Questões de Inglês do ENEM

Questões de Inglês, da área de Linguagens, com gabarito em cada página.

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5.311 questões encontradas. Mostrando a página 196 de 266.

  • 4E465728-AF

    Inglês

    Passado perfeito progressivo | Past perfect continuous
    UECE · 2013MédioEntre para guardar nos favoritos
    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
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  • 4E424E3A-AF

    Inglês

    Adjetivos | Adjectives
    UECE · 2013DifícilEntre para guardar nos favoritos
    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
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  • 4E3ED6ED-AF

    Inglês

    Interpretação de texto | Reading comprehension
    UECE · 2013MédioEntre para guardar nos favoritos
    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
    Escolha uma alternativa para a questão 4e3ed6ed-af
  • 4E3AA488-AF

    Inglês

    Interpretação de texto | Reading comprehension
    UECE · 2013DifícilEntre para guardar nos favoritos
    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
    Escolha uma alternativa para a questão 4e3aa488-af
  • 4E367754-AF

    Inglês

    Interpretação de texto | Reading comprehension
    UECE · 2013DifícilEntre para guardar nos favoritos
    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
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  • 4E332760-AF

    Inglês

    Interpretação de texto | Reading comprehension
    UECE · 2013MédioEntre para guardar nos favoritos
    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
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  • 4E2F0772-AF

    Inglês

    Interpretação de texto | Reading comprehension
    UECE · 2013DifícilEntre para guardar nos favoritos
    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
    Escolha uma alternativa para a questão 4e2f0772-af
  • 1AB85904-AF

    Inglês

    Interpretação de texto | Reading comprehension
    UECE · 2013DifícilEntre para guardar nos favoritos

    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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  • 1AB5550E-AF

    Inglês

    Interpretação de texto | Reading comprehension
    UECE · 2013Muito difícilEntre para guardar nos favoritos

    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
    Escolha uma alternativa para a questão 1ab5550e-af
  • 1AB18C60-AF

    Inglês

    Interpretação de texto | Reading comprehension
    UECE · 2013DifícilEntre para guardar nos favoritos

    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
    Escolha uma alternativa para a questão 1ab18c60-af
  • 1AAD3F7D-AF

    Inglês

    Interpretação de texto | Reading comprehension
    UECE · 2013Muito difícilEntre para guardar nos favoritos

    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
    Escolha uma alternativa para a questão 1aad3f7d-af
  • 1AA96CF3-AF

    Inglês

    Interpretação de texto | Reading comprehension
    UECE · 2013DifícilEntre para guardar nos favoritos

    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
    Escolha uma alternativa para a questão 1aa96cf3-af
  • 1AA5652B-AF

    Inglês

    Interpretação de texto | Reading comprehension
    UECE · 2013Muito difícilEntre para guardar nos favoritos

    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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  • 91EA8C91-AF

    Inglês

    Interpretação de texto | Reading comprehension
    UNESP · 2013FácilEntre para guardar nos favoritos

    Brazil wants to count trees in the Amazon rainforest


    By Channtal Fleischfresser

    February 11, 2013



    Imagem da questão de Inglês, UNESP 2013, Interpretação de texto | Reading comprehension

    Photo: Flickr/Nico Crisafulli



              Brazil is home to roughly 60 percent of the Amazon, about half of what remains of the world’s tropical rainforests. And now, the country has plans to count its trees. A vast undertaking, the new National Forest Inventory hopes to gain “a broad panorama of the quality and the conditions in the forest cover”, according to Brazil’s Forestry Minister Antonio Carlos Hummel.

           The census, set to take place over the next four years, will scour 3,288,000 square miles, sampling 20,000 points at 20 kilometer intervals and registering the number, height, diameter, and species of the trees, among other data.

             The initiative, aimed to better allocate resources to the country’s forests, is part of a large-scale turnaround in Brazil’s relationship to its forests. While it once had one of the worst rates of deforestation in the world, last year only 1,797 square miles of the Amazon were destroyed – a reduction of nearly 80% compared to 2004.



    (www.smartplanet.com. Adaptado.)

    O objetivo do Censo Florestal é
    Escolha uma alternativa para a questão 91ea8c91-af
  • 91E1719D-AF

    Inglês

    Interpretação de texto | Reading comprehension
    UNESP · 2013FácilEntre para guardar nos favoritos

    Brazil wants to count trees in the Amazon rainforest


    By Channtal Fleischfresser

    February 11, 2013



    Imagem da questão de Inglês, UNESP 2013, Interpretação de texto | Reading comprehension

    Photo: Flickr/Nico Crisafulli



              Brazil is home to roughly 60 percent of the Amazon, about half of what remains of the world’s tropical rainforests. And now, the country has plans to count its trees. A vast undertaking, the new National Forest Inventory hopes to gain “a broad panorama of the quality and the conditions in the forest cover”, according to Brazil’s Forestry Minister Antonio Carlos Hummel.

           The census, set to take place over the next four years, will scour 3,288,000 square miles, sampling 20,000 points at 20 kilometer intervals and registering the number, height, diameter, and species of the trees, among other data.

             The initiative, aimed to better allocate resources to the country’s forests, is part of a large-scale turnaround in Brazil’s relationship to its forests. While it once had one of the worst rates of deforestation in the world, last year only 1,797 square miles of the Amazon were destroyed – a reduction of nearly 80% compared to 2004.



    (www.smartplanet.com. Adaptado.)

    O Governo brasileiro
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  • 91D4F3DA-AF

    Inglês

    Interpretação de texto | Reading comprehension
    UNESP · 2013MédioEntre para guardar nos favoritos

    Instrução: Leia a tira para responder à questão.


    Imagem da questão de Inglês, UNESP 2013, Interpretação de texto | Reading comprehension

    (www.hagardunor.net)


    A expressão sick and tired no primeiro quadrinho tem sentido equivalente, em português, a
    Escolha uma alternativa para a questão 91d4f3da-af
  • 91D0785A-AF

    Inglês

    Interpretação de texto | Reading comprehension
    UNESP · 2013MédioEntre para guardar nos favoritos

    Instrução: Leia a tira para responder à questão.


    Imagem da questão de Inglês, UNESP 2013, Interpretação de texto | Reading comprehension

    (www.hagardunor.net)


    A personagem de barba, Hagar,
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  • 0ED6ED8F-4E

    Inglês

    Interpretação de texto | Reading comprehension
    ENEM · 2013MédioEntre para guardar nos favoritos

    Movie: Hijras - The Third Gender


    Director: Devika Urvashi Bhisé

    Duration: 29 minutes

    Hijras are the outcastes of Indian society and live on its fringes. These eunuchs (originally only castrated males) were once employed by sultans and maharajas to guard the women in their harems. Now shunned by society, they are treated with less respect than the Dalits, or untouchables. Considered neither men nor women, Hijras have no constitutional rights. Currently, there is an ongoing debate in India regarding whether or not they should be granted the status of a third gender.

    Most hijras are genetically born as men, but believe they are women within. The rest are hermaphrodites with some abnormality in genitalia. For those born men, becoming a hijra is a painful process that involves removing the entire genitalia in a secret ceremony that is often undergone without any anesthetic.

    Currently, most hijras have only three ways in which they can make a living: prostitution, begging, and as performing shamans removing bad luck and/or spells from suspicious Indian households. Sex work is one of the only options for hijras because there are few employment opportunities available to them. Hijras are most commonly seen knocking on car windows, begging for money at stoplights. Although hijras are feared for their dissimilarities, they are also revered for their alleged mystical abilities. Most Indian families seek their blessings during any auspicious ceremony such as a birth, a wedding, or the building of a new house.

    As pariahs of society, they are subjected to prejudice that is often translated into verbal abuse, humiliation, extreme discrimination, and violence in public as well as private venues. I have documented a short film to create awareness of the plight of this segment of society and allow their voices to be heard. I was privileged to share this community's inner life and have tried to capture its stark reality as a friend rather than a voyeur. The filming took place from June 2008 to September 2008 in various cities and locations in India.

    Disponível em: www.engendered.org. Acesso em: 25 fev. 2012.


    O filme Hijras - The Third Gender tem como objetivo chamar atenção para a situação vivida por um segmento da sociedade indiana, os hijras. De acordo com o que se captura dessas vozes no filme e do que se lê no texto, esse segmento reivindica

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