2025 (Current Year) Faculty Courses School of Engineering Undergraduate major in Industrial Engineering and Economics
Data Analysis for Industrial Engineering and Economics
- Academic unit or major
- Undergraduate major in Industrial Engineering and Economics
- Instructor(s)
- Kazuhide Nakata / Ken Kobayashi
- Class Format
- Lecture/Exercise (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Tue / 5-6 Fri
- Class
- -
- Course Code
- IEE.B337
- Number of credits
- 110
- Course offered
- 2025
- Offered quarter
- 2Q
- Syllabus updated
- Mar 19, 2025
- Language
- Japanese
Syllabus
Course overview and goals
Recently, data analysis often appear in various aspects in economics and industrial engineering.
In this course, the instructor will explain fundamental theory and various models of data analysis, while also touching on their connection to economics and industrial engineering.
Knowledge related to data analysis is necessary for approaching various problems in economics and industrial engineering from a mathematical standpoint.
We would like students to acquire such knowledge through this course.
Course description and aims
Students in this course will learn the following for the data analysis discussed in the lecture.
(1) Gain an understanding of and be able to explain models dealt with in each analysis.
(2) Gain an understanding of the structure and various properties in each analysis, and be able to explain in mathematical language.
(3) Learn to actually calculate each analysis.
(4) Gain an understanding of and be able to explain the links been economics and industrial engineering and each analysis.
Keywords
Data Analysis
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
The instructor will cover various problems in each class, and explain the structure of solutions, how the solutions are found, as well as their connection to economics and industrial engineering.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction | explain the goal of this lecture. |
| Class 2 | Supervised learning 1 | understand Basic theory |
| Class 3 | Supervised learning 2 | understand Basic theory |
| Class 4 | Preparation for exercises | Be able to perform exercises using a PC |
| Class 5 | Linear model 1 | understand linear model |
| Class 6 | Linear model 2, k-nearest neighbor method | understand linear model and k-nearest neighbor method |
| Class 7 | Exercises on linear regression, classification and k-nearest neighbor method | Be able to use linear regression, classification and k-nearest neighbor method to analyze data |
| Class 8 | Support Vector Machines | understand support vector machines |
| Class 9 | Neural networks | understand neural networks |
| Class 10 | Exercises on support vector machines and neural networks | Be able to use support vector machines and neural networks to analyze data |
| Class 11 | Clustering | understand clustering |
| Class 12 | Feature detection | understand feature detection |
| Class 13 | Exercises on clustering and feature extraction | Be able to use clustering and feature extraction to analyze data |
| Class 14 | Data analysis | Be able to analyze data |
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.
Handouts will be distributed at the beginning of each class.
Reference books, course materials, etc.
None.
Evaluation methods and criteria
Students' course scores are based on quizzes and reports.
Related courses
- IEE.A207 ： Computer Programming (Industrial Engineering and Economics)
- IEE.A204 ： Probability for Industrial Engineering and Economics
Prerequisites
It is desirable to enroll the following course: Computer Programming (Industrial Engineering and Economics), Probability for Industrial Engineering Economics.