Data Science
Initiatives
Establishing "aDSec" to expand data science education across the university
In May 2022, the Aoyama Data Science Education Conference (aDSec) was established within the Aoyama Standard Education Institute. The Graduate School of Science and Engineering at our university launched the Data Scientist Training Program in 2019 and has been working to train practical data scientists. Through initiatives such as aDSec, we will expand the target to the entire university and further accelerate our development.
Data science education to be fully implemented with the "AI Strategy 2019"
Assistant to the President for Data Science, Professor, Department of Business Administration, College of Business Administration
Masuo Araki
He completed the standard period for completing his doctoral studies in economics at Aoyama Gakuin University's Graduate School of Economics. He received his Master's degree in Economics from Aoyama Gakuin University. After working as a visiting researcher at the Economic Planning Agency's Economic Research Institute and an assistant at Hitotsubashi University's Economic Research Institute, he was appointed full-time lecturer at Aoyama Gakuin University's School of Business Administration in 1999, and also served as visiting associate professor at Hitotsubashi University's Economic Research Institute (2008-2010). In 2010, he was appointed professor at the Department of Business Administration, School of Business Administration, Aoyama Gakuin University, where he remains to this day. His areas of expertise are economic statistics and information education.
Since the 2010s, a new vision of society known as a data-driven society, the era of the Fourth Industrial Revolution, and Society 5.0 has been advocated, while concerns about the shortage of AI talent to realize these have grown day by day. The 5th Science and Technology Basic Plan of 2016 and the Ministry of Education, Culture, Sports, Science and Technology's Comprehensive Human Resource Development Initiative can be seen as part of a series of efforts to urgently develop AI talent.
In terms of data science education at universities, the Integrated Innovation Strategy Promotion Council's "AI Strategy 2019 (Cabinet decision on June 11, 2019)" announced that it would provide AI literacy education to 500,000 people every year, regardless of their field of study. When the "Mathematics, Data Science, and AI Education Program Certification System" led by the Ministry of Education, Culture, Sports, Science and Technology was launched in 2021, universities across the country simultaneously shifted their focus to data science education.
The expansion of government-led data science education is in line with the direction of our university, which has emphasized "producing students who can make rational decisions with evidence." However, if the education target is "all students, regardless of their field of study," the educational system will need to be fundamentally revised, and faculty members will also be required to work together regardless of their field of study. Therefore, our university began preparations in 2021 to establish a faculty discussion group, and has launched the following three initiatives, including the Aoyama Data Science Education Conference (aDSec) within the Aoyama Standard Education Organization, which will be established in May 2022.
Leading the way with the Data Scientist Training Program
Director of the Data Scientist Training Program at the Graduate School of Science and Engineering, Director of the Advanced Information Technology Research Center at the Faculty of Science and Engineering, Professor at the Department of Information Technology at the Faculty of Science and Engineering
Gozo Ohara
He dropped out of the doctoral program in physics at the Graduate School of Engineering Science, Osaka University, and received his PhD in Engineering from Osaka University. After working as an assistant and assistant professor at the Institute of Scientific and Industrial Research, Osaka University, he became an associate professor in the Department of Information Technology, School of Science and Engineering, Aoyama Gakuin University in 2009, and became a professor in 2017. His areas of expertise are discovery science, intelligent informatics, and knowledge engineering.
The Graduate School of Science and Engineering launched the Data Scientist Training Program in 2019. Supported by AOYAMA VISION, the program aims to develop practical data scientists who possess three abilities: insight to interpret data, application skills to put cutting-edge technology into practice, and evaluation skills to assess the validity of results. To that end, the program has introduced lecture courses to teach knowledge, seminar courses to learn skills, and problem-solving courses to cultivate practical skills.
In implementing the program, we work with external companies and research institutes to provide learning opportunities that target real-world problems. In the "Advanced Data Analysis" course, we invite practitioners of data science from companies and research institutes to give lectures on the initiatives and challenges facing their institutions. In addition, in the "Problem-Solving Exercises," we have some of our students accepted as interns at companies and research institutes, and we also conduct on-campus PBL (Problem Based Learning) where students analyze data provided by partner institutions and receive reviews of the results from the data provider.
Three abilities aimed at in the Data Scientist Training Program
Over the three years since 2019, 95 students have completed the program (24 in the first year, 38 in the second year, and 33 in the third year). Some of the graduates have been employed by the companies where they undertook off-campus internships while enrolled in the program. This year, 35 students are enrolled. In addition, lectures were given by invited lecturers from 12 companies and research institutions, and 16 students were accepted for internships at four partner institutions. Data was provided by three partner institutions for the on-campus PBL.
There are two main issues for the future. The first is the difference in prior knowledge of information technology and programming skills between students who graduated from information-related departments and those who graduated from other departments. This makes it difficult to balance the progress of classes and exercises, so it is necessary to consider a curriculum structure that compensates for the lack of prior knowledge and programming skills.
Another issue is the burden on the faculty in charge. We hope to find a solution in the future as part of efforts to expand and optimize data science education across the university, including increasing the number of faculty in charge. We also hope to build a win-win relationship in which analyzing data provided by local companies helps solve their problems while also promoting human resource development at the university.
| Fiscal Year 2019 | 24 people |
|---|---|
| Fiscal Year 2020 | 38 people |
| Fiscal Year 2021 | 33 people |
Future developments will focus on diversity and servant leadership
Associate Professor, Department of Business Administration, Faculty of Business Administration
Hoshina Kakaku
Completed the doctoral program in Mathematics at the Chuo University Graduate School of Science and Engineering. Doctor of Science from Chuo University. After working as an assistant professor at the Shiga University Data Science Education and Research Center, he was appointed as an associate professor in the Department of Business Administration, Faculty of Business Administration, Aoyama Gakuin University in 2019, where he remains to this day. At the same time, he served as a specially appointed associate professor at the Shiga University Data Science Education and Research Center, and in 2022 he was appointed as a specially appointed associate professor at the Data Science and AI Innovation Research Promotion Center at the same university. His areas of expertise are statistical modeling (sparse modeling, Bayesian modeling) and data science education in industry and academia.
At the Shiga University Data Science Education and Research Center, where I worked for two years from 2017, I conducted joint research with companies, provided training to employees, and developed lectures on data polishing together with companies. From that experience, I learned that the essence of data science is not statistics, machine learning, or programming, but "problem solving." I came to understand that data science is "solving problems by making full use of data analysis."
If we re-evaluate the requirements for data science education, we can see that they include problem-solving ability, mathematical ability, knowledge of statistics and machine learning, programming ability, knowledge of servers, and knowledge of problem solving through data analysis. However, teaching all of these at university is certainly not cost-effective. On the other hand, the problems that need to be solved through data science are themselves more likely to be encountered at university, especially in liberal arts departments.
While there are six requirements for data science education, the problems to be solved through data science are more likely to be encountered in liberal arts departments at universities.
For these reasons, we would like to develop an educational program that uses data science to solve problems in the social sciences. Specifically, we will first provide data science education as a general education, and then in order to smoothly connect it to research in each student's field of expertise, we will make use of existing data science-related courses in each faculty and share lectures on topics that cannot be offered by a single faculty across the university. This will enable students to graduate with the expectation that they will be able to use data science to solve problems in their own fields of expertise.
In addition, Aoyama Gakuin University has the advantage of cultivating "servant leaders" who discover their own mission and willingly serve people and society, leading their lives as guides. The consensus-building that is important when servant leaders get others involved has an affinity with data science, which helps to build consensus in organizations. In other words, by developing data science education based on the servant leadership mentality cultivated by Aoyama Gakuin University, we believe that we can provide students with intellect that can truly be used to solve real problems, rather than simply receiving knowledge.
Cultivate talent who can use data science to solve problems in their own fields of expertise
Providing a more distinctive data science education
Vice President (Academic Affairs, Student Affairs, and Industry-Government-Academia Collaboration), Professor, Department of Social Informatics, Faculty of Social Informatics
Hiromasa Inatsumi
He left the doctoral program in mechanical engineering at the Waseda University Graduate School of Science and Engineering. He holds a PhD in Engineering (Waseda University). He was appointed to the Faculty of Science and Engineering in 1993. He has served as Vice Director of the Information Science Research Center, Director of Academic Affairs at the Faculty of Science and Engineering, Dean of the Faculty of Science and Engineering and the Graduate School of Science and Engineering since 2004, Dean of the Faculty of Social Informatics and the Graduate School of Social Informatics since 2010, and Director of the Student Counseling Center since 2016. His areas of expertise are information theory, artificial intelligence, machine learning, and Japanese language education.
Data science is not an academic field that specializes in science, but is truly an "interdisciplinary" theme. This is because it is expected to produce greater results by adding a "data-based and data-backed" approach to the traditional humanities and social sciences efforts that deal with various issues surrounding people and society.
In order to make data science an initiative for universities in the future, it is urgent to create a platform that allows for a wide range of learning in this field, even in humanities and social science faculties. Therefore, in addition to building this platform as soon as possible, we believe that it is necessary to develop "data-based and data-supported" initiatives for each specialized field based on it, that is, distinctive data science education.
Our university has already been providing information literacy education for all students for the past 20 years. We are now planning to expand this achievement into a data science education program as a new literacy education for all students. Specifically, we will start aDSec and provide an introductory education program as a standard Aoyama subject to students of all faculties. From there, aDSec will support and coordinate the program and expand it to specialized education in each faculty. Although we are not calling it a Faculty of Data Science or Department of Data Science, we will demonstrate our unique strengths by bridging data science with each specialized field and by engaging in various initiatives in each specialized field.
Data science is no longer a specialized field of science, but an interdisciplinary topic.
Lab Interview
A future where software gets smarter by using human knowledge
Associate Professor, Department of Information Technology, College of Science and Engineering
Takeshi Morita
Graduate School of Science and Engineering, Department of Science and Engineering, Intelligent Information Course, Master's Program, 1st year
Yuki Sawamura
The Knowledge Engineering Laboratory in the Department of Information Technology in the Faculty of Science and Engineering is researching technology that allows artificial intelligence to use human knowledge, and systems that can utilize that knowledge. We asked about the content and results of their research.
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Develop data-driven thinking skills needed in any field
Professor, Department of Social Informatics, Faculty of Social Informatics
Atsushi Terao
Faculty of Social Informatics, Department of Social Informatics, 3rd year
Shohei Namiki
Terao's seminar uses data science to conduct a wide range of research into human thought and behavior. Through learning in the seminar, students aim to be able to interpret data and make data-based assertions.
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Faculty Interviews
Understanding the issue through data science using cutting-edge computer technology
The mysterious world of classical languages
Faculty of Literature, Department of Japanese Literature
Professor Yasuhiro Kondo
Professor Kondo has been using cutting-edge computer technology to continue his research into the oldest classical languages since before the idea of "integration of the humanities and sciences" became as widespread as it is today. We spoke to Professor Kondo about the kind of talent that will be needed in the future of humanities research.
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Statistics is rapidly spreading in modern society and is required for today's data science
Statistical literacy
Faculty of Economics Department of Economics
Associate Professor Tamae Kawasaki
Data science based on statistics can be a powerful tool in modern society, but at the same time, it can be difficult to use it properly without statistical literacy, says Associate Professor Kawasaki.
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