| 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
（Katakana） |
データリテラシー |
Subject Name in
English |
Data Literacy |
| Instructor |
MONDEN REI |
Instructor
(Katakana) |
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
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 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) |
Text/Reference
Books,etc. |
Not specified |
PC or AV used in
Class,etc. |
|
| (More Details) |
Power-point, PC |
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
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 |
|
| 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. |