2022 Faculty Courses School of Computing Major courses
Applied Artificial Intelligence and Data Science B
- Academic unit or major
- Major courses
- Instructor(s)
- Asako Kanezaki / Yoshihiro Miyake / Katsumi Nitta / Hiroshi Nagahashi / Takao Kobayashi / Tsuguto Morioka / Hiroyuki Mitsugi / Masahiro Nakamura / Mikio Mizobata / Daisuke Seguchi / Atsuo Kato / Takeshi Okano
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Tue
- Class
- -
- Course Code
- XCO.T484
- Number of credits
- 100
- Course offered
- 2022
- Offered quarter
- 2Q
- Syllabus updated
- Jul 10, 2025
- Language
- Japanese
Syllabus
Course overview and goals
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.
Course description and aims
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.
Student learning outcomes
実務経験と講義内容との関連 (又は実践的教育内容)
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
- 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/Objectives
| Course schedule | Objectives | |
|---|---|---|
| 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 |
Study advice (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 course material.
Textbook(s)
None required
Reference books, course materials, etc.
Materials will be provided on T2SCHOLA in advance and projected in the class room
Evaluation methods and criteria
Mainly short report required in each class will be considered
Related courses
- XCO.T483 ： Applied Artificial Intelligence and Data Science A
- XCO.T485 ： Applied Artificial Intelligence and Data Science C
- XCO.T486 ： Applied Artificial Intelligence and Data Science D
Prerequisites
Students of doctoral course are required to register for Progressive Applied Artificial Intelligence and Data Science B(XCO.T688) instead of Advanced Artificial Intelligence and Data Science B (XCO.T484)
Other
This lecture is supported by Daiwa Institute of Research Ltd.