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    Slogans de protesto

       Samantha Pearson, colunista do prestigioso Financial Times, escreveu um artigo sobre a adoção de slogans publicitários pelos manifestantes que tomaram as ruas de diversas cidades brasileiras nos últimos meses. Segundo ela, a utilização dos temas "O gigante acordou", extraída da campanha do uísque Johnnie Walker, e do "Vem pra rua", retirado dos anúncios da Fiat, revelam sinais de consumismo excessivo e alienação política.
         Entendo a surpresa da moça. Relacionamos protestos com palavras de ordem contra o establishment e não a favor dele. No entanto, como um dos mais antigos historiadores da humanidade e, porque não dizer, na qualidade de o “pai da história”, devo esclarecer que a utilização de slogans de anunciantes em protestos e revoluções não é algo novo.
    [...]
        O lema “paz, pão e terra”, usado por Lenin para promover a primeira fase da revolução soviética, expressou com perfeição três demandas da época: a saída da Rússia da Primeira Guerra Mundial, comida para todos e a necessidade de uma reforma agrária radical. Esse simples e brilhante slogan não saiu da cabeça de nenhum propagandista marxista. Os bolcheviques o tomaram de uma campanha de um condomínio fechado que prometia paz, pão e terra aos compradores, pois, os imensos lotes oferecidos aos burgueses se encontravam longe do centro de Moscou e ao lado de um campo de trigo.
        O caso de Maio de 68 é o mais exemplar. Esse movimento talvez seja o mais profícuo em slogans. Centenas de frases de efeito foram gritadas e estampadas em muros e cartazes pelos manifestantes. Algumas delas seguem ainda hoje animando as mentes inquietas. O que pouca gente sabe é que muitos dos ditos da época foram sacados de campanhas publicitárias de produtos dos mais diversos setores da economia. "Decretado o estado de felicidade permanente." (Cerveja). “A Poesia está na rua.” (Perfume). “O sonho é realidade.” (Previdência privada). "Não mudem de empregadores, mudem o emprego da vida." (Trabalho autônomo). "A imaginação toma o poder." (Carro).
         Ao historiador, cabe analisar o passado e não fazer previsões, mas me arrisco a dizer que, num futuro próximo, manifestantes poderão se apropriar também das frases promocionais usadas pelo varejo. Já vejo cartazes estampando dizeres como “imperdível, redução de tarifa já”, “pelo fim da corrupção, nem que seja em 10x sem juros”, “queima total de estoque de parlamentares”. O ambiente é propício.

    KNIJNIK, Vitor. Blog do Heródoto. Carta Capital. São Paulo: Confiança, Ano XVIII, Nº 757, jul. 2013, p.19. (adaptado)
    De acordo com o tipo de discurso, predominantemente, utilizado pelo autor do texto, é CORRETO afirmar que
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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 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
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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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    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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    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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    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
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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
    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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    TEXTO II


    O texto II desta prova foi extraído do segundo capítulo da novela A indesejada aposentadoria, do escritor maranhense Josué Montello (*1917 — †2006). Contemporâneo dos escritores que fizeram o romance de 30, Montello enveredou por outro caminho: explorou a narrativa urbana. Escreveu uma das obras-primas da literatura brasileira: Os tambores de São Luís (1975). A novela A indesejada aposentadoria, de 1972, conta a história de Guihermino Pereira, um funcionário público, já nas vésperas de se aposentar. 


    Imagem da questão de Português, UECE 2013, Coesão e coerência

    Josué Montello. A indesejada aposentadoria.
    Capítulo II, p. 11-14. Texto adaptado. 

    Atente aos seguintes trechos e ao que é dito a respeito da atitude revelada pelo enunciador sobre o conteúdo de seu próprio enunciado.


    I. “De sua espécie talvez tenha sido o derradeiro exemplar conhecido” (linhas 55- 56) — O conteúdo do enunciado não foi totalmente assumido pelo enunciador.

    II. “era mesmo o oposto da personagem de Cervantes” (linhas 69-70) — O conteúdo do enunciado foi só relativamente assumido pelo enunciador.

    III. “Não há exagero em afirmar-se que a sua casa de subúrbio [...] era, para ele, o lugar onde aguardava que a repartição voltasse a abrir” (linhas 106-110) — O enunciador assume totalmente a responsabilidade sobre seu discurso.


    Está correto o que se diz em 

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    TEXTO II


    O texto II desta prova foi extraído do segundo capítulo da novela A indesejada aposentadoria, do escritor maranhense Josué Montello (*1917 — †2006). Contemporâneo dos escritores que fizeram o romance de 30, Montello enveredou por outro caminho: explorou a narrativa urbana. Escreveu uma das obras-primas da literatura brasileira: Os tambores de São Luís (1975). A novela A indesejada aposentadoria, de 1972, conta a história de Guihermino Pereira, um funcionário público, já nas vésperas de se aposentar. 


    Imagem da questão de Português, UECE 2013, Interpretação de Textos

    Josué Montello. A indesejada aposentadoria.
    Capítulo II, p. 11-14. Texto adaptado. 

    Considere o que se diz sobre o segundo parágrafo (linhas 66–74).


    I. A semelhança entre Guilhermino e Dom Quixote era somente física. Psicologicamente um era o avesso do outro.

    II. Ao coordenar “poste” e “Dom Quixote” — “Era alto, magro, rosto comprido, com um pouco de poste e outro tanto de Dom Quixote.”—, o enunciador provoca a quebra do paralelismo semântico, que surpreende o leitor e tem efeito textual.

    III. A aproximação de “poste” e “Quixote” pode dar, ao enunciado, inclusive pela presença do eco: oste – ote, um tom humorístico.


    Está correto o que se diz em

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    TEXTO I

    O texto I desta prova é um excerto da parte 2, capítulo III, da obra Bandeirantes e pioneiros: paralelo entre duas culturas, de Vianna Moog — gaúcho de São Leopoldo (*1906 — †1988). Nesse capítulo, Moog faz um estudo comparativo entre a colonização dos EUA e a do Brasil, um paralelo entre as fundações da América inglesa e da América portuguesa. 


    Imagem da questão de Português, UECE 2013, Coesão e coerência

    Vianna Moog. Bandeirantes e pioneiros: Paralelo entre duas culturas. Capítulo III: Conquista e colonização. p. 103-104. Texto adaptado.

    Observe o trecho seguinte e o que se diz dele, em uma perspectiva estilística: “para eles só havia um modo de ser agradável a Deus: ler a Bíblia e trabalhar, trabalhar e prosperar, prosperar e acumular riquezas” (linhas 23-26).


    I. Há uma gradação ascendente, formada por três grupos de dois verbos, repetindo-se o último elemento de cada grupo no início do grupo seguinte. Essa gradação ilustra as várias etapas de trabalho por que passaram os colonizadores americanos até ficarem ricos. Não foi uma prosperidade fácil.

    II. Os verbos que formam a gradação são verbos de ação, o que pode servir para ilustrar a vida laboriosa dos colonizadores das terras do norte.

    III. A repetição dos verbos marca, de certa forma, o ritmo do excerto, causando a sensação do esforço empreendido pelos colonizadores para formar a nova pátria e para prosperar.


    Está correto o que se afirma em

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    TEXTO I

    O texto I desta prova é um excerto da parte 2, capítulo III, da obra Bandeirantes e pioneiros: paralelo entre duas culturas, de Vianna Moog — gaúcho de São Leopoldo (*1906 — †1988). Nesse capítulo, Moog faz um estudo comparativo entre a colonização dos EUA e a do Brasil, um paralelo entre as fundações da América inglesa e da América portuguesa. 


    Imagem da questão de Português, UECE 2013, Adjetivos

    Vianna Moog. Bandeirantes e pioneiros: Paralelo entre duas culturas. Capítulo III: Conquista e colonização. p. 103-104. Texto adaptado.

    Considere a seguinte passagem do texto: “em busca de minas de ouro e de prata e de riqueza fácil” (linhas 11-13) e o que se diz dela.


    I. O segundo “e” do excerto não tem justificativa gramatical, mas estilística.

    II. Enquanto o primeiro “e” coordena a expressão ”de minas de ouro” com a expressão “de (minas) de prata”, o segundo “e” coordena a expressão “de minas de ouro e de (minas) de prata” com a expressão “de riqueza fácil”.

    III. As três locuções adjetivas — “de minas de ouro”, “de (minas) de prata” e “de riqueza fácil” — completam o sentido do substantivo “busca”.


    Está correto o que se diz apenas em

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    TEXTO I

    O texto I desta prova é um excerto da parte 2, capítulo III, da obra Bandeirantes e pioneiros: paralelo entre duas culturas, de Vianna Moog — gaúcho de São Leopoldo (*1906 — †1988). Nesse capítulo, Moog faz um estudo comparativo entre a colonização dos EUA e a do Brasil, um paralelo entre as fundações da América inglesa e da América portuguesa. 


    Imagem da questão de Português, UECE 2013, Interpretação de Textos

    Vianna Moog. Bandeirantes e pioneiros: Paralelo entre duas culturas. Capítulo III: Conquista e colonização. p. 103-104. Texto adaptado.

    Considerando o ponto de vista do enunciador, escreva V se a afirmação for verdadeira e F, se for falsa. As afirmações podem estar explícitas, no texto, ou resultar de inferências.


    ( ) Os povoadores dos EUA tinham o propósito de ficar na nova terra, enquanto os do Brasil tinham o objetivo de enriquecer e voltar para Portugal.

    ( ) Os propósitos iniciais dos dois povos tiveram consequências decisivas na construção das duas nações.

    ( ) Os colonizadores estão para os conquistadores assim como os pioneiros estão para os bandeirantes.

    ( ) Era próprio dos conquistadores o espírito construtivo, enquanto o espírito predatório era próprio dos colonizadores.

    ( ) Os valores próprios do Calvinismo foram decisivos para o tipo de desenvolvimento das colônias norte-americanas.


    Está correta a seguinte sequência de cima para baixo:

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    TEXTO I

    O texto I desta prova é um excerto da parte 2, capítulo III, da obra Bandeirantes e pioneiros: paralelo entre duas culturas, de Vianna Moog — gaúcho de São Leopoldo (*1906 — †1988). Nesse capítulo, Moog faz um estudo comparativo entre a colonização dos EUA e a do Brasil, um paralelo entre as fundações da América inglesa e da América portuguesa. 


    Imagem da questão de Português, UECE 2013, Interpretação de Textos

    Vianna Moog. Bandeirantes e pioneiros: Paralelo entre duas culturas. Capítulo III: Conquista e colonização. p. 103-104. Texto adaptado.

    Assinale a opção que aponta, de acordo com o excerto transcrito, o fator que, para o enunciador, é responsável por muitas das diferenças entre os EUA e o Brasil.
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    Texto 1


    Sobre o estupro coletivo de uma estudante de 23 anos em Nova Déli, o advogado que defende os suspeitos declarou: “Até o momento eu não vi um único exemplo de estupro de uma mulher respeitável”. Sobre esta declaração, o advogado garantiu que não tentou difamar a vítima. “Eu só disse que as mulheres são respeitadas na Índia, sejam mães, irmãs, amigas, mas diga-me que país respeita uma prostituta?!”


    (Advogado de acusados de estupro na Índia denuncia confissão forçada.

    http://noticias.uol.com.br. Adaptado.)



    Texto 2


    Na Índia, a violência contra as mulheres tomou uma nova e mais perversa forma, a partir do cruzamento de duas linhas: as estruturas patriarcais tradicionais e as estruturas capitalistas emergentes. Precisamos pensar nas relações entre a violência do sistema econômico e a violência contra as mulheres.


    (Vandana Shiva, filósofa indiana. No continuum da violência.

    O Estado de S.Paulo, 12.01.2013. Adaptado.)



    Os textos referem-se ao fato ocorrido na Índia em dezembro de 2012. Pela leitura atenta dos textos, podemos afirmar que:

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    Instrução: Leia a tira para responder à questão.


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

    (www.hagardunor.net)


    O trecho do segundo quadrinho – I’m going to – introduz uma
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    Instrução: Leia a tira para responder à questão.


    Imagem da questão de Inglês, UNESP 2013, Aspectos linguísticos | Linguistic aspects

    (www.hagardunor.net)


    No trecho do primeiro quadrinho – she’s sick and tired of smelling beer –, ’s pode ser reescrito como
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    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
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    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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    Instrução: A questão toma por base uma passagem de um livro de José Ribeiro sobre o folclore nacional.



    Curupira


           Na teogonia* tupi, o anhangá, gênio andante, espírito andejo ou vagabundo, destinava-se a proteger a caça do campo. Era imaginado, segundo a tradição colhida pelo Dr. Couto de Magalhães, sob a figura de um veado branco, com olhos de fogo.
          Todo aquele que perseguisse um animal que estivesse amamentando corria o risco de ver Anhangá e a visão determinava logo a febre e, às vezes, a loucura. O caapora é o mesmo tipo mítico encontrado nas regiões central e meridional e aí representado por um homem enorme coberto de pelos negros por todo o rosto e por todo o corpo, ao qual se confiou a proteção da caça do mato. Tristonho e taciturno, anda sempre montado em um porco de grandes dimensões, dando de quando em vez um grito para impelir a vara. Quem o encontra adquire logo a certeza de ficar infeliz e de ser mal sucedido em tudo que intentar. Dele se originaram as expressões portuguesas caipora e caiporismo, como sinônimo de má sorte, infelicidade, desdita nos negócios. Bilac assim o descreve: “Companheiro do curupira, ou sua duplicata, é o Caapora, ora gigante, ora anão, montado num caititu, e cavalgando à frente de varas de porcos do mato, fumando cachimbo ou cigarro, pedindo fogo aos viajores; à frente dele voam os vaga-lumes, seus batedores, alumiando o caminho”.
         Ambos representam um só mito com diferente configuração e a mesma identidade com o curupira e o jurupari, numes que guardam a floresta. Todos convergem mais ou menos para o mesmo fim, sendo que o curupira é representado na região setentrional por um “pequeno tapuio” com os pés voltados para trás e sem os orifícios necessários para as secreções indispensáveis à vida, pelo que a gente do Pará diz que ele é músico. O Curupira ou Currupira, como é chamado no sul, aliás erroneamente, figura em uma infinidade de lendas tanto no norte como no sul do Brasil. No Pará, quando se viaja pelos rios e se ouve alguma pancada longínqua no meio dos bosques, “os romeiros dizem que é o Curupira que está batendo nas sapupemas, a ver se as árvores estão suficientemente fortes para sofrerem a ação de alguma tempestade que está próxima. A função do Curupira é proteger as florestas. Todo aquele que derriba, ou por qualquer modo estraga inutilmente as árvores, é punido por ele com a pena de errar tempos imensos pelos bosques, sem poder atinar com o caminho de casa, ou meio algum de chegar até os seus”. Como se vê, qualquer desses tipos é a manifestação de um só mito em regiões e circunstâncias diferentes.

    (O Brasil no folclore, 1970.)



    (*) Teogonia, s.f.: 1. Filos. Doutrina mística relativa ao nascimento dos deuses, e que frequentemente se relaciona com a formação do mundo. 2. Conjunto de divindades cujo culto forma o sistema religioso dum povo politeísta. (Dicionário Aurélio Eletrônico – Século XXI.)
    Todo aquele que perseguisse um animal que estivesse amamentando corria o risco de ver Anhangá [...].


    Se a frase apresentada for reescrita trocando-se perseguisse, que está no pretérito imperfeito do modo subjuntivo, por perseguir, futuro do mesmo modo, as formas estivesse e corria assumirão, por correlação de modos e tempos, as seguintes flexões:
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    Instrução: A questão toma por base um fragmento de Glória moribunda, do poeta romântico brasileiro Álvares de Azevedo (1831-1852).



    É uma visão medonha uma caveira?

    Não tremas de pavor, ergue-a do lodo.

    Foi a cabeça ardente de um poeta,

    Outrora à sombra dos cabelos loiros.

    Quando o reflexo do viver fogoso

    Ali dentro animava o pensamento,

    Esta fronte era bela. Aqui nas faces

    Formosa palidez cobria o rosto;

    Nessas órbitas — ocas, denegridas! —

    Como era puro seu olhar sombrio!



    Agora tudo é cinza. Resta apenas

    A caveira que a alma em si guardava,

    Como a concha no mar encerra a pérola,

    Como a caçoula a mirra incandescente.



    Tu outrora talvez desses-lhe um beijo;

    Por que repugnas levantá-la agora?

    Olha-a comigo! Que espaçosa fronte!

    Quanta vida ali dentro fermentava,

    Como a seiva nos ramos do arvoredo!

    E a sede em fogo das ideias vivas

    Onde está? onde foi? Essa alma errante

    Que um dia no viver passou cantando,

    Como canta na treva um vagabundo,

    Perdeu-se acaso no sombrio vento,

    Como noturna lâmpada apagou-se?

    E a centelha da vida, o eletrismo

    Que as fibras tremulantes agitava

    Morreu para animar futuras vidas?



    Sorris? eu sou um louco. As utopias,

    Os sonhos da ciência nada valem.

    A vida é um escárnio sem sentido,

    Comédia infame que ensanguenta o lodo.

    Há talvez um segredo que ela esconde;

    Mas esse a morte o sabe e o não revela.

    Os túmulos são mudos como o vácuo.

    Desde a primeira dor sobre um cadáver,

    Quando a primeira mãe entre soluços

    Do filho morto os membros apertava

    Ao ofegante seio, o peito humano

    Caiu tremendo interrogando o túmulo...

    E a terra sepulcral não respondia.



                        (Poesias completas, 1962.)

    E a centelha da vida, o eletrismo


    No contexto em que é empregado, o termo eletrismo, que não consta dos dicionários, significa:
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