Foundations of Data Science : a comprehensive overview formed at the 1st International Symposium on the Science of Data Science

We present a summary of the 1st International Symposium on the Science of Data Science, organized in Summer 2021 as a satellite event of the 8th Swiss Conference on Data Science held in Lucerne, Switzerland. We discuss what establishes the scientific core of the discipline of data science by introducing the corresponding research question, providing a concise overview of relevant related prior work, followed by a summary of the individual workshop contributions. Finally, we expand on the common views which were formed during the extensive workshop discussions

Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science

Preparing Undergraduate Students Majoring in Computer Science and Mathematics with Data Science Perspectives and Awareness in the Age of Big Data

AbstractUndergraduate students majoring in Computer Science and Mathematics are entering the workforce not only as programmers and mathematicians but also as data and business intelligent analysts. These job profiles require students to effectively utilize databases and data warehouses technologies, summarize data from external sources including the Internet and provide solutions to complicate, dynamic and ever-changing problems. These areas of hard skills have not been integrated as a major component of undergraduate programs in mathematics and computer science. This paper is aimed at showing how to motivate the significance of mastering data science proficiency as well as depicting examples and resources for lecturers in implementing data science in computer sciences and mathematics curriculum. Two case studies from Computer Science and Informatics Mathematics Programs at Faculty of Science and Technology, Suan Sunandha Rajabhat University in Bangkok, Thailand are presented

Starting a Debate: Data Science – Occupation or Profession? A Discussion Paper

With this contribution at the ECDA-2019 in Bayreuth we want to start a much needed debate about the nature of the work of a data scientist. Is it a mere occupation or does the societal impact together with ethical issues surrounding the work imply data science should become a real profession in the sense of Airaksinen (Airaksinen, 2009). We explore the elements of data science and the responsibility a data scientist has for society. Some barriers are identified and what can be done about them. In this paper, we describe the line of reasoning which was presented, and some lessons learned from the actual discussions with the audience

Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in data science. The group consisted of 25 undergraduate faculty from a variety of institutions in the United States, primarily from the disciplines of mathematics, statistics, and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in data science

Research on Cognitive Domain in Geoscience Learning: Quantitative Reasoning, Problem Solving, and Use of Models

Models (from simple mental models to complex computational models) are used by geoscientists to conceptualize and better understand the Earth system and to make predictions. Earth processes affect the human condition and result in hazards and complex issues that require both expert and citizenry decision-making about mitigation and adaptation. In addition, a wide range of Earth materials (e.g., mineral, rock, water) are valued resources that need sustainable management. All of these challenges require recognition of the problem (problem-finding), and the development and application of problem-solving skills. In addition, Earth system understanding and problem-solving benefit strongly from quantitative reasoning. Quantitative reasoning, problem-solving, and use of models present many daunting challenges to both students and instructors. All are valued by the professional geoscience community and by employers, and all would benefit from more education research. In this chapter, grand challenges and recommended strategies for each of these areas are identified and described

The importance of understanding web frameworks for statisticians

As the web and computational technology carry on growing and huge data are yielded on the web, these technologies are turn into important for a statisticians' work. It is worthy that statistician always gain knowledge of new aspects of computation. A lack of computational reasoning skills gets it hard for statisticians to work in a team. If statistician do not take up this computations challenge more coherently, statistics will be marginalized and take away related at a time when its data science reputation grow up significantly. In addition, people rely on the information on web, for whatever their reason.Since web growth, several major transforms have evolved, from the most rudimentary concept until a new model of interaction between humans and machines. Simple interactivity denotes that users can enter data to the application on a web page, then click on button, and then appears a new web page with the results of the computations. This application has been known as web application with most are built with the utility of web frameworks which is a package of programming tasks that offering services through the Internet. Therefore, this paper gives short overview the importance of Flask web frameworks to assist the lack of computational skill of statistician over web application in the simplest possible way and how web framework is used to create a web page with application form, run the application to compute statistical calculation which has been deployed in local server, and produce a web page with the solution

Re-integrating scholarly infrastructure: the ambiguous role of data sharing platforms

Web-based platforms play an increasingly important role in managing and sharing research data of all types and sizes. This article presents a case study of the data storage, sharing, and management platform Figshare. We argue that such platforms are displacing and reconfiguring the infrastructure of norms, technologies, and institutions that underlies traditional scholarly communication. Using a theoretical framework that combines infrastructure studies with platform studies, we show that Figshare leverages the platform logic of core and complementary components to re-integrate a presently splintered scholarly infrastructure. By means of this logic, platforms may provide the path to bring data inside a scholarly communication system still optimized mainly for text publications. Yet the platform strategy also risks turning over critical scientific functions to private firms whose longevity, openness, and corporate goals remain uncertain. It may amplify the existing trend of splintering infrastructures, with attendant effects on equity of servic