 'I Live to Sing'
Washington Post − Saturday, August 24, 2013

Julie Cohen and Kamal Khan met in elementary school in Fairfax County about 40 years  . Today, Cohen, 49, is the Brooklyn-based founder of BetterThanFiction Productions, a documentary film company; Khan is the director of the University of Cape Town Opera School. “I Live to Sing," a feature-length documentary directed and produced by Cohen, focuses on three of Khan's black students who made their way from humble beginnings in often poverty-ridden townships to excel in opera ‒an art form most closely associated with white, elite audiences and performers.

How did you come to do this project?
It was just the fortuitous situation of knowing Kamal Khan. I met Kamal in third grade at Pine Ridge Elementary School in Fairfax County. He was unusual in that even at age 9 his prime interests seemed to be opera, classical music, Shakespeare. These are interests that when you're 40 and living in New York are not so strange! He became James Levine's assistant conductor at the Metropolitan Opera and he still now does a lot of conducting internationally, although his home base is at the University of Cape Town. In the meantime I started doing several documentaries about the human side behind the performing arts. Knowing what Kamal was up to I realized that his fascinating work − from an artistic, political and social context − was just the sort of thing I was interested in making films about.

Why is it interesting to you to document performing artists?
We're all so steeped in the relatively small circle of people who become really famous or really big deals. But it's also, I think, wonderful to see the work of and hear the life stories of the majority of performing artists who are toiling away, many of whom are supremely talented, but the world doesn't necessarily get to know.

Tell me about Linda's life, the young soprano featured in your film.
Linda Nteleza comes from a huge township adjacent to Cape Town that has a lot of problems − poverty, health-care issues, education issues, huge unemployment. I believe it has the fastest-growing rate of tuberculosis in the world, and Linda has suffered from the consequences of that. Linda learned to sing in school and then followed by her work in community choir, and through the teachers and coaches learned about University of Cape Town and its music program. She lived only a half-hour from the university but hadn't been aware that music was something that was out there. She was encouraged to go and apply. I think she didn't expect to get it, but to her joy andnamazement she did.
When Linda told her mother that “I want to go to college to study opera," her mother's immediate response was, “What's opera?" It wasn't that she wasn't well-versed in the art form; she didn't know what it was. Linda herself had first heard opera in a TV commercial for Shell Oil that had a beautiful soprano opera singer as background music and she was completely entranced, like, “That's what I want to sing."
Were you an opera fan before this?
[Laughs] I . . . must . . . confess that I was not only not an opera fan, but really almost actively probably disliked opera before this project. That's actually not something that I mentioned to Kamal when I pitched the idea of “Can I follow your program around? Can I bring cameras to your school?" [Laughs] . . . But as often when you delve into different art forms, particularly classical art forms that you are ignorant of, the more you get to know it, the  it starts to sound.

(Adapted from http://www.washingtonpost.com/lifestyle/style/qanda-the-stories-behind-the-black-opera-stars-of-i-live-to sing/2013/08/23)
A palavra que, no contexto, preenche adequadamente a  lacunaImagem associada para resolução da questão é:
Alternativas
Ano: 2013 Banca: SENAC-SP Órgão: SENAC-SP Prova: SENAC-SP - 2013 - SENAC-SP - Vestibular - Inglês |
Q524688 Inglês
Atenção: A  questão refere-se ao texto apresentado abaixo.

The stories behind the black opera stars of 'I Live to Sing'
Washington Post − Saturday, August 24, 2013

Julie Cohen and Kamal Khan met in elementary school in Fairfax County about 40 years  . Today, Cohen, 49, is the Brooklyn-based founder of BetterThanFiction Productions, a documentary film company; Khan is the director of the University of Cape Town Opera School. “I Live to Sing," a feature-length documentary directed and produced by Cohen, focuses on three of Khan's black students who made their way from humble beginnings in often poverty-ridden townships to excel in opera ‒an art form most closely associated with white, elite audiences and performers.

How did you come to do this project?
It was just the fortuitous situation of knowing Kamal Khan. I met Kamal in third grade at Pine Ridge Elementary School in Fairfax County. He was unusual in that even at age 9 his prime interests seemed to be opera, classical music, Shakespeare. These are interests that when you're 40 and living in New York are not so strange! He became James Levine's assistant conductor at the Metropolitan Opera and he still now does a lot of conducting internationally, although his home base is at the University of Cape Town. In the meantime I started doing several documentaries about the human side behind the performing arts. Knowing what Kamal was up to I realized that his fascinating work − from an artistic, political and social context − was just the sort of thing I was interested in making films about.

Why is it interesting to you to document performing artists?
We're all so steeped in the relatively small circle of people who become really famous or really big deals. But it's also, I think, wonderful to see the work of and hear the life stories of the majority of performing artists who are toiling away, many of whom are supremely talented, but the world doesn't necessarily get to know.

Tell me about Linda's life, the young soprano featured in your film.
Linda Nteleza comes from a huge township adjacent to Cape Town that has a lot of problems − poverty, health-care issues, education issues, huge unemployment. I believe it has the fastest-growing rate of tuberculosis in the world, and Linda has suffered from the consequences of that. Linda learned to sing in school and then followed by her work in community choir, and through the teachers and coaches learned about University of Cape Town and its music program. She lived only a half-hour from the university but hadn't been aware that music was something that was out there. She was encouraged to go and apply. I think she didn't expect to get it, but to her joy andnamazement she did.
When Linda told her mother that “I want to go to college to study opera," her mother's immediate response was, “What's opera?" It wasn't that she wasn't well-versed in the art form; she didn't know what it was. Linda herself had first heard opera in a TV commercial for Shell Oil that had a beautiful soprano opera singer as background music and she was completely entranced, like, “That's what I want to sing."
Were you an opera fan before this?
[Laughs] I . . . must . . . confess that I was not only not an opera fan, but really almost actively probably disliked opera before this project. That's actually not something that I mentioned to Kamal when I pitched the idea of “Can I follow your program around? Can I bring cameras to your school?" [Laughs] . . . But as often when you delve into different art forms, particularly classical art forms that you are ignorant of, the more you get to know it, the  it starts to sound.

(Adapted from http://www.washingtonpost.com/lifestyle/style/qanda-the-stories-behind-the-black-opera-stars-of-i-live-to sing/2013/08/23)
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Ano: 2013 Banca: FATEC Órgão: FATEC Prova: FATEC - 2013 - FATEC - Vestibular - Prova 01 |
Q382416 Inglês
Finally, a Billboard That Creates Drinkable Water Out of Thin Air

imagem-014.jpg

I’ve never cared much for billboards. Not in the city, not out of the city - not anywhere, really. It’s like the saying in that old Five Man Electrical Band1 song. So when the creative director of an ad agency in Peru sent me a picture of what he claimed was the frst billboard that produces potable water from air, my initial reaction was: gotta be a hoax, or at best, a gimmick2

Except it’s neither: the billboard pictured here is real, it’s located in Lima, Peru, and it produces around 100 liters of water a day (about 26 gallons) from nothing more than humidity, a basic fltration system and a little gravitational ingenuity3 .

Let’s talk about Lima for a moment, the largest city in Peru and the ffth largest in all of the Americas, with some 7.6 million people (closer to 9 million when you factor in the surrounding metro area). Because it sits along the southern Pacifc Ocean, the humidity in the city averages 83% (it’s actually closer to 100% in the mornings). But Lima is also part of what’s called a coastal desert: it lies at the northern edge of the Atacama, the driest desert in the world, meaning the city sees perhaps half an inch of precipitation annually (Lima is the second largest desert city in the world after Cairo). Lima thus depends on drainage from the Andes as well as runof from glacier melt - both sources on the decline because of climate change. (...)

1Five Man Electrical Band: nome de um grupo de rock canadense.

2
gimmick: algo que não é sério, usado para atrair a atenção das pessoas temporariamente, especialmente para fazê-las comprar algo.

3
ingenuity: habilidade de pensar em novos meios inteligentes de se fazer algo.


No terceiro parágrafo, o pronome it em – Because it sits along the southern Pacifc Ocean – pode ser substituído, de maneira a manter o sentido original do texto, por
Alternativas
Respostas
2241: C
2242: C
2243: D
2244: B
2245: D
2246: C
2247: A
2248: C
2249: B
2250: D
2251: B
2252: D
2253: B
2254: A
2255: C
2256: D
2257: E
2258: A
2259: C
2260: A