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- School of Science
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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
- Graduate major in Technology and Innovation Management
- Instructor(s)
- Miyashita Shuto
- Class Format
- Lecture / Exercise
- Media-enhanced courses
- Day/Period(Room No.)
- -
- Group
- -
- Course number
- TIM.A405
- Credits
- 1
- Academic year
- 2024
- Offered quarter
- 3Q
- Syllabus updated
- 2024/3/14
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
While the use of data is highly expected in business, correct understanding about methodology of data analysis is required for appropriate decision-making.
In this lecture, students learn basic knowledge for quantitative analysis, especially statistical analysis, in Management of Technology.
Student learning outcomes
The goals of this course are as follows:
- To understand the basics of data analysis so that students can use them to solve business problems.
- To be able to program for elementary data analysis, especially statistical analysis.
Keywords
Statistics, data science, quantitative analysis, descriptive statistics, hypothesis testing, regression analysis
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Lectures provide the knowledge on elementary data analysis and programming exercises is scheduled after each topic.
In the programming exercises, students overview the flow of data analysis using Python and develop the ability applying data analysis to business problems.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Guidance | Understand the overview of data science and quantitative analysis, especially statistical analysis, in business |
| Class 2 | Descriptive statistics | Understand the basic summary statistics and data visualization methods |
| Class 3 | Programming exercise (1) | Acquire fundamental programming skills how to use Python through exercises |
| Class 4 | Hypothesis testing | Understand the procedures for hypothesis testing and what to pay attention to when applying it |
| Class 5 | Programming exercise (2) | Acquire programming skills for descriptive statistics and hypothesis testing through exercises |
| Class 6 | Regression analysis | Understand what kind of problems to be suit for regression analysis and its assumptions behind performing |
| Class 7 | Programming exercise (3) | Acquire programming skills for regression analysis through exercises |
Out-of-Class Study Time (Preparation and Review)
It is recommended to read and review the relevant sections of the references after the lecture.
In order to master programming, learning by writing code on outside of class is also recommended.
Textbook(s)
None required.
Reference books, course materials, etc.
Lecture materials will be distributed.
In addition to those listed below, references will be introduced in the lecture.
Foster Provost Tom Fawcett, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking O'Reilly Media (2013)
Assessment criteria and methods
Class contribution 20%, Exercise 30%, Report 50%
Related courses
- TIM.B412 ： Strategic Management for Research and Development I
- TIM.B413 ： Strategic Management for Research and Development II
- TIM.A414 ： Introduction to Models and Experiments in Social Science
- TIM.B535 ： Digital Marketing
- TIM.A406 ： Methodology of Mathematical and Computational Analysis II
Prerequisites (i.e., required knowledge, skills, courses, etc.)
No prerequisites.
