2025 (Current Year) Faculty Courses School of Engineering Undergraduate major in Industrial Engineering and Economics
Computer Programming (Industrial Engineering and Economics)
- Academic unit or major
- Undergraduate major in Industrial Engineering and Economics
- Instructor(s)
- Kazuhide Nakata
- Class Format
- Lecture/Exercise (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-8 Mon (W9-311)
- Class
- -
- Course Code
- IEE.A207
- Number of credits
- 110
- Course offered
- 2025
- Offered quarter
- 1Q
- Syllabus updated
- Mar 19, 2025
- Language
- Japanese
Syllabus
Course overview and goals
In the first half of the lecture, students learn the basic programming languages such as the control structure and functions and data types. In the second half, students experience program development through making programs to solve some exercise problems.
Through lectures and workshop experience, the course enables students to understand and acquire fundamental skills.
Course description and aims
After completing this course, students will be able to do:
computer programming
Keywords
programming, python
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Students practice programming using python.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction |
We instruct in each class |
| Class 2 | Interpreter |
We instruct in each class |
| Class 3 | Control structure |
We instruct in each class |
| Class 4 | Function |
We instruct in each class |
| Class 5 | Data structure |
We instruct in each class |
| Class 6 | Module |
We instruct in each class |
| Class 7 | Colaboratory |
We instruct in each class |
| Class 8 | Class |
We instruct in each class |
| Class 9 | PageRank |
We instruct in each class |
| Class 10 | PageRank, implementation |
We instruct in each class |
| Class 11 | Recommendation algorithm |
We instruct in each class |
| Class 12 | Recommendation algorithm, implementation |
We instruct in each class |
| Class 13 | Machine learning |
We instruct in each class |
| Class 14 | Machine learning, implementation |
We instruct in each class |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
None required
Reference books, course materials, etc.
Course materials can be found on T2SCHOLA
Evaluation methods and criteria
Students will be assessed on their understanding of python programming.
Students' course scores are based on programming code and final examination.
Related courses
- IEE.A230 ： Advanced Computer Programming
Prerequisites
This lecture is limited to students in the department of Industrial Engineering and Economics.
Other
Bring the notebook PC to lecture