▶Japanese
Post to SNS
- Home
- > School of Engineering
- > Undergraduate major in Industrial Engineering and Economics
- > Data Collection and Analysis
- School of Science
-
School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
-
School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- 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
- Media-enhanced courses
- Day/Period(Room No.)
- Tue5-8(W631)
- Group
- -
- Course number
- IEE.C305
- Credits
- 2
- Academic year
- 2016
- Offered quarter
- 3Q
- Syllabus updated
- 2017/1/11
- Lecture notes updated
- 2016/11/21
- 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.
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 | General guidance | To understand goals, structure and grading of this course. |
| Class 2 | Lecture: variation of data | Understand characteristics of various kinds of data. |
| Class 3 | Exercise: variation of data | Be able to select appropriate types of data for research purpose. |
| Class 4 | Lecture: collection of qualitative data | Learn methods for collection of qualitative data. |
| Class 5 | Exercise: collection of qualitative data | Experience actual methods for data collection of qualitative data. |
| Class 6 | Lecture: analysis of qualitative data | Learn methods for analysis of qualitative data. |
| Class 7 | Exercise: analysis of qualitative data | Experience actual methods for data analysis of qualitative data. |
| Class 8 | Lecture: collection of quantitative data | Learn methods for collection of quantitative data. |
| Class 9 | Exercise: collection of quantitative data | Experience actual methods for data collection of quantitative data. |
| Class 10 | Lecture: Basic statistics and statistical tests | Learn about data summarizing, visualization, reviewing basic statistics, and statistical analysis. |
| Class 11 | Exercise: Basic statistics and statistical tests | Exercise methods for data processing, visualization, and statistical tests. |
| Class 12 | Lecture: correlation and regression | Learn about correlation analysis and regression. |
| Class 13 | Exercise: correlation and regression | Exercise methods of correlation analysis and regression analysis. |
| Class 14 | Lecture: Multivariable analysis | Learn various multivariable analysis methods. |
| Class 15 | Exercise: Multivariable analysis | Exercise various methods of multivariable analysis. |
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: 80%
Participation: 20%
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) and Industrial Engineering (IEE.C202) or have equivalent knowledge.
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
