Data Science Education

Mike, Koby

doi:10.1145/3372782.3407110

Cited by 13 publications

(9 citation statements)

References 4 publications

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“…While the presentation of the term data science has so far only in several context of discussion in the last thirty years. Data science is not yet born; the content is still young so that it can be present in the form of a study program (Karbasian & Johri, 2020;Krystina, 2020;Leidig & Cassel, 2020;Mike, 2020). However, data science we expressed as a multi-disciplinary study that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data (Dhar, 2013).…”

Section: Resultsmentioning

confidence: 99%

The birth of a science

Nasution

2020

Hist. sci. technol.

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Physics, biology, chemistry, for example, do not already only study programs in many universities but are at the forefront of the development of science, and that is science itself. The development of the basic foundation becomes the mother of science, such as mathematics, as a suspicion of the birth of new sciences. The interests related to the life and welfare of humans always invites the possibility of the birth of science in concepts, but mathematics confirms it. In certain cases, as an application, or it gives birth to answers in technology form, which is distorted according to the social demands of society. Every science has its methodology, which is based on the logic that helps to reason. A natural model is an approach to generating methods, as well as the principles that develop in artificial intelligence based on mathematics. This principle not only changes the way of perceiving data, but it also establishes new definitions of data, and changes statistics, optimization, and other fields. Each science also has implications for other sciences but also has applications that are also related to other sciences. Every science supports technology to improve social welfare. Historically, century by century, many new scientific fields were born, and it has evidence from the publication of scientific works to books as scientific standards. Then, the teaching curricula supported its initial dissemination and its development through research with evidence of scientific publications either through papers in proceedings of scientific meetings or articles in the journals. A study program about science in the related faculty is mandatory, and it is as the spearhead in further scientific and technological development. So everything starts and ends at one point, namely the birth of new science, as the birth of data science. This paper describes the birth of science by involving historical traces. The reflection of scientific development is starting from the foundation to become a scientific field, namely data science. It also illustrates the roadmap of a scientific indirectly.

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Section: Resultsmentioning

confidence: 99%

The birth of a science

Nasution

2020

Hist. sci. technol.

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“…Today, the importance of data science education is widely recognized, and many programming practices also include data analysis exercises [2], [3], [4]. Let us consider a very common scenario for exercising data science in a classroom.…”

Section: Scenariomentioning

confidence: 99%

Training AI Model that Suggests Python Code from Student Requests in Natural Language

Kuramitsu,

Obara,

Sato

et al. 2024

Journal of Information Processing

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Programming is a creative activity, but it can be difficult to learn due to constant updates, poorly maintained documentation, and unexpected errors. One reason for the difficulties is the shortage of programming teachers, which often leaves students unable to get help when they need it, even for simple questions. Many unanswered questions are a barrier to improving programming skills for creative purposes. The purpose of this paper is to address this issue by exploring whether an AI-based system can help reduce the difficulties faced by students. Recent advancements in deep learning technology have made it easier for teachers to train AI models that can learn from their own experiences in the classroom, including the types of questions, requests, and difficulties that students encounter. We have developed an AI model that can translate Python code from Japanese by using machine translation techniques and large language models. We have integrated this model into a learning assistant system that suggests code to students when they express their programming intentions. In this paper, we present our experiences in developing and deploying this AI-based assistant in the classroom, as well as the feedback we have received from students. By sharing our initial experiences, we aim to envision the potential of educational AI development for the future.

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“…While computer science undergraduate programs are the popular preference for DS (Mike, 2020), incorporating DS into domain programs can be of immense benefit (Castellanos et al, 2019;Davenport & Malone, 2021). It is important to understand that the nature of DS has distinct needs and significance depending on the organisation or domain.…”

Section: Discussionmentioning

confidence: 99%

“…DSE should further afford skills and competencies to work with various forms of data and interpret the analytical results, especially for those who lack DS literacy (Dichev & Dicheva, 2017). Therefore, it is important to establish instructors' confidence in teaching DS (Mike, 2020), or how best to prepare instructors to teach DS in various domains (Emery et al, 2021). For instance, data scientists in biomedicine need to be trained in computer science, statistics and mathematics, and biomedicine (Garmire et al, 2017;Hassan & Liu, 2020).…”

Section: Ds Instructional Programsmentioning

confidence: 99%

“…In addition, research on how DS should be taught or what type of competencies are required from data science academic staff is still lacking. Such research opportunities have the potential to support academia as it struggles to position itself within the data science field (Cao, 2019;Engel, 2017;Mike, 2020) and lead to the creation of new research topics in IS (Cao, 2017) such as how instructors approach DS teaching practices (Lau et al, 2022). Another challenge confronting the field of DSE includes data science tools and techniques that are continuously evolving.…”

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confidence: 99%

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Instructors' perception of the competencies required to teach DS in HEI

Msweli¹

EPiC Series in Education Science

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While data science education may be a solution in democratising data science, there are challenges towards achieving this. One of the challenges is the shortage of qualified academic staff members, who are able to deliver a multidisciplinary curriculum.Teaching data science is a developing topic and represents a gap in the literature as we embark on the journey of discovering the knowledge required to teach data science. This study aimed to gain insight into instructors’ perceptions on their skills and competencies in teaching data science

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Data Science Education

Cited by 13 publications

References 4 publications

The birth of a science

The birth of a science

Training AI Model that Suggests Python Code from Student Requests in Natural Language

Instructors' perception of the competencies required to teach DS in HEI

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