Hiroshima University Syllabus

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Academic Year 2024Year School/Graduate School Common Graduate Courses (Master’s Course)
Lecture Code 8E500103 Subject Classification Common Graduate Courses
Subject Name データリテラシー
Subject Name
Subject Name in
Data Literacy
Instructor MONDEN REI
モンデン レイ
Campus Higashi-Hiroshima Semester/Term 1st-Year,  Second Semester,  3Term
Days, Periods, and Classrooms (3T) Mon9-10:IMC-Main 2F PC Rm
Lesson Style Lecture Lesson Style
(More Details)
Lecture using power point and blackboard, practical training, Moodle 
Credits 1.0 Class Hours/Week   Language of Instruction J : Japanese
Course Level 5 : Graduate Basic
Course Area(Area) 25 : Science and Technology
Course Area(Discipline) 02 : Information Science
Eligible Students
Keywords Statistical inference, Machine learning, R 
Special Subject for Teacher Education   Special Subject  
Class Status
within Educational
(Applicable only to targeted subjects for undergraduate students)
This course is one of the elective subjects in the category of "Career Development and Data Literacy Courses" for Common Graduate Courses. This category of courses aims to provide opportunities for students to learn about the development of the current social systems, to gain knowledge needed for the future, to concretely tackle the challenges facing modern society, and to acquire the ability to utilize knowledge and skills. 
Criterion referenced
(Applicable only to targeted subjects for undergraduate students)
Class Objectives
/Class Outline
We study statistical inference and machine learning for the first step of data science through exercises using R. 
Class Schedule lesson1 (Statistical inference) Data reading
lesson2 (Statistical inference) Studying the shape of data distribution
lesson3 (Statistical inference) Data filing and searching
lesson4 (Statistical inference) Hypothesis testing
lesson5 (Machine learning) Unsupervised learning 1 (principal component analysis)
lesson6 (Machine learning) Unsupervised learning 2 (cluster analysis)
lesson7 (Machine learning) Supervised learning 1 (multiple regression analysis)
lesson8 (Machine learning) Supervised learning 2 (neural network) 
Not specified 
PC or AV used in
(More Details) Power-point, PC 
Learning techniques to be incorporated  
Suggestions on
Preparation and
Please do not hesitate to ask a question if you have dubious points. 
Requirements This lecture is for very beginners who have never used R before. However, the lecture assumes that you understand basic PC skills such as saving files, downloading data from the web cite, and changing and saving Excel files. If you have experience in R, this lecture will be too basic. Thus not recommended.
Although this lecture is for very beginners, it is not easy to learn the skills instantly and we expect that you patiently work on programming. We will use RStudio Cloud so that both Windows and Mac computers can be used in the same way. This lecture will be given only on the face-to-face lecture format. 
Grading Method Tasks and Report 
Practical Experience  
Summary of Practical Experience and Class Contents based on it  
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. 
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