2025 (Current Year) Faculty Courses School of Engineering Undergraduate major in Industrial Engineering and Economics
Data Collection and Analysis
- Academic unit or major
- Undergraduate major in Industrial Engineering and Economics
- Instructor(s)
- Hiroyuki Umemuro
- Class Format
- Lecture/Exercise
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Class
- -
- Course Code
- IEE.C305
- Number of credits
- 110
- Course offered
- 2025
- Offered quarter
- 3Q
- Syllabus updated
- Mar 27, 2025
- Language
- Japanese
Syllabus
Course overview and goals
In many fields of Industrial Engineering and Economics, we conduct research by collecting a wide variety of data, analyzing them, and validating hypotheses. The goal of this course is to learn various methods for collection and analysis of data necessary for industrial engineering and economics research.
Each lecture is followed by an exercise for comprehension of the methods learned.
Course description and aims
By the end of this course, students are expected to:
(1) understand characteristics of various kinds of data.
(2) understand characteristics of various methods of data collection and be able to select appropriate methods depending on purposes.
(3) understand characteristics of various methods of data analysis and be able to select appropriate methods depending on purposes.
Student learning outcomes
実務経験と講義内容との関連 (又は実践的教育内容)
Professor to conduct this class has experiences on data analysis while he was working for a private company.
Keywords
qualitative data, quantitative data, statistics, multivariable analysis
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
A pair of lecture and exercise is a basic unit for this course. Knowledge and methods learned in lecture is further exercised in the following class.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction + data + interview | To understand goals, structure and grading of this course. To understand the concept and kinds of data. To understand methods of interview. |
| Class 2 | Qualitative data analysis | Learn and experience actual methods for data analysis of qualitative data. |
| Class 3 | Questionnaire | Learn and experience design of and investigation using questionnaires. |
| Class 4 | Data analysis software | Install and setup statistical analysis software. |
| Class 5 | Viewing data and Comparison | Learn and experience methods for viewing overview of data through descriptive statistics, histograms, and plots. Learn comparison of distribution using t-test. |
| Class 6 | Correlation, regression, and discriminant analysis | Learn and experience methods for investigating multiple data, through correlation analysis, regression analysis, and discriminant analysis. |
| Class 7 | Factor analysis and Structured Equation Modeling | Learn and exploratory factor analysis and structured equation modeling. |
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)
No textbook is set. Class materials are provided in the classes.
Reference books, course materials, etc.
No special references are set. Necessary information is provided in class.
Evaluation methods and criteria
Exercise: 70%
Final Report: 30%
Related courses
- IEE.C302 ： Quality Management
- IEE.B207 ： Econometrics I
- IEE.A205 ： Statistics for Industrial Engineering and Economics
- IEE.C431 ： Applied Statistical Analysis
- IEE.B337 ： Data Analysis for Industrial Engineering and Economics
Prerequisites
Students must have successfully completed both Statistics for Industrial Engineering and Economics (IEE.A205) or have equivalent knowledge.
Students must bring own laptop to be used in exercise every week.
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
Hiroyuki Umemuro
umemuro.h.f22d[at]m.isct.ac.jp