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Shiga University’s endeavor to promote
human resources development for data
science in Japan
Takuma Tanaka1,2,3*, Tetsuto Himeno1,2,3 and Kaoru
Fueda1,2,3
1* Faculty of Data Science, Shiga University, 1-1-1 Banba, Hikone,

Shiga, 522-8522, Japan.
2* Graduate School of Data Science, Shiga University, 1-1-1

Banba, Hikone, Shiga, 522-8522, Japan.
3* The Center for Data Science Education and Research, Shiga

University, 1-1-1 Banba, Hikone, Shiga, 522-8522, Japan.

*Corresponding author(s). E-mail(s):takuma-tanaka@biwako.shiga-u.ac.jp;
Abstract
In 2017, Shiga University established the Faculty of Data Science,
which was the ﬁrst faculty in Japan specializing in data science andstatistics. This paper reports the Faculty’s historical context, curricula, and collaboration with industry and other universities. The
career paths of the graduates and the massive open online courses andtextbooks provided by the Faculty of Data Science are also summarized.
Keywords: Collaboration, Data science, Data scientist, Project-basedlearning, Statistical science, Education

1 Introduction
The coronavirus disease 2019 (COVID-19) pandemic has been a stark reminderof the importance of data acquisition, data accumulation, data analysis, anddata visualization, i.e., the importance of data science (Gardner, Ratcliﬀ,

1

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Shiga University’s endeavor in data science education in Japan

Dong, & Katz, 2021). Although the term “data science” has been used sincethe 1960s (Irizarry, 2020; Naur, 1966), the presence of data and data sciencein society has seen explosive growth over the past decade, starting with anarticle with an eye-catching title, “Data Scientist: The Sexiest Job of the 21stCentury” (Davenport & Patil, 2012). Data science has become an indispensable tool in various areas in society including information technology, business,
public policy, medicine, and basic science.
Combined with data, artiﬁcial intelligence (AI) is projected to deliver 1.2
percent additional GDP growth per year until 2030 (Bughin, Seong, Manyika,
Chui, & Joshi, 2018). However, to maximize the value gained from big data, notonly technology investment, but also newly trained experts and the adequate
policies and management are needed (Byers, 2014). Therefore, governments
and political organizations around the world, including the European Unionand United Nations, are rushing to fund human development and higher education in data science and AI (European Commission, 2014; UN Global Pulse,
2021). For instance, in 2020, the UK government awarded £24 million of funding to 18 universities that deliver AI and data science conversion courses(Department for Digital, Culture, Media & Sport, Department for Education, Department for Business and Energy & Industrial Strategy, & Oﬃce for
Artiﬁcial Intelligence, 2020)
On the other hand, a severe shortage of data scientists and AI engineers
has been reported in Japan compared with other developed countries. A reporton big data by McKinsey Global Institute (Manyika et al., 2011) showed thatthe number of people in Japan with deep analytical training decreased from2004 to 2008 to about 3,400 in total. Mizuho Research and Technologies (2019)predicted that AI engineers would reach a shortage of 145,000. The Japan
DataScientist Society (2019) reported that less than 30% of companies thathave at least one data scientist.
The eﬀort to train data scientists in Japan started with the establishmentof the Japan DataScientist Society in 2013, and since that time, various policies
for the development of data scientists have been implemented. The Ministryof Education, Culture, Sports, Science and Technology (MEXT) sponsoredthe Data Scientist Training Network in July 2013, the aims of which were thesustainable development and eﬀective utilization of data analytics experts inJapan (Maruyama, Kamiya, Higuchi, & Takemura, 2015). In 2016, the Japan
Inter-university Consortium for Mathematics and Data Science Education wasestablished, with the purpose of creating and disseminating a standard curriculum and teaching materials, such as data sets, that could serve as models
for schools across the entire country. In 2019, AI Strategy 2019, which was
issued by a government council (Integrated Innovation Strategy Promotion
Council, 2019), proposed education reform for all high school, college, and university students. With these actions, data science education has made progressin Japan.

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Shiga University’s endeavor in data science education in Japan3

One of the most important moments in the eﬀort to raise the standards ofdata science education in Japan was the foundation of the Faculty of Data Science of Shiga University in 2017. This was the ﬁrst faculty in Japan to specialize
in statistics and data science. The history and context of statistics education
before the establishment of the Faculty of Data Science of Shiga University
is described in Takemura (2018). As a sequel to Takemura (2018), this paper
describes the government’s policy, the curricula of the Faculty of Data Science,and its collaboration with industry and other universities. In this paper, we
detail how the Faculty of Data Science has established university-level data
science education and collaboration with higher education and industry inJapan.
This paper is organized as follows. Section 2 gives a detailed account of thegovernment’s policy with an emphasis on higher education. Section 3 describes
the curricula of the Faculty of Data Science and its eﬀort to disseminate data
science education to Shiga University as a whole. Section 4 is divided into
two subsections: the ﬁrst summarizes the collaboration between the Faculty ofData Science and other universities and high schools, and the second reports
its collaboration with industry. The employment status of the graduates is also
reported. Section 5 concludes.

2 Policy of human resource development fordata science by the Japanese government
The establishment of the Inter-university Consortium for Mathematics andData Science Education in 2016, the purpose of which is described above,
brought about a growing tendency for redesigning data science education in
Japan. The standard curriculum created by the curriculum committee of theconsortium was based on “The Data Science for Undergraduates: Opportunities and Options” (National Academies of Sciences, Engineering, and Medicine,2018).
In 2019, the Japanese government announced a policy called AI Strategy
2019 to strengthen the development of human resources for mathematics, data
science, and AI (Integrated Innovation Strategy Promotion Council, 2019).
One of the objectives of the policy was to improve statistics and data scienceeducation. Before then, inferential statistics had been taught in the high school
curriculum. However, this was not compulsory until 2021, so only about 10%of high school students studied inferential statistics (Ministry of Education,
Culture, Sports, Science and Technology, 2019). MEXT is preparing a new
curriculum starting in 2022 to encourage more high school students to study
statistics. Moreover, AI Strategy 2019 proposed that all college and university students should acquire data science and AI literacy. To implement this
proposal, the Inter-university Consortium for Mathematics and Data Science
Education published the skill sets, learning goals, and model curriculum of data
science. To promote the model curriculum, the Japanese government enacted
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Shiga University’s endeavor in data science education in Japan

with the model curriculum was assessed. Moreover, in 2020, MEXT proposeda data scientist training project for master’s students for the realization of
a Super Smart Society and Doctoral program for Data Related InnoVation
Expert (D-DRIVE) to enable doctoral students and postdoctoral researchers
to improve their ability fully (https://ddrive.jp/).
However, the implementation of these projects requires a larger numberof experts for teaching data science and statistics. To train these experts,
the Institute of Statistical Mathematics was chosen as the core institution of
the Statistical Expert Resource Development Consortium to be developed in
2021, and Shiga University served as its satellite center for western Japan.
The purpose of this consortium is to provide statistics education to researchers
who specialize in various applied ﬁelds and to foster statistics experts. It is
expected that such researchers retrained in statistics will be able to supervise
students and foster data scientists at universities in the consortium.
Therefore, a wide range of policies for the development of human resourcesin data science is currently being implemented in Japan. However, greater
eﬀorts in data science education are needed to develop data science education
at a higher level.

3 Data science curriculum in Shiga University
One of the antecedents of Shiga University is Hikone Commercial College,
which was established 100 years ago in 1922. The mission of Hikone Commercial College was to train businesspersons in the era of industrialization. In linewith this mission, to train businesspersons in the era of big data, Shiga University launched the Center for Data Science Education and Research in 2016 andestablished the Faculty of Data Science in 2017. The student quota and number of faculty members were 100 and 17, respectively. Graduate courses were
established in 2019 (master’s course) and 2020 (doctor’s course), the quotas
for which were 20 and 3, respectively. To date, more than half of the graduate
students are employees of companies and public agencies. This fact shows that
Shiga University successfully ﬁlls the demand for recurrent education. In 2021,
following the graduation of the inaugural class of the undergraduate and graduate courses, the university increased the quota of students for the master’scourse in the Graduate School of Data Science from 20 to 40; the quota forthe doctor’s course is planned to be increased. These measures are intended to
fulﬁll the social demand for experts in data science. Since the establishmentof the Faculty of Data Science, the total number of Faculty and Center members has increased to 44. In response to the rapid progress of AI and its social
implementation, researchers in AI and social research were newly hired in the
Faculty and Center.
In the same year, curriculum reform of the undergraduate course was carried out. The objectives of the curriculum reform were twofold. The ﬁrst
objective was to encourage the students to obtain more practical skills. The
skill sets needed in the graduation project were more rigorously deﬁned andSpringer Nature 2021 LATEX template
Shiga University’s endeavor in data science education in Japan
1st grade
2nd semester
1st semester

2nd grade
3rd semester
4th semester

Introduction to
Data Science Practice

Advanced Course in
Data Science PracticeIntroduction to
Data Science3rd grade
5th semester
6th semester

Methodology ofCreating Value from Data

Presentation Skills
Training

Practical Theory ofData Science

Value-Creation Courses (3 credit points)(Oﬃcial Statistics; Speech Data and Dialogue System; Bioinformatics;
Marketing; Biostatistics; Causal Inference from Biological and Economic Data;
Psychological Analysis; Image Processing; Sociology; Artiﬁcial Intelligence)

Data Cleansing

Data Science Ethics

Data Science Advanced Practice I/II
Data-Driven Thinking
for Business5

4th grade
7th semester
8th semester
Data
Data Science
Science Graduation
Graduation Practice
Practice I/II
I/II
Advanced Course in Practical
Theory of Data Science A

Advanced Course in Practical
Theory of Data Science C

Advanced Course in Practical
Theory of Data Science B

Advanced Course in Practical
Theory of Data Science D

Value-Creation Courses (2 credit points)(Insurance Strategy; Business Thinking; Spatial Econometric Analysis;
Environment, Transport and Urban Policy; Spatial Analysis for Disaster;
Data Analysis in Climatology and Meteorology)

Economics and Management Courses: (Micro-Economics A/B, Macro-Economics A/B, Bookkeeping and Accounting A/B, Business Administration, Financial Accounting I/II, Financial Statements I/II, Management Accounting I/II,
Security Analysis and Portfolio Management I/II/III/IV, Econometrics, Financial Econometrics, Basics of Business Economics）

Introduction to
Artiﬁcial Intelligence

Data Structure
and Algorithm

Programming 1

Programming 2

P

P

Basics ofComputer Application

Basics ofData Analysis

Linear Algebra
Fundamentals of
Statistical Inference

P
P

Software Design

Programming 4

P

DS Courses: Data Analytics/Analysis Series

DS Courses: Social Research Series

Advanced Course in
Data Science A

Advanced Course in
Data Science C

Information SecurityP

Courses of Graduate School of Data Science

Basics of Computational
Data Analysis B

Computational
Data Analysis A

Computational
Data Analysis B

Mathematical Statistics

Probability Theory

Linear Regression Models

Experimental Design

Bayesian Theory

Introduction toMultivariate Analysis

Text Mining

Simulation Techniques

Statistical Quality Control

Introduction to Qualitative
Data Analysis

P

Optimization Theory

Survival Analysis

Sample Survey

Introduction to Machine Learning
and Artiﬁcial Intelligence

Sample Research
Method I

Sample Research
Method II

Advanced Course in
Data Science B

P

Introduction to
Time Series Analysis

Data-Driven PBL PracticeDS Courses: Data Engineering Series

Information Network

Programming 3

Introductory Course

Fundamental and Applied Value-Creation Courses

Information Theory

Database
Introduction to Multimedia
Data Processing

Basics of Computational
Data Analysis A

Calculus

Applied Mathematics

Machine Learning and
Artiﬁcial Intelligence

Practice in Social Research
I/II
山路を登りながら

Advanced Course in
Data Science D: with practice

Red: compulsory course

Advanced Course inSocial Research Method A

Advanced Course inSocial Research Method B

Fig. 1 Curriculum map of the Faculty of Data Science of Shiga University (2021–).

designed to be acquired by students. A course on data cleansing was newly
established, and the programming courses were rescheduled to start earlier.
The second objective was to deepen the knowledge and skills of the students
in the emerging ﬁeld of AI. To this end, Introduction to Multimedia Data Processing, Speech Data and Dialogue Systems, and Image Processing were newly
established.
The reformed curriculum map of the Faculty of Data Science is shownin Fig. 1. Courses are classiﬁed into six groups: an introductory course, dataengineering courses, data analytics courses, social research courses, fundamental and applied value-creation courses, and data-driven project-based learning(PBL) practices. Data engineering courses include courses on programmingand computer science. Data analytics courses include courses in mathematics, statistics, and machine learning. Social research courses include courses
on sample surveys and research. Value-creation courses include ethics, data
cleansing, and various applied ﬁelds of data science. Data-driven PBL practices, of which we give a detailed account in the following, contain practices
using data from various ﬁelds. Most courses in the ﬁrst, second, and thirdsemesters and all PBL practices are compulsory.
In the following, we give a detailed account of the curriculum reform using
examples from the PBL and data engineering courses. Since its establishment
in 2017, the Faculty of Data Science of Shiga University has put PBL frontand center. PBL practices are compulsory in the second, fourth, ﬁfth, sixth,

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Shiga University’s endeavor in data science education in Japanseventh, and eighth semesters. The PBL practice in the second semester (Introduction to Data Science Practice) focuses on the basics of data analysis basedon the Problem, Plan, Data, Analysis, and Conclusion (PPDAC) cycle (Wild& Pfannkuch, 1999) and data analysis skills using mainly Excel. The PBL
practice in the fourth semester (Advanced Course in Data Science Practice) is
aimed at acquiring advanced data analysis skills using Python and R. These
two PBL practices lay the foundation of the PBL practices in the ﬁfth througheighth semester, in which the students accomplish their graduation project.
Introduction to Data Science Practice in the second semester includes lectures on the PPDAC cycle and group exercises. After the curriculum reform,
we deﬁned the content of each class, such as how to calculate representativevalues and use logistic regression. Lecture on these topics are given in the ﬁrst
half of the class and practiced in the second. In the second half of the class,the students form groups and tackle problems they have formulated.
Before the curriculum reform, the students practiced either Python or R
in Advanced Course in Data Science Practice. After the reform, they pursue
the skills of both. Additionally, after the reform, greater emphasis is placed ondata preprocessing, especially in regard to missing data.
Data engineering courses are intended to encourage students to acquire
practical data processing skills. Although lectures and practices on programming started in the second semester before the curriculum reform, they startin the ﬁrst semester after the reform. The earlier starting time aims to enable
students to acquire essential programming skills before learning PBL practices.
There are four sets of programming lectures and practices in our curriculum. Programming 1 in the ﬁrst semester is an introduction to Python.
Programming 2 in the second semester is an intermediate Python course. The
main theme of Programming 2 is learning the standard data-science libraries
including NumPy, Pandas, Matplotlib, and scikit-learn. Although Programming 2 was initially designed as an algorithm-oriented programming course,
after the curriculum reform, it focused on statistics and AI. Programming 3
in the third semester is an introduction to Java. This is also a course for GUI
programming. Programming 4 in the fourth semester is a course on JavaScript,
HTML, web programming, web scraping, and 3D visualization. Initially, thiscourse was designed to contain numerical analysis, but this was later omitted
to emphasize data acquisition and visualization.
In addition to training specialists, the Faculty of Data Science helps theother faculties of Shiga University integrate data science education into theircurricula. There are two faculties at Shiga University other than the Faculty of Data Science: the Faculty of Education and the Faculty of Economy.
The Faculty of Education is planning to train teachers capable of promoting
evidence-based education. The Faculty of Economy is reforming its curriculum
to train businesspersons who can practice data-driven decision-making. The
Faculty of Data Science provides these faculties with introductory courses ondata science, one of which is planned to be compulsory for all students. Massive open online courses (MOOCs), which are described in the next section,
are used in the courses for these faculties.

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74 Collaboration with universities and industry
We collaborate with other universities to promote and disseminate data scienceeducation in all universities in Japan and with industry to provide data-driven
consultancy and train employees to be data scientists. In this section, we review
our various eﬀorts, i.e., collaboration with other universities, the MOOCs andtextbooks by the Faculty of Data Science, collaboration with high schools, theemployment status of graduates, collaboration with industry, and the consortia
established to promote collaborations.

4.1 Inter-university collaboration for data scienceeducation
In August 2020, the Liaison Committee for Data Science Departments ofUniversities in Japan (https://lcdsj.jp/) was formed with the aim of contributing to the development of data science education and research in Japanthrough cooperation and collaboration among specialized data science faculties, departments, graduate schools, and majors. At the time of its inception,
six universities and departments were involved: Faculty of Data Science ofShiga University, Department of Statistical Science of the Graduate University for Advanced Studies, School of Information and Data Science ofNagasaki University, School of Social Information Sciences of University of
Hyogo, Hitotsubashi University, and Rissho University, the latter two of whichwere planning to open data science faculties in the future. In November 2020,
Gunma University, which was planning to establish a data science faculty, alsojoined. The Liaison Committee promotes education and research in the ﬁeldof data science at universities and graduate schools through ongoing discussions on education, research, and management, while promoting direct dialog
between industry, government, and academia, and issuing joint statement ondata science education.
The Faculty of Data Science of Shiga University is developing a textbook
series in cooperation with the Inter-university Consortium for Mathematics
and Data Science Education and another by itself. The former includes multimedia data analytics and data science mathematics, and the latter includes
social research and data visualization. The most introductory textbook of
the latter series, “Introduction to Data Science,” has been used in almost 30
Japanese universities and become a new classic in this ﬁeld. The Korean translation was published in 2020, and a version customized for Nagasaki University
is in preparation. This wide acceptance indicates that our textbooks meet thedemands of universities in Japan and Asia-Paciﬁc countries.
Data science education at Shiga University is attracting interest fromnot only universities but also high schools all over the country. Accordingly,
Shiga University has entered into cooperative agreements to provide researchguidance and teaching materials to high schools in Shiga, Hyogo, Kagawa,and Shimane Prefectures, most of which are Super Science Highschools, i.e.,
government-chartered high schools. Among these, Kanonji Daiichi High SchoolSpringer Nature 2021 LATEX template
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Shiga University’s endeavor in data science education in Japanin Kagawa Prefecture has been designated as a priority quota school and holds
a national statistics meeting every year. We are also collaborating with theprivate Mukogawa Women’s University Junior High School and High School
to contribute to a gender-equal society.
In the Faculty of Data Science, we encourage students to participate ininternships from early in their education. This is because we believe that participation in internships enables students to consider their career path more
concretely and choose their career path smoothly. In fact, the ﬁrst class ofstudents, who graduated in March 2021, has successfully found jobs. The job
oﬀer rate for university graduates in Japan was 96% in 2021, down 2.0 percentage points from the previous year because of COVID-19. The tourism andtransportation businesses and aviation and food industries have been particularly aﬀected. However, graduates from the Faculty of Data Science of Shiga
University, including those entering into these industries, were hardly aﬀected.
The percentages of graduates employed by the telecommunication, manufacturing, marketing consulting, ﬁnancial, transportation, and other industries
were 41%, 26%, 11%, 4%, 4%, and 14%, respectively (students who moved on
to graduate school are excluded). These ﬁgures indicate the demand for data
scientists across a wide range of industries.

4.2 Collaboration with industry
A potentially huge demand is seen from companies to apply the basic research
conducted in universities and research institutes to their own business. Because
many companies still lack data science personnel and experience, our faculty
members are supporting the use of data science in companies in various ways,
such as through joint research. In this way, we are expanding the range of applications in companies and the career paths of our graduates. The employees arelearning data science while solving problems in the workplace not only throughjoint research, but also by studying in our graduate school. Conversely, companies give lectures to our students and provide us with data and assignments
for exercises and PBL practices.
In 2017, the faculty members of Shiga University founded the General
Incorporated Association Omi Data Science Initiative (President: Akimichi
Takemura) to promote the advancement of data science technology throughindustry–academia collaboration. In July 2020, this association and Shiga University jointly established the Shiga University Data Science Collaboration
Consortium. The consortium, which consists of students and faculty membersof Shiga University, as well as researchers from other universities, provides avenue for mutual exchange among students, faculty members, and companies,
as well as information on data science education and research.

5 SummaryIn this paper, we have described the Japanese government’s policy on data
science education, the establishment of the Faculty of Data Science of Shiga

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University, its curriculum, and its collaboration with industry and other universities. Our endeavor to promote data science in Japan has been bearing
fruit, thereby raising the standard of data science in Japan. Stimulated by the
success of the Faculty of Data Science of Shiga University, several universities
in Japan have established faculties specializing in data science. However, Shiga
University is still a front-runner in both data science education and collaboration with industry. Shiga University will continue its eﬀorts to promote data
science education in Japan and communicate it internationally.
Acknowledgments. The authors thank Professor Akimichi Takemura forhis careful reading of the manuscript and helpful comments. This work was
supported by JSPS KAKENHI Grant Number JP18H04092. On behalf of all
authors, the corresponding author states that there is no conﬂict of interest.

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