Hiroshima University Syllabus

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Academic Year 2024Year School/Graduate School Common Graduate Courses (Master’s Course)
Lecture Code 8E500107 Subject Classification Common Graduate Courses
Subject Name Data Literacy
Subject Name
Subject Name in
Data Literacy
ナヌッシュ テンデイル ジョージ
Campus Higashi-Hiroshima Semester/Term 1st-Year,  Second Semester,  3Term
Days, Periods, and Classrooms (3T) Tues9-10:IAS K103
Lesson Style Lecture Lesson Style
(More Details)
Credits 1.0 Class Hours/Week   Language of Instruction E : English
Course Level 5 : Graduate Basic
Course Area(Area) 25 : Science and Technology
Course Area(Discipline) 02 : Information Science
Eligible Students
Keywords Estimation, hypothesis testing, power analysis, meta-analysis, data visualization, 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
Provide a deep understanding of basic descriptive and inferential tools. Promote correct use of statistics and correct interpretation of statistical results. Promote transparency and replicability. Use R to analyze data. 
Class Schedule Lesson 1 - Classical inference: Estimation, hypothesis testing
Lesson 2 - Bayesian inference: Estimation
Lesson 3 - Bayesian inference: Hypothesis testing
Lesson 4 - Practical lab
Lesson 5 - Power Analysis
Lesson 6 - Meta-analysis
Lesson 7 - Data visualization
Lesson 8 - Practical lab 
No textbook required. Papers, blogs, etc will be suggested, all of which are available free of charge. To be provided throughout the course. 
PC or AV used in
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Learning techniques to be incorporated  
Suggestions on
Preparation and
It is best to work regularly. Follow the lectures sequence. Some minimum working knowledge of R is to be expected, but it is possible to learn some basic R code during the course. Reproduce all examples discussed in the lectures on your own. Work carefully through the assignments.  
Requirements Basic knowledge of R. 
Grading Method Attendance and assignments. 
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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