8E500107 Data Literacy

Hiroshima University Syllabus

Japanese

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
（Katakana）データリテラシー

Subject Name in
English Data Literacy

Instructor NUNES TENDEIRO JORGE

Instructor
(Katakana) 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
Program
(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
Evaluation
(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

Text/Reference
Books,etc. 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
Class,etc. 　

(More Details)

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review 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

Message

Other

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.

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 （Katakana）	データリテラシー
Subject Name in English	Data Literacy
Instructor	NUNES TENDEIRO JORGE
Instructor (Katakana)	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 Program (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 Evaluation (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
Text/Reference Books,etc.	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 Class,etc.
(More Details)
Learning techniques to be incorporated
Suggestions on Preparation and Review	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
Message
Other
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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