2020 Methodology of Mathematical and Computational Analysis I

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Academic unit or major
Graduate major in Technology and Innovation Management
Instructor(s)
Kajikawa Yuya  Nakamaru Mayuko 
Course component(s)
Lecture / Exercise    (ZOOM)
Day/Period(Room No.)
Sat5-6(CIC812)  
Group
-
Course number
TIM.A405
Credits
1
Academic year
2020
Offered quarter
1Q
Syllabus updated
2020/10/26
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

This course gives lectures on quantitative analysis theories and methods. It also illustrates several cases about technology management. Bibliometric analysis, statistical analysis, and machine learning will be learned. The aim of this course is to acquire analytical skills which are required to learn and to conduct research on technology management.
Data science and its methodology becomes fundamentals for individuals and organizations to differentiate themselves from the others in the era of big data and information flood. In this course, students will learn both methodology of data science and practical cases with exercise and group work in order to learn basic knowledge for analysis and methodology applying analysis to managerial decision and utilizing for business.

Student learning outcomes

Students will gain an understanding of the following items from taking this course.
1) Points to note and differences in techniques for quantitative analysis methods that use statistical analysis and machine learning
2) Analysis methods for non-structured data such as text analysis and network analysis
3) Handling important data for survey research related to management of technology such as articles and patents
Also, through exercises,
4) After getting a picture of global research trends, students are expected to learn the skills to plan and propose new R&D topics and strategies.

Keywords

Statistical analysis, machine learning, network analysis, text analysis, R&D planning and strategy

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Students must attend the first class because groups will be formed. With the exception of the group presentation in class 7, students will be provided in classroom lectures with the big picture, theory, methods, and issues of data science. Based on that, students will do group work and work on reports.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Guidance Understand overview and aim of this course.
Class 2 Statistical analysis. Understand methodology of data sampling and statistical analysis.
Class 3 Machine learning. Understand theory and methodology of machine learning.
Class 4 Unstructured data analysis. Understand methodology of analysis of unstructured data including text analysis and network analysis.
Class 5 Pragmatic data analysis in real business (1). Understand plausible issues when we apply data analysis to technology management.
Class 6 Practice of data analysis (1) Practice of data analysis by a tool.
Class 7 Pragmatic data analysis in real business(2). Practice of data analysis by a tool.

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)

Not assigned.

Reference books, course materials, etc.

Not assigned.

Assessment criteria and methods

Report (100%)

Related courses

  • TIM.B412 : Strategic Management for Research and Development I
  • TIM.B413 : Strategic Management for Research and Development II
  • TIM.D401 : Exercises in Research Literacy I
  • TIM.D402 : Exercises in Research Literacy II

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

No prerequisite.

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