▶Japanese
Post to SNS
- Home
- > School of Computing
- > School of Computing
- > Progressive Applied Artificial Intelligence and Data Science B
- 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
- School of Computing
- Instructor(s)
- Kanezaki Asako Miyake Yoshihiro Nitta Katsumi Nagahashi Hiroshi Kobayashi Takao Morioka Tsuguto Mitsugi Hiroyuki Nakamura Masahiro Mizobata Mikio Seguchi Daisuke Kato Atsuo Okano Takeshi
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- Day/Period(Room No.)
- Tue7-8()
- Group
- -
- Course number
- XCO.T688
- Credits
- 1
- Academic year
- 2022
- Offered quarter
- 2Q
- Syllabus updated
- 2022/4/4
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
This course is designed for students to understand the outline of Finance and to consider the possibility to utilize Technology in Finance. The lecturers will explain broad pictures and recent trends of the topic in each class, as shown below.
Student learning outcomes
This course aims to develop ability of each student to be more successful in the real world with the consideration of Finance and Data science, and also through the opportunity for students to describe their own ideas.
Course taught by instructors with work experience
| ✔ Applicable | How instructors' work experience benefits the course |
|---|---|
| The lecturers of this course are engineers of Daiwa Institute of Research Ltd. |
Keywords
FinTech, Data-Science, algorithm, Artificial-Intelligence, Big-Data, Economic-Indicator
Competencies that will be developed
| ✔ Specialist skills | ✔ Intercultural skills | Communication skills | ✔ Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Class1－Class7：Lecture
This course requires students to take an active role in their own learning. It is required to submit a summary report after each class.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Introduction | To grasp broad picture of utilization of technology in finance |
| Class 2 | Finance and Data-Science | To grasp broad picture of Data-Science in finance |
| Class 3 | Financial Products and Data Analysis | To understand basics of financial products and relevant data-analysis |
| Class 4 | Finance/Economic Analysis | To review general knowledge of data and theory in finance/economic analysis |
| Class 5 | Market Transaction and Market Data | To understand transactions in the market and market data |
| Class 6 | Financial Services and Customer Data | To grasp broad picture of financial services for customers and data of customer services |
| Class 7 | Foresight of FinTech and Data-Science | To consider the foreseeable future of FinTech and data-science |
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)
None
Reference books, course materials, etc.
Materials will be provided on T2SCHOLA in advance and projected in the Zoom lecture
Assessment criteria and methods
Attendance, Summary-sheet, and Report at the end of the course will be considered
Related courses
- XCO.T687 ： Progressive Applied Artificial Intelligence and Data Science A
- XCO.T689 ： Progressive Applied Artificial Intelligence and Data Science C
- XCO.T690 ： Progressive Applied Artificial Intelligence and Data Science D
Prerequisites (i.e., required knowledge, skills, courses, etc.)
This course is for doctoral course students. Other students are required to register for Advanced Artificial Intelligence and Data Science B (XCO.T484).
Other
This lecture is supported by Daiwa Institute of Research Ltd.
