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2022　Data Analysis for Industrial Engineering and Economics

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Academic unit or major
Undergraduate major in Industrial Engineering and Economics
Instructor(s)
Nakata Kazuhide  Kobayashi Ken 
Class Format
Lecture / Exercise    (Face-to-face)
Media-enhanced courses
Day/Period(Room No.)
Tue5-6(W933)  Fri5-6(W933)  
Group
-
Course number
IEE.B337
Credits
2
Academic year
2022
Offered quarter
2Q
Syllabus updated
2022/3/16
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

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.

Student learning outcomes

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 that will be developed

✔ 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/Required learning

  Course schedule Required learning
Class 1 Introduction explain the goal of this lecture.
Class 2 Basic theory understand Basic theory
Class 3 Preparation for exercises Be able to perform exercises using a PC
Class 4 Linear regression and classification understand linear regression and classification
Class 5 k-nearest neighbor method understand k-nearest neighbor method
Class 6 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 7 Support Vector Machines understand support vector machines
Class 8 Neural networks understand neural networks
Class 9 Exercises on support vector machines and neural networks Be able to use support vector machines and neural networks to analyze data
Class 10 Clustering understand clustering
Class 11 Feature detection understand feature detection
Class 12 Exercises on clustering and feature extraction Be able to use clustering and feature extraction to analyze data
Class 13 Data analysis 1 Be able to analyze data
Class 14 Data analysis 2 Be able to analyze data

Out-of-Class Study Time (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.

Assessment criteria and methods

reports (100%)

Related courses

  • IEE.A207 ： Computer Programming (Industrial Engineering and Economics)
  • IEE.A204 ： Probability for Industrial Engineering and Economics

Prerequisites (i.e., required knowledge, skills, courses, etc.)

It is desirable to enroll the following course: Computer Programming (Industrial Engineering and Economics), Probability for Industrial Engineering and Economics

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