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 (WL2-401(W641)) / 5-6 Fri (WL2-401(W641))
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.