English II End Term: 3 April 2022 (January 2022 term)

Read the following passage and answer the given subquestions.
The term “data science” has attracted a lot of attention. Much of this attention is in business, in government, and in the academic areas of statistics and computer science. Here, we discuss data science from the perspective of scientific research. What is data science? Why might scientists care about it?
Our perspective is that data science is the child of statistics and computer science. While it has inherited some of their methods and thinking, it also seeks to blend them, refocus them, and develop them to address the context and needs of modern scientific data analysis. This perspective is not new. Over 50 years ago, Tukey defined “data analysis” as a broad endeavour, much broader than traditional mathematical statistics. In a sense, today’s data science, although set against a modern backdrop, is cast from Tukey’s original mould.
In modern research, scientists from diverse disciplines are confronting abundant datasets and are confident that there is value in the data for advancing their scientific goals. We give three examples at genomic, social, and galactic scales. First, modern sequencing technology has enabled high-resolution genetic sequencing at massive scale, and geneticists have connected the genetic data to large databases of individuals’ behaviours and diseases. These data can potentially aid researchers in studying the human genome, helping them understand how it evolves, and how it governs observed traits. Second, social scientists now have the opportunity to study large archives of digitised texts, often with rich information about human behaviour and interactions. These data could help them more effectively navigate and understand the contours of society, finding relevant sources to their work and identifying hard to spot patterns of language that suggest new interpretations and theories. Third, modern telescopes create digital sky surveys that have transformed observational astronomy, generating hundreds of terabytes of raw image data about billions of sky objects. A catalogue of these objects, if available, would give astronomers an unprecedented window into the structure of the cosmos.
Reference:
Blei, David M., and Padhraic Smyth. ‘Science and Data Science’. Proceedings of the National Academy of Sciences 114, no. 33 (15 August 2017): 8689.

Which of the following statements best summarises the gist of this passage?

Read the following passage and answer the given subquestions.
The term “data science” has attracted a lot of attention. Much of this attention is in business, in government, and in the academic areas of statistics and computer science. Here, we discuss data science from the perspective of scientific research. What is data science? Why might scientists care about it?
Our perspective is that data science is the child of statistics and computer science. While it has inherited some of their methods and thinking, it also seeks to blend them, refocus them, and develop them to address the context and needs of modern scientific data analysis. This perspective is not new. Over 50 years ago, Tukey defined “data analysis” as a broad endeavour, much broader than traditional mathematical statistics. In a sense, today’s data science, although set against a modern backdrop, is cast from Tukey’s original mould.
In modern research, scientists from diverse disciplines are confronting abundant datasets and are confident that there is value in the data for advancing their scientific goals. We give three examples at genomic, social, and galactic scales. First, modern sequencing technology has enabled high-resolution genetic sequencing at massive scale, and geneticists have connected the genetic data to large databases of individuals’ behaviours and diseases. These data can potentially aid researchers in studying the human genome, helping them understand how it evolves, and how it governs observed traits. Second, social scientists now have the opportunity to study large archives of digitised texts, often with rich information about human behaviour and interactions. These data could help them more effectively navigate and understand the contours of society, finding relevant sources to their work and identifying hard to spot patterns of language that suggest new interpretations and theories. Third, modern telescopes create digital sky surveys that have transformed observational astronomy, generating hundreds of terabytes of raw image data about billions of sky objects. A catalogue of these objects, if available, would give astronomers an unprecedented window into the structure of the cosmos.
Reference:
Blei, David M., and Padhraic Smyth. ‘Science and Data Science’. Proceedings of the National Academy of Sciences 114, no. 33 (15 August 2017): 8689.

How did Tukey define “data analysis”?

Read the following passage and answer the given subquestions.
The term “data science” has attracted a lot of attention. Much of this attention is in business, in government, and in the academic areas of statistics and computer science. Here, we discuss data science from the perspective of scientific research. What is data science? Why might scientists care about it?
Our perspective is that data science is the child of statistics and computer science. While it has inherited some of their methods and thinking, it also seeks to blend them, refocus them, and develop them to address the context and needs of modern scientific data analysis. This perspective is not new. Over 50 years ago, Tukey defined “data analysis” as a broad endeavour, much broader than traditional mathematical statistics. In a sense, today’s data science, although set against a modern backdrop, is cast from Tukey’s original mould.
In modern research, scientists from diverse disciplines are confronting abundant datasets and are confident that there is value in the data for advancing their scientific goals. We give three examples at genomic, social, and galactic scales. First, modern sequencing technology has enabled high-resolution genetic sequencing at massive scale, and geneticists have connected the genetic data to large databases of individuals’ behaviours and diseases. These data can potentially aid researchers in studying the human genome, helping them understand how it evolves, and how it governs observed traits. Second, social scientists now have the opportunity to study large archives of digitised texts, often with rich information about human behaviour and interactions. These data could help them more effectively navigate and understand the contours of society, finding relevant sources to their work and identifying hard to spot patterns of language that suggest new interpretations and theories. Third, modern telescopes create digital sky surveys that have transformed observational astronomy, generating hundreds of terabytes of raw image data about billions of sky objects. A catalogue of these objects, if available, would give astronomers an unprecedented window into the structure of the cosmos.
Reference:
Blei, David M., and Padhraic Smyth. ‘Science and Data Science’. Proceedings of the National Academy of Sciences 114, no. 33 (15 August 2017): 8689.

Which of the following statements is/are true about today’s data science?