| Academic Year |
2024Year |
School/Graduate School |
Liberal Arts Education Program |
| Lecture Code |
30106005 |
Subject Classification |
Information and Data Science Courses |
| Subject Name |
ゼロからはじめるプログラミング[1総総,1教数,1理,1情] |
Subject Name
（Katakana） |
|
Subject Name in
English |
Starting Programming from Scratch |
| Instructor |
AIZAWA HIROAKI,FURUI AKIRA |
Instructor
(Katakana) |
アイザワ ヒロアキ,フルイ アキラ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
1st-Year, Second Semester, 3Term |
| Days, Periods, and Classrooms |
(3T) Weds5-8：Online |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
|
| Course materials of videos are provided. |
| Credits |
2.0 |
Class Hours/Week |
|
Language of Instruction |
J
:
Japanese |
| Course Level |
1
:
Undergraduate Introductory
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
02
:
Information Science |
| Eligible Students |
|
| Keywords |
Computer programming, Python, Data science, AI |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status within
Liberal Arts Education | To equip students with the basic knowledge and skills necessary to use data in today’s advanced information society, develop their understanding of the usefulness and problems of computers and information-related ethical issues, so as to ensure the proper |
|---|
| Expected Outcome | 1. To acquire basic knowledge, skills, and attitudes regarding information science and data science, and thereby to be able to process data and receive and send information appropriately.
2. To understand and be able to explain information ethics necessar |
|---|
Class Objectives
/Class Outline |
The objective of this course is to learn the art of Python programming and knowledge and skills for using computers. At the same time, students are expected to improve their logical thinking and mathematical sense. We use Python programming language because it is easy to understand and recently used for AI and data science. |
| Class Schedule |
Lesson1: Introduction
Lesson2: Variables (lecture)
Lesson3: Variables (exercise)
Lesson4: Conditional statements (lecture)
Lesson5: Conditional statements (exercise)
Lesson6: Loops (lecture)
Lesson7: Loops (exercise)
Lesson8: Functions (lecture)
Lesson9: Functions (exercise)
Lesson10: Classes (lecture)
Lesson11: Classes (exercise)
Lesson12: Introduction to data analysis (lecture)
Lesson13: Introduction to data analysis (exercise)
Lesson14: Introduction to machine learning (lecture)
Lesson15: Introduction to machine learning (exercise) |
Text/Reference
Books,etc. |
Videos will be provided. |
PC or AV used in
Class,etc. |
|
| (More Details) |
Google Colaboratory will be used for Python programming, Microsoft Stream for video streaming, and Microsoft Teams for answering questions and submitting assignments. Lecture materials and announcements will be centralized on support pages on GitHub Pages. |
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
Reading and understanding many good programs would be a best way of mastering programming skills. Do not hesitate to ask instructor or TA a question during exercises. Do not just understand the contents in your head, but actually write and run the program. |
| Requirements |
A Google account is required to use Google Colaboratory. |
| Grading Method |
Based on programming assignments in the exercise sessions (excluding advanced problems) and a final exam (online). |
| 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. |