This course is designed for students to understand the outline of Finance and to consider the possibility to utilize Data science in Finance. The lecturers will explain broad pictures and recent trends of the topic in each class, as shown below.
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.
FinTech, Data-Science, algorithm, Artificial-Intelligence, Big-Data, Economic-Indicator
|✔ Specialist skills||✔ Intercultural skills||✔ Communication skills||✔ Critical thinking skills||✔ Practical and/or problem-solving skills|
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|
|Class 1||Introduction||To grasp broad picture of Utilization of Technology in Finance|
|Class 2||FinTech and Finance Business||To understand functions and products of Finance|
|Class 3||Finance/Economic Analysis||To review general knowledge of Data and theory in Finance/Economic Analysis|
|Class 4||Technology to sustain Financial functions||To review general knowledge of technologies which sustain Financial functions|
|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|
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.
Materials will be provided on OCW-i in advance and projected in the class room
Attendance, Summary-sheet, and Report at the end of the course will be considered
This lecture is supported by Daiwa Institute of Research Ltd.