2019 Data Collection and Analysis

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Academic unit or major
Undergraduate major in Industrial Engineering and Economics
Umemuro Hiroyuki  Urakami Jacqueline 
Class Format
Lecture / Exercise     
Media-enhanced courses
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Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
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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.


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.
Class 8 Presentation Present outcomes of individual data analysis.


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%
Presentation: 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.
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

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