Journal of JSEE
Online ISSN : 1881-0764
Print ISSN : 1341-2167
ISSN-L : 1341-2167
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Volume 70, Issue 1
Displaying 1-27 of 27 articles from this issue
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Foreword
Special purpose
Introduction and explanation
  • How Should Higher Education Comprehend Data Science Education and DX?
    Masashi HATTORI
    2022 Volume 70 Issue 1 Pages 1_3-1_6
    Published: 2022
    Released on J-STAGE: January 20, 2022
    DOIhttps://doi.org/10.4307/jsee.70.1_3
    JOURNAL FREE ACCESS
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    The terms, data science and digital transformation, have been popular in corporative activities and services. This trend implicates higher education as well. In addition, the coronavirus crisis accelerates digital transformation in higher education. Almost all universities use distance education to adapt the crisis. Many stakeholders including faculties and students understand pros and cons of distance education. This situation is a good chance of catching up with digital transformation in higher education. This article describes data science education and digital transformation in higher education toward Society 5.0.
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Editorial
Case study
Editorial
  • Data Science Education in AI Society
    ～Fostering Problem-Solving Skills using Data and ICT 〜
    Michiko WATANABE
    2022 Volume 70 Issue 1 Pages 1_30-1_35
    Published: 2022
    Released on J-STAGE: January 20, 2022
    DOIhttps://doi.org/10.4307/jsee.70.1_30
    JOURNAL FREE ACCESS
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    With the availability of vast amounts of data and the tools to analyze them, organizations in almost every sector are focusing on leveraging data for competitive advantage. While data analytics skills are in demand, the shortage of human resources is becoming a major issue around the world, making the design of data science education a key challenge for universities. This paper discusses the design of data science education in relation to the development of problem-solving skills.
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  • The Importance of Literacy Education for Value Creation through Data Science
    Ryoichi ISHIZUKA, Yuto FUJIMOTO, Jun SUGIMOTO
    2022 Volume 70 Issue 1 Pages 1_36-1_40
    Published: 2022
    Released on J-STAGE: January 20, 2022
    DOIhttps://doi.org/10.4307/jsee.70.1_36
    JOURNAL FREE ACCESS
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    The introduction and use of data science, AI, and DX are essential to increase the competitiveness of companies and nations, and a system to train data scientists, people with expertise in these fields, is being established in the field of education. On the other hand, value creation through data science has a cross-disciplinary aspect, and to achieve this, it is necessary for the parties involved to cooperate beyond their individual areas of expertise. Therefore, this paper discusses the role that “non-data science specialists” should play in projects to create value in data science, and the necessity and importance of data science literacy education for them, using the efforts of Hitachi Zosen Corporation as an example.
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Paper
Case study
  • A Case in Which All Students Gave Their Presentations on a Video-sharing Site in a Course of Statistical Modeling
    Shogo OKAMOTO
    2022 Volume 70 Issue 1 Pages 1_53-1_57
    Published: 2022
    Released on J-STAGE: January 20, 2022
    DOIhttps://doi.org/10.4307/jsee.70.1_53
    JOURNAL FREE ACCESS
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    University courses pertained to data-science subjects typically involve students′ presentations. However, due to the temporal limitation of the courses, all student attendees may not be allowed to have their opportunities of presentations. This paper introduces a method in which all students give their presentations by utilizing a video-sharing-site. Furthermore, a double-blind mutual evaluation was attempted to ensure the perceived fairness of evaluation and students′ active engagement in the question and answer section. These detailed methods are described and their potential effects are discussed.
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  • Basic Mathematics Education and Use of Calculation Programs
    Takeo FUJIWARA
    2022 Volume 70 Issue 1 Pages 1_58-1_63
    Published: 2022
    Released on J-STAGE: January 20, 2022
    DOIhttps://doi.org/10.4307/jsee.70.1_58
    JOURNAL FREE ACCESS
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    We are introducing a lecture Basic Mathematics using MATLAB, which is currently offered at the Faculty of Economics, University of Tokyo. The goal is to understand mathematics itself and acquire “usable mathematics”, not-sticking to strict proofs. Basic mathematics education, learned with “Calculation = Experimental Method” is effective in a wide range of fields.
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  • A Practical Example of Data Science Education through Hands-on Lecture using Python
    Naofumi WADA, Hiroki MATSUZAKI, Hirofumi SANADA
    2022 Volume 70 Issue 1 Pages 1_64-1_69
    Published: 2022
    Released on J-STAGE: January 20, 2022
    DOIhttps://doi.org/10.4307/jsee.70.1_64
    JOURNAL FREE ACCESS
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    Programming education is essential for developing data scientists and data engineers with practical skills. Python has become the de facto standard programming language for data analysis. In this paper, we present an example of practical data science education using Python conducted for second-year undergraduate students in the Department of Information and Computer Science at our university in 2019 and 2020. A feature of the lecture is a fill-in-the-blank “Lecture Notes” created by Jupyter Notebook. By the introduction of Lecture Notes, students can learn Python programming as if they were taking notes in hands-on style class. As a result of a questionnaire survey, about 80％ of the students answered that they were able to concentrate on the class.
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  • Initiatives Related to Industry-Academia Joint Research Projects for the Purpose of Data Science Education
    Kohei OTAKE, Takashi NAMATAME
    2022 Volume 70 Issue 1 Pages 1_70-1_74
    Published: 2022
    Released on J-STAGE: January 20, 2022
    DOIhttps://doi.org/10.4307/jsee.70.1_70
    JOURNAL FREE ACCESS
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    In this paper, we introduce the management method of the joint industry-university research project with private companies and local governments, which we have been working on for the purpose of “practical education with emphasis on contact with the society.” First, we describe the characteristics of collaborative research projects in higher education institutions in terms of “project members,” “problem setting,” “knowledge and skills,” and “handling of data and publication of results.” Next, we describe the management method of the collaborative research project that we have been working on. Specifically, it consists of three phases: problem extraction, data analysis, and verification and operation. Finally, we introduce a case study of a joint research project with a private company. Moreover, we summarize some insights into data science education gained through the operation of the project.
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70th Anniversary Project
Editorial
Collection of writing
Information
Report
Short note
Oasis
Announcement
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