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  • Linguagens

    Inglês

    Entre 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
    Abrir questão de Inglês, UECE 2013
  • Linguagens

    Português

    Entre para guardar nos favoritos
         Jornais impressos são coisas palpáveis, concretas, estão materializados em papel. No papel está seu suporte físico. Do papel, assim como da tinta, podem-se examinar a idade e a autenticidade. Já em televisão, como em toda forma de mídia eletrônica, é cada vez mais difícil encontrar o suporte físico original da informação. A bem da verdade, na era digital, quando já não se tem mais sequer o negativo de uma fotografia, posto que as fotos passaram a ser produzidas em máquinas digitais, praticamente não há mais o suporte físico primeiro, original, do documento que depois será objeto de pesquisa histórica. São tamanhas as possibilidades de alteração da imagem digital que, anos depois, será difícil precisar se aquela imagem que se tem corresponde exatamente à cena que foi de fato fotografada. E não se terá um negativo original para que seja tirada a prova dos nove.

    Eugênio Bucci e Maria R. Kehl, Videologias: ensaios sobre televisão. São Paulo: Boitempo, 2004
    Examine os seguintes comentários sobre diferentes elementos linguísticos presentes no texto:

    I Na oração que inicia o texto, a palavra “concretas” é um adjetivo que amplia o significado de outro adjetivo.
    II No trecho “do documento que depois será objeto de pesquisa histórica”, a palavra “objeto” assume sentido abstrato.
    III A expressão “prova dos nove” deve ser entendida, no texto, em seu sentido literal.


    Está correto o que se afirma em
    Escolha uma alternativa para a questão 4e3cdb22-fd
    Abrir questão de Português, FGV 2015
  • Linguagens

    Inglês

    Entre 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
    Abrir questão de Inglês, UECE 2013
  • Linguagens

    Português

    Entre para guardar nos favoritos
         Jornais impressos são coisas palpáveis, concretas, estão materializados em papel. No papel está seu suporte físico. Do papel, assim como da tinta, podem-se examinar a idade e a autenticidade. Já em televisão, como em toda forma de mídia eletrônica, é cada vez mais difícil encontrar o suporte físico original da informação. A bem da verdade, na era digital, quando já não se tem mais sequer o negativo de uma fotografia, posto que as fotos passaram a ser produzidas em máquinas digitais, praticamente não há mais o suporte físico primeiro, original, do documento que depois será objeto de pesquisa histórica. São tamanhas as possibilidades de alteração da imagem digital que, anos depois, será difícil precisar se aquela imagem que se tem corresponde exatamente à cena que foi de fato fotografada. E não se terá um negativo original para que seja tirada a prova dos nove.

    Eugênio Bucci e Maria R. Kehl, Videologias: ensaios sobre televisão. São Paulo: Boitempo, 2004
    Os conectivos sublinhados nos trechos “posto que as fotos passaram a ser produzidas em máquinas digitais” e “que, anos depois, será difícil precisar”, tendo em vista as relações sintático-semânticas estabelecidas no texto, introduzem, respectivamente, orações que exprimem ideia de
    Escolha uma alternativa para a questão 4e3fc446-fd
    Abrir questão de Português, FGV 2015
  • Linguagens

    Inglês

    Entre 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
    Escolha uma alternativa para a questão 4e424e3a-af
    Abrir questão de Inglês, UECE 2013
  • Linguagens

    Português

    Entre para guardar nos favoritos

        Era no tempo do rei.

        Uma das quatro esquinas que formam as ruas do Ouvidor e da Quitanda, cortando-se mutuamente, chamava-se nesse tempo - O canto dos meirinhos -; e bem lhe assentava o nome, porque era aí o lugar de encontro favorito de todos os indivíduos dessa classe (que gozava então de não pequena consideração). Os meirinhos de hoje não são mais do que a sombra caricata dos meirinhos do tempo do rei; esses eram gente temível e temida, respeitável e respeitada; formavam um dos extremos da formidável cadeia judiciária que envolvia todo o Rio de Janeiro no tempo em que a demanda era entre nós um elemento de vida: o extremo oposto eram os desembargadores. Ora, os extremos se tocam, e estes, tocando-se, fechavam o círculo dentro do qual se passavam os terríveis combates das citações, provarás, razões principais e finais, e todos esses trejeitos judiciais que se chamava o processo.

        Daí sua influência moral.


    Manuel Antônio de Almeida, Memórias de um sargento de milícias.  

    Dentre as expressões do texto relativas à atividade própria dos meirinhos, citadas abaixo, a única que assume conotação pejorativa é
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    Abrir questão de Português, FGV 2015
  • Linguagens

    Inglês

    Entre 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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    Abrir questão de Inglês, UECE 2013
  • Linguagens

    Português

    Entre para guardar nos favoritos

        Era no tempo do rei.

        Uma das quatro esquinas que formam as ruas do Ouvidor e da Quitanda, cortando-se mutuamente, chamava-se nesse tempo - O canto dos meirinhos -; e bem lhe assentava o nome, porque era aí o lugar de encontro favorito de todos os indivíduos dessa classe (que gozava então de não pequena consideração). Os meirinhos de hoje não são mais do que a sombra caricata dos meirinhos do tempo do rei; esses eram gente temível e temida, respeitável e respeitada; formavam um dos extremos da formidável cadeia judiciária que envolvia todo o Rio de Janeiro no tempo em que a demanda era entre nós um elemento de vida: o extremo oposto eram os desembargadores. Ora, os extremos se tocam, e estes, tocando-se, fechavam o círculo dentro do qual se passavam os terríveis combates das citações, provarás, razões principais e finais, e todos esses trejeitos judiciais que se chamava o processo.

        Daí sua influência moral.


    Manuel Antônio de Almeida, Memórias de um sargento de milícias.  

    Já na frase de abertura das Memórias de um sargento de milícias – “Era no tempo do rei.” –, que remete ao mesmo tempo à abertura-padrão dos contos da carochinha e ao período joanino da história do Brasil, manifestam-se as duas modalidades do tempo presentes nessa obra: uma, de caráter lendário e intemporal e, outra, de caráter histórico bem determinado.
    O convívio dessas duas modalidades do tempo indica que, do ponto de vista dessa obra, a história brasileira caracterizou-se pela conjunção de
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    Abrir questão de Português, FGV 2015
  • Linguagens

    Inglês

    Entre 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 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
    Escolha uma alternativa para a questão 4e49edb9-af
    Abrir questão de Inglês, UECE 2013
  • Linguagens

    Inglês

    Entre 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 sentences “Many use data sets provided by businesses or government, and pass back their results.” and “Because data science is so new, universities are scrambling to define it…” contain, respectively, a
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        Era no tempo do rei.

        Uma das quatro esquinas que formam as ruas do Ouvidor e da Quitanda, cortando-se mutuamente, chamava-se nesse tempo - O canto dos meirinhos -; e bem lhe assentava o nome, porque era aí o lugar de encontro favorito de todos os indivíduos dessa classe (que gozava então de não pequena consideração). Os meirinhos de hoje não são mais do que a sombra caricata dos meirinhos do tempo do rei; esses eram gente temível e temida, respeitável e respeitada; formavam um dos extremos da formidável cadeia judiciária que envolvia todo o Rio de Janeiro no tempo em que a demanda era entre nós um elemento de vida: o extremo oposto eram os desembargadores. Ora, os extremos se tocam, e estes, tocando-se, fechavam o círculo dentro do qual se passavam os terríveis combates das citações, provarás, razões principais e finais, e todos esses trejeitos judiciais que se chamava o processo.

        Daí sua influência moral.


    Manuel Antônio de Almeida, Memórias de um sargento de milícias.  

    O convívio de um repertório narrativo de caráter mítico ou lendário e de referências diretas a coisas e pessoas reais, pertencentes à realidade histórica, presente nas Memórias de um sargento de milícias, caracteriza também, principalmente, a estrutura da obra
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    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 the sentences “…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 “This is a generation of kids that grew up with data science around them.”, one finds adjective clauses that are respectively
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        “Vais encontrar o mundo, disse-me meu pai, à porta do Ateneu. Coragem para a luta.” Bastante experimentei depois a verdade deste aviso, que me despia, num gesto, das ilusões de criança educada exoticamente na estufa de carinho que é o regime do amor doméstico; diferente do que se encontra fora, tão diferente, que parece o poema dos cuidados maternos um artifício sentimental, com a vantagem única de fazer mais sensível a criatura à impressão rude do primeiro ensinamento, têmpera brusca da vitalidade na influência de um novo clima rigoroso. Lembramo-nos, entretanto, com saudade hipócrita, dos felizes tempos; como se a mesma incerteza de hoje, sob outro aspecto, não nos houvesse perseguido outrora, e não viesse de longe a enfiada das decepções que nos ultrajam. 
        Eufemismo, os felizes tempos, eufemismo apenas, igual aos outros que nos alimentam, a saudade dos dias que correram como melhores. Bem considerando, a atualidade é a mesma em todas as datas.

    Raul Pompeia, O Ateneu.  
    Na frase “disse-me meu pai”, ocorre ordem indireta, uma vez que seu sujeito está posposto ao verbo. No texto, há outros exemplos desse tipo de inversão. Considere os seguintes trechos:

    I “que me despia, num gesto, das ilusões de criança educada exoticamente na estufa de carinho”;
    II “que parece o poema dos cuidados maternos um artifício sentimental”;
    III “e não viesse de longe a enfiada das decepções”.


    Configura também frase com sujeito posposto a que está citada em
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    Entre 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 sentences “In the fall, Columbia will offer new master’s and certificate programs heavy on data.” and “Data scientists are the magicians of the Big Data era.” contain, respectively, at least one
    Escolha uma alternativa para a questão 4e585fd8-af
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        “Vais encontrar o mundo, disse-me meu pai, à porta do Ateneu. Coragem para a luta.” Bastante experimentei depois a verdade deste aviso, que me despia, num gesto, das ilusões de criança educada exoticamente na estufa de carinho que é o regime do amor doméstico; diferente do que se encontra fora, tão diferente, que parece o poema dos cuidados maternos um artifício sentimental, com a vantagem única de fazer mais sensível a criatura à impressão rude do primeiro ensinamento, têmpera brusca da vitalidade na influência de um novo clima rigoroso. Lembramo-nos, entretanto, com saudade hipócrita, dos felizes tempos; como se a mesma incerteza de hoje, sob outro aspecto, não nos houvesse perseguido outrora, e não viesse de longe a enfiada das decepções que nos ultrajam. 
        Eufemismo, os felizes tempos, eufemismo apenas, igual aos outros que nos alimentam, a saudade dos dias que correram como melhores. Bem considerando, a atualidade é a mesma em todas as datas.

    Raul Pompeia, O Ateneu.  
    Costuma-se considerar que O Ateneu, de Raul Pompeia, embora tenha como subtítulo “Crônica de saudades”, pertence à modalidade literária conhecida como romance de formação, cujo modelo ou paradigma é a narrativa do percurso de aprendizagem de um jovem, no qual ele forma seu caráter, supera os problemas encontrados e prepara sua inserção harmônica na sociedade.
    Admitindo-se esse paradigma, a reflexão sobre o tempo apresentada pelo narrador na abertura do livro, aqui reproduzida, indica que O Ateneu irá se configurar como um romance de formação
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    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 sentence “She might, for example, analyze tax return data to develop algorithms that flag fraudulent filings.” contains
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    Texto para responder às questão

    A saúde em pedaços: os determinantes sociais da saúde (DSS)

        A redução da saúde à sua dimensão biológica se constitui em um dos maiores dilemas da área. Isso porque essa visão estreita fundamenta práticas de pouco alcance quando se trata de saúde coletiva, porquanto prioriza a assistência individual e curativa, constituindo-se em uma espiral em torno das doenças e que, exatamente por isso, ajuda a reproduzi-las. Porém, essa concepção, embora hegemônica, não existe sem ser tensionada.

        A Organização Mundial da Saúde (OMS), ainda na primeira metade do século XX, tentou destacar que saúde não é só a ausência de doença. Todavia, pouco explica o porquê disso, uma vez que, como diria Ana Lúcia Magela de Rezende, na sua “Dialética da Saúde”, cai na tautologia de definir a saúde como sendo o completo bem-estar físico, psíquico e social. Ora, dizer que saúde é bem-estar é o mesmo que dizer que seis é meia dúzia. O que é o bem-estar?

        Na formulação da OMS essa questão permanece vaga. O uso do termo completo junto a bem-estar torna o conceito ainda mais problemático, tendo em vista seu caráter absolutista e, logo, inalcançável nestes termos.

        Foi o campo da Saúde do Trabalhador e, posteriormente, com maior precisão, a Saúde Coletiva (com origens na Medicina Social Latino-Americana) que superaram as dicotomias entre saúde e doença, social e biológico, e individual e coletivo ao formularem a concepção de saúde enquanto processo. Considerando tal processualidade, nem estamos absolutamente doentes nem absolutamente sãos, mas em contínuo movimento entre essas condições. Saúde e doença são dois momentos de um mesmo processo, coexistem, uma explicando a existência da outra.

        O predomínio de uma ou de outra depende do recorte e/ou ângulo de análise em cada momento e contexto. Essa forma de entender a saúde rompe com o pragmatismo biologicista, mas sem negar que a dimensão biológica é parte relevante do processo saúde-doença.

        Possui o mérito (com autores como Berlinguer, Donnangelo, Laurell, Arouca, Tambellini, Breilh, Nogueira, entre outros) de demonstrar que, embora a saúde se manifeste individual e biologicamente, ela é fruto de um processo de determinação social. Processo esse que é histórico e dinâmico, uno mas heterogêneo. Na verdade, só pode ser processo por causa dessas características. Ele nem pode ser considerado estaticamente ou como algo imutável ou imune às transformações sociais, nem pode ser considerado como um conjunto de fragmentos ou fatores quase que autônomos uns dos outros ou, muito menos, como uma massa homogênea e amorfa.

    (Diego de Oliveira Souza. Doutor em Serviço Social/UERJ. Professor do PPGSSUFAL/Maceió e da graduação em Enfermagem/UFAL/Arapiraca. Disponível em:https://docs.wixstatic.com/ugd/15557d_eae93514d26e4 aecb5e50ab81243343f.pdf. Acesso em agosto de 2019. Adaptado.)
    A sugestão de substituição para o trecho selecionado que manteria a correção gramatical e o sentido original do texto pode ser observada em:
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        “Vais encontrar o mundo, disse-me meu pai, à porta do Ateneu. Coragem para a luta.” Bastante experimentei depois a verdade deste aviso, que me despia, num gesto, das ilusões de criança educada exoticamente na estufa de carinho que é o regime do amor doméstico; diferente do que se encontra fora, tão diferente, que parece o poema dos cuidados maternos um artifício sentimental, com a vantagem única de fazer mais sensível a criatura à impressão rude do primeiro ensinamento, têmpera brusca da vitalidade na influência de um novo clima rigoroso. Lembramo-nos, entretanto, com saudade hipócrita, dos felizes tempos; como se a mesma incerteza de hoje, sob outro aspecto, não nos houvesse perseguido outrora, e não viesse de longe a enfiada das decepções que nos ultrajam. 
        Eufemismo, os felizes tempos, eufemismo apenas, igual aos outros que nos alimentam, a saudade dos dias que correram como melhores. Bem considerando, a atualidade é a mesma em todas as datas.

    Raul Pompeia, O Ateneu.  
    No início do texto, o pai do narrador revela pensar que a experiência do mundo é centrada na luta. Consideradas as diferenças, também vê o mundo principalmente sob esse prisma a personagem
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    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 the sentences “We’re building these models that have impact on human life.” and “She discovered the possibilities while working as a corporate tax analyst at the Internal Revenue Service.” one finds, respectively, a/an
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    Imagem da questão de Português, USP 2011, Interpretação de Textos



    Ao comparar Jerônimo com uma crisálida, o narrador alude, em linguagem literária, a fenômenos do desenvolvimento da borboleta, por meio das seguintes expressões do texto:


    I. “transformação, lenta e profunda” (L. 5);


    II. “reviscerando” (L. 6);


    III. “alando” (L. 7);


    IV. “trabalho misterioso e surdo” (L. 7).


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