Questões de Inglês do ENEM

    "Checking Facebook should only take a minute." Those are the famous last words of countless people every day, right before getting sucked into several hours of watching cat videos or commenting on Instagrammed sushi lunches. That behavior is natural, given how the Internet is structured, experts say. The Internet’s omnipresence and lack of limits encourage people to lose track of time, making it hard to exercise the self-control to turn it off.

    "The Internet is not addictive in the same way as pharmacological substances are," said Tom Stafford, a cognitive scientist at the University of Sheffield in the U.K. "But it's compulsive; it's compelling; it's distracting." Humans are social creatures. Therefore, people enjoy the social information available via email and the Web.     

    The main reason the Internet is so addictive is that it lacks boundaries between tasks, Stafford said. Someone may set out to "research something, and then accidentally go to Wikipedia, and then wind up trying to find out what ever happened to Depeche Mode," Stafford said, referring to the music band. Studies suggest willpower is like a muscle: It can be strengthened, but can also become exhausted. Because the Internet is always "on," staying on task requires constantly flexing that willpower muscle, which can exhaust a person's self-control.

    For those who want to loosen the grip of the Web on their lives, a few simple techniques may do the trick. Web-blocking tools that limit surfing time can help people regain control over their time. Another method is to plan ahead, committing to work for 20 minutes, or until a certain task is complete, and then allowing five minutes of Web surfing, Stafford said.

(Adapted from: http://www.mnn.com/green-tech/computers/stories/why-the-internet-is-so-addictive) 

    "Checking Facebook should only take a minute." Those are the famous last words of countless people every day, right before getting sucked into several hours of watching cat videos or commenting on Instagrammed sushi lunches. That behavior is natural, given how the Internet is structured, experts say. The Internet’s omnipresence and lack of limits encourage people to lose track of time, making it hard to exercise the self-control to turn it off.

    "The Internet is not addictive in the same way as pharmacological substances are," said Tom Stafford, a cognitive scientist at the University of Sheffield in the U.K. "But it's compulsive; it's compelling; it's distracting." Humans are social creatures. Therefore, people enjoy the social information available via email and the Web.     

    The main reason the Internet is so addictive is that it lacks boundaries between tasks, Stafford said. Someone may set out to "research something, and then accidentally go to Wikipedia, and then wind up trying to find out what ever happened to Depeche Mode," Stafford said, referring to the music band. Studies suggest willpower is like a muscle: It can be strengthened, but can also become exhausted. Because the Internet is always "on," staying on task requires constantly flexing that willpower muscle, which can exhaust a person's self-control.

    For those who want to loosen the grip of the Web on their lives, a few simple techniques may do the trick. Web-blocking tools that limit surfing time can help people regain control over their time. Another method is to plan ahead, committing to work for 20 minutes, or until a certain task is complete, and then allowing five minutes of Web surfing, Stafford said.

(Adapted from: http://www.mnn.com/green-tech/computers/stories/why-the-internet-is-so-addictive) 

    "Checking Facebook should only take a minute." Those are the famous last words of countless people every day, right before getting sucked into several hours of watching cat videos or commenting on Instagrammed sushi lunches. That behavior is natural, given how the Internet is structured, experts say. The Internet’s omnipresence and lack of limits encourage people to lose track of time, making it hard to exercise the self-control to turn it off.

    "The Internet is not addictive in the same way as pharmacological substances are," said Tom Stafford, a cognitive scientist at the University of Sheffield in the U.K. "But it's compulsive; it's compelling; it's distracting." Humans are social creatures. Therefore, people enjoy the social information available via email and the Web.     

    The main reason the Internet is so addictive is that it lacks boundaries between tasks, Stafford said. Someone may set out to "research something, and then accidentally go to Wikipedia, and then wind up trying to find out what ever happened to Depeche Mode," Stafford said, referring to the music band. Studies suggest willpower is like a muscle: It can be strengthened, but can also become exhausted. Because the Internet is always "on," staying on task requires constantly flexing that willpower muscle, which can exhaust a person's self-control.

    For those who want to loosen the grip of the Web on their lives, a few simple techniques may do the trick. Web-blocking tools that limit surfing time can help people regain control over their time. Another method is to plan ahead, committing to work for 20 minutes, or until a certain task is complete, and then allowing five minutes of Web surfing, Stafford said.

(Adapted from: http://www.mnn.com/green-tech/computers/stories/why-the-internet-is-so-addictive) 

    "Checking Facebook should only take a minute." Those are the famous last words of countless people every day, right before getting sucked into several hours of watching cat videos or commenting on Instagrammed sushi lunches. That behavior is natural, given how the Internet is structured, experts say. The Internet’s omnipresence and lack of limits encourage people to lose track of time, making it hard to exercise the self-control to turn it off.

    "The Internet is not addictive in the same way as pharmacological substances are," said Tom Stafford, a cognitive scientist at the University of Sheffield in the U.K. "But it's compulsive; it's compelling; it's distracting." Humans are social creatures. Therefore, people enjoy the social information available via email and the Web.     

    The main reason the Internet is so addictive is that it lacks boundaries between tasks, Stafford said. Someone may set out to "research something, and then accidentally go to Wikipedia, and then wind up trying to find out what ever happened to Depeche Mode," Stafford said, referring to the music band. Studies suggest willpower is like a muscle: It can be strengthened, but can also become exhausted. Because the Internet is always "on," staying on task requires constantly flexing that willpower muscle, which can exhaust a person's self-control.

    For those who want to loosen the grip of the Web on their lives, a few simple techniques may do the trick. Web-blocking tools that limit surfing time can help people regain control over their time. Another method is to plan ahead, committing to work for 20 minutes, or until a certain task is complete, and then allowing five minutes of Web surfing, Stafford said.

(Adapted from: http://www.mnn.com/green-tech/computers/stories/why-the-internet-is-so-addictive) 

    London is a city made for walking. Unlike, for example, Los Angeles its centre is easily accessible on foot. Between 2001 and 2011 the number of trips made daily on foot in London increased by 12%. Each day 6.2m walks are made across the city.

   Several reasons account for the walking boom. The number of Londoners increased by 12% from 7.3m in 2001 to 8.2m in 2011, and Underground trains are hot and overcrowded. But other factors also encourage pedestrians. In 2004 Ken Livingstone, then mayor of London, promised to make London a “walkable city”. Some of his plans were carried on by Boris Johnson, the current mayor. These include a scheme to create clearly-marked maps for use across the city and make streets more pedestrian-friendly. Londoners may also be more aware of the advantages of walking. Health campaigns like the National Health Service’s “Live Well” emphasize that walking is the easiest form of exercise.

   High streets and town centres need to win back walkers. Learning from London’s incentives could be a start. 

The word could in “Learning from London’s incentives could be a start.” (paragraph 03) indicates

    London is a city made for walking. Unlike, for example, Los Angeles its centre is easily accessible on foot. Between 2001 and 2011 the number of trips made daily on foot in London increased by 12%. Each day 6.2m walks are made across the city.

   Several reasons account for the walking boom. The number of Londoners increased by 12% from 7.3m in 2001 to 8.2m in 2011, and Underground trains are hot and overcrowded. But other factors also encourage pedestrians. In 2004 Ken Livingstone, then mayor of London, promised to make London a “walkable city”. Some of his plans were carried on by Boris Johnson, the current mayor. These include a scheme to create clearly-marked maps for use across the city and make streets more pedestrian-friendly. Londoners may also be more aware of the advantages of walking. Health campaigns like the National Health Service’s “Live Well” emphasize that walking is the easiest form of exercise.

   High streets and town centres need to win back walkers. Learning from London’s incentives could be a start. 

According to the cartoon below, judge the following statements as true (T) or false (F).

Source: http://eoienglishclub.wikispaces.com/ENGLISH+B1/unit3

I. The girl’s sentence can be transcript as “See you. I'll text you later”.

II. Young people do not make difference between spoken and written speech.

III. The old man can’t realize young people play with the sound of the words.

IV. Young people text so much, they adequate their speech according to the facilities of keyboards.

V. Language changes from one generation to another.

Choose the CORRECT alternative.

 Read the text below to answer question.

By David Willey BBC News, Rome

     Pope Francis, the first ever pontiff from Latin America, has struck an unusual new tone. Now he is back at his desk in his modest Vatican quarters, he has some important decisions to make about the future governance of his Church. Normally the cardinals who run the Holy See are off on their long summer holidays at this time of year. But Pope Francis' seasons are not the same as those of his predecessors. In the Southern Hemisphere, where the bulk of his international flock now lives, it is winter. 

      Pope Francis plans to spend the month of August preparing for some radical changes in the future governance of his worldwide Church. Be prepared for some big surprises. His remarks on gay people are being seen as much less judgemental than his predecessor's position on the issue. Pope Benedict XVI signed a document in 2005 that said men with deep-rooted homosexual tendencies should not be priests. But Pope Francis said gay clergymen should be forgiven and their sins forgotten.

     "The Catechism of the Catholic Church explains this very well," Pope Francis said in a wide-ranging 80-minute long interview with Vatican journalists."It says they should not be marginalised because of this but that they must be integrated into society."But he condemned what he described as lobbying by gay people. "The problem is not having this orientation," he said. "We must be brothers. The problem is lobbying by this orientation, or lobbies of greedy people, political lobbies, Masonic lobbies, so many lobbies. This is the worse problem."

According to the text, why Pope Francis does not feel comfortable to judge gay people?

   

   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

   

   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

   

   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

   

   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

   

   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

   

   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

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