摘要 :
The advent of modern technologies has led to the expansion of the use of databases and data services. This phenomenon has led to the rise of Data Science, and data scientists have become professionals with a particular importance ...
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The advent of modern technologies has led to the expansion of the use of databases and data services. This phenomenon has led to the rise of Data Science, and data scientists have become professionals with a particular importance in terms of extracting new knowledge from data. Achieving a comprehensive competence for learning through “big data” is a task of re-engineering the existing way of learning. It is appropriate to understand what knowledge and skill base data scientists must have in order to meet the current technology capabilities and the expectations of business organizations. Тhe research addresses the following questions: 1 What skills base should a data scientist have? 2 What is the good practice in data science training? 3 What are the potential students of data science training course and what will be their input knowledge and skills base? The aim of this study is to present the road map of the progress for a student in developing the identified competences through Data Science Curriculum. In the context of the data science area, the curriculum as an endpoint in the map refers to the building of certain skills that allow extracting knowledge from the available data and is aimed at training students of the University of Library Science and Information Technology in Bulgaria
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摘要 :
As a fast-growing field with widespread impact on economy and its promising hiring potential, Data Science has been enthusiastically sought-out by many disciplines in the academia, including Mathematics, Statistics, Library Scienc...
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As a fast-growing field with widespread impact on economy and its promising hiring potential, Data Science has been enthusiastically sought-out by many disciplines in the academia, including Mathematics, Statistics, Library Science, Management Information Systems, as well as Computer Science, as an attracting area for recruiting students, soliciting grants, and expanding their existing programs. Although some standalone programs in Data Science have been established around the country, many institutions encounter the challenges in balancing the curricula among different disciplines, allocating new resources, and cooperating with the existing majors regarding enrollment management, student advising, and faculty preparation. In this paper, a strategy-based framework is proposed for those who have limited resources of all kinds to introduce Data Science through their existing Computer Science programs at a baccalaureate level with a minimum curriculum disruption. Instead of a standalone program, to embed Data Science into Computer Science education is demonstrated to be a practical, effective, cost-saving approach based on an extensive study of the synergy between Data Science and Computer Science education. While the proposed framework is not a one-size-fits-all approach, it provides a doable route for blending Data Science into Computer Science education in systematic ways. It has become the consensus that an adequate exposure to Data Science will better prepare computer science students for taking the challenges in this ever-changing, data embraced world.
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摘要 :
As a fast-growing field with widespread impact on economy and its promising hiring potential, Data Science has been enthusiastically sought-out by many disciplines in the academia, including Mathematics, Statistics, Library Scienc...
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As a fast-growing field with widespread impact on economy and its promising hiring potential, Data Science has been enthusiastically sought-out by many disciplines in the academia, including Mathematics, Statistics, Library Science, Management Information Systems, as well as Computer Science, as an attracting area for recruiting students, soliciting grants, and expanding their existing programs. Although some standalone programs in Data Science have been established around the country, many institutions encounter the challenges in balancing the curricula among different disciplines, allocating new resources, and cooperating with the existing majors regarding enrollment management, student advising, and faculty preparation. In this paper, a strategy-based framework is proposed for those who have limited resources of all kinds to introduce Data Science through their existing Computer Science programs at a baccalaureate level with a minimum curriculum disruption. Instead of a standalone program, to embed Data Science into Computer Science education is demonstrated to be a practical, effective, cost-saving approach based on an extensive study of the synergy between Data Science and Computer Science education. While the proposed framework is not a one-size-fits-all approach, it provides a doable route for blending Data Science into Computer Science education in systematic ways. It has become the consensus that an adequate exposure to Data Science will better prepare computer science students for taking the challenges in this ever-changing, data embraced world.
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摘要 :
Data science is changing our world in many different ways. Data and the associated data science innovations are changing everything: the way we work, the way we move, the way we interact, the way we care, the way we learn, and the...
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Data science is changing our world in many different ways. Data and the associated data science innovations are changing everything: the way we work, the way we move, the way we interact, the way we care, the way we learn, and the way we socialize. As a result, many professions will cease to exist. For example, today's call centers will disappear just like video rental shops disappeared. At the same time, new jobs, products, services, and opportunities emerge. Hence, it is important to understand the essence of data science. This extended abstract discusses the four essential elements of data science: "water" (availability, magnitude, and different forms of data), "fire" (irresponsible uses of data and threats related to fairness, accuracy, confidentiality, and transparency), "wind" (the way data science can be used to improve processes), and "earth" (the need for data science research and education). Next to providing an original view on data science, the abstract also highlights important next steps to ensure that data will not just change, but also improve our world.
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摘要 :
Data science is changing our world in many different ways. Data and the associated data science innovations are changing everything: the way we work, the way we move, the way we interact, the way we care, the way we learn, and the...
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Data science is changing our world in many different ways. Data and the associated data science innovations are changing everything: the way we work, the way we move, the way we interact, the way we care, the way we learn, and the way we socialize. As a result, many professions will cease to exist. For example, today's call centers will disappear just like video rental shops disappeared. At the same time, new jobs, products, services, and opportunities emerge. Hence, it is important to understand the essence of data science. This extended abstract discusses the four essential elements of data science: "water" (availability, magnitude, and different forms of data), "fire" (irresponsible uses of data and threats related to fairness, accuracy, confidentiality, and transparency), "wind" (the way data science can be used to improve processes), and "earth" (the need for data science research and education). Next to providing an original view on data science, the abstract also highlights important next steps to ensure that data will not just change, but also improve our world.
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摘要 :
As the importance of data science is increasing, the number of projects involving data science and machine learning is rising either in quantity or in complexity. It is essential to employ a methodology that may contribute to the ...
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As the importance of data science is increasing, the number of projects involving data science and machine learning is rising either in quantity or in complexity. It is essential to employ a methodology that may contribute to the improvement of the outputs. In this context, it is crucial to identify possible approaches. And an overview of the evolution of data mining process models and methodologies is given for context. And the analysis showed that the methodologies covered were not complete. So, a new approach is proposed to tackle this problem. POST-DS (Process Organization and Scheduling electing Tools for Data Science) is a process-oriented methodology to assist the management of data science projects. This approach is not supported only in the process but also in the organization scheduling and tool selection.
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摘要 :
As the importance of data science is increasing, the number of projects involving data science and machine learning is rising either in quantity or in complexity. It is essential to employ a methodology that may contribute to the ...
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As the importance of data science is increasing, the number of projects involving data science and machine learning is rising either in quantity or in complexity. It is essential to employ a methodology that may contribute to the improvement of the outputs. In this context, it is crucial to identify possible approaches. And an overview of the evolution of data mining process models and methodologies is given for context. And the analysis showed that the methodologies covered were not complete. So, a new approach is proposed to tackle this problem. POST-DS (Process Organization and Scheduling electing Tools for Data Science) is a process-oriented methodology to assist the management of data science projects. This approach is not supported only in the process but also in the organization scheduling and tool selection.
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摘要 :
As the importance of data science is increasing, the number of projects involving data science and machine learning is rising either in quantity or in complexity. It is essential to employ a methodology that may contribute to the ...
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As the importance of data science is increasing, the number of projects involving data science and machine learning is rising either in quantity or in complexity. It is essential to employ a methodology that may contribute to the improvement of the outputs. In this context, it is crucial to identify possible approaches. And an overview of the evolution of data mining process models and methodologies is given for context. And the analysis showed that the methodologies covered were not complete. So, a new approach is proposed to tackle this problem. POST-DS (Process Organization and Scheduling electing Tools for Data Science) is a process-oriented methodology to assist the management of data science projects. This approach is not supported only in the process but also in the organization scheduling and tool selection.
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摘要 :
The digital revolution made available vast amounts of data both in industry and in the research landscape. The ability to manipulate and extract knowledge and value from this data represents a new profession called the Data Scient...
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The digital revolution made available vast amounts of data both in industry and in the research landscape. The ability to manipulate and extract knowledge and value from this data represents a new profession called the Data Scientist: expected to be the most visible job in future years. The EDISON project has been established in order to support universities, research centers, industry and research infrastructure organisations to cope with the potential shortfall of Data Scientists, to define the framework of competences as well as the body of knowledge for this profession. In this paper the EDISON team describes how it intends to nurture the profession of Data Scientist to cope with the expected increase in demand. The strategy proposed is based on both the analysis of the demand side (industries, research centers and research infrastructure organisations) and the supply side (Universities and training centers) bridging between the providers and employers by cooperating on the establishment of a Competence Framework and a Body of Knowledge for the Data Scientist Professional. The project will exploit piloting initiatives in cooperation with pioneer universities and also involve external experts as evangelists.
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摘要 :
The digital revolution made available vast amounts of data both in industry and in the research landscape. The ability to manipulate and extract knowledge and value from this data represents a new profession called the Data Scient...
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The digital revolution made available vast amounts of data both in industry and in the research landscape. The ability to manipulate and extract knowledge and value from this data represents a new profession called the Data Scientist: expected to be the most visible job in future years. The EDISON project has been established in order to support universities, research centers, industry and research infrastructure organisations to cope with the potential shortfall of Data Scientists, to define the framework of competences as well as the body of knowledge for this profession. In this paper the EDISON team describes how it intends to nurture the profession of Data Scientist to cope with the expected increase in demand. The strategy proposed is based on both the analysis of the demand side (industries, research centers and research infrastructure organisations) and the supply side (Universities and training centers) bridging between the providers and employers by cooperating on the establishment of a Competence Framework and a Body of Knowledge for the Data Scientist Professional. The project will exploit piloting initiatives in cooperation with pioneer universities and also involve external experts as evangelists.
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