| Academic Year |
2025Year |
School/Graduate School |
Common Graduate Courses (Master’s Course) |
| Lecture Code |
8E500102 |
Subject Classification |
Common Graduate Courses |
| Subject Name |
データリテラシー |
Subject Name
（Katakana） |
データリテラシー |
Subject Name in
English |
Data Literacy |
| Instructor |
DAI YILING |
Instructor
(Katakana) |
ダイ オクリョウ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
1st-Year, First Semester, 2Term |
| Days, Periods, and Classrooms |
(2T) Mon7-8：IMC-Main 2F PC Rm |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Face-to-face, Online (simultaneous interactive) |
| Lecture using power point and practical training |
| Credits |
1.0 |
Class Hours/Week |
2 |
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, SDG_04 |
| 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 |
We study statistical inference and machine learning for the first step of data science through exercises using Python. |
| 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 and cluster analysis)
lesson6 (Machine learning) Unsupervised learning 2 (principal component analysis and cluster analysis)
lesson7 (Machine learning) Supervised learning 1 (multiple regression analysis and neural network)
lesson8 (Machine learning) Supervised learning 2 (multiple regression analysis and neural network)
The class schedule may be changed due to progress. |
Text/Reference
Books,etc. |
Because handouts are used, textbooks are not specified. |
PC or AV used in
Class,etc. |
Handouts, moodle |
| (More Details) |
Power-point, PC |
| Learning techniques to be incorporated |
Post-class Report |
Suggestions on
Preparation and
Review |
Please do not hesitate to ask a question if you have dubious points. |
| Requirements |
This is an introductory course for data science but not a course teaching Python language. Therefore, it will be easier if you have some experience in Python. However, students who have never used Python before are still encouraged to take this course if they are willing to spend more time and effort on the assignments!
We will use Google Colab so that you don't need to construct the Python environment in your local PC.
The lecture will mainly be held face-to-face and online based on special situations. |
| Grading Method |
Tasks and Report (100%) |
| 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. |