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- 工学院，物質理工学院，環境・社会理工学院共通科目
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School of Environment and Society
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- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Undergraduate major in Industrial Engineering and Economics
- Instructor(s)
- Umemuro Hiroyuki
- Class Format
- Lecture / Exercise (Face-to-face)
- Media-enhanced courses
- Day/Period(Room No.)
- Tue5-8(W631)
- Group
- -
- Course number
- IEE.C305
- Credits
- 2
- Academic year
- 2022
- Offered quarter
- 3Q
- Syllabus updated
- 2022/9/1
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
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.
Student learning outcomes
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.
Course taught by instructors with work experience
| ✔ Applicable | How instructors' work experience benefits the course |
|---|---|
| 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 that will be developed
| ✔ 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/Required learning
| Course schedule | Required learning | |
|---|---|---|
| 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 | Learn and experience methods for viewing overview of data through descriptive statistics, histograms, and plots. |
| Class 6 | Comparison, correlation, and regression | Learn and experience methods for investigating multiple data, through t-tests, correlation analysis, and regression analysis. |
| Class 7 | Factor analysis and discriminant analysis | Learn and experience factor analysis and discriminant analysis. |
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)
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.
Assessment criteria and methods
Exercise: 70%
Final Report: 30%
Related courses
- IEE.C202 ： Industrial Engineering
- IEE.C302 ： Quality Management
- IEE.B207 ： Econometrics I
- IEE.A205 ： Statistics for Industrial Engineering and Economics
Prerequisites (i.e., required knowledge, skills, courses, etc.)
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.aa[at]m.titech.ac.jp
