The purpose of this class course is to understand the current status and state-of-the-art of social implementation of AI and data science technologies, and to examine the applicability and challenges of these technologies. In each class, lecturers from companies in various fields such as architecture, IT, finance, and materials will introduce case studies of technology and product development using data science and AI.
The goal is for students to gain a broad perspective of the real world by acquiring knowledge about the application of data science and AI technologies in a wide range of fields, and by explaining their considerations about social applications in their assigned reports.
This course aims to develop ability of each student to be more successful in the real world with the consideration of social implementation of data science and artificial intelligence.
✔ Applicable | How instructors' work experience benefits the course |
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This lecture will be given by lecturers from companies such as Kawasaki Heavy Industries Ltd., TeamLab Inc., DIC Corp.,The Bank of Tokyo-Mitsubishi UFJ ltd., Takenaka Corp., Electronic Power Development Co. Ltd. Please note that the companies giving the lectures are subject to change. |
Data utilization, big data, machine learning, artificial intelligence, data science, heavy industries, digital art, materials, finance, construction, electric power
✔ Specialist skills | Intercultural skills | Communication skills | ✔ Critical thinking skills | ✔ Practical and/or problem-solving skills |
This course is classified as a high-flex type, but can only be taken in designated classrooms in Ookayama and Suzukakedai.
Course schedule | Required learning | |
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Class 1 | Case Studies of Data Science and AI Implementations in the heavy industries | Understanding Data Science and AI Implementation Case Studies in the heavy industries |
Class 2 | Data Science and AI Application Cases in Digital Art (1) | Understanding Data Science and AI Applications in Digital Art |
Class 3 | Data Science and AI Application Cases in Digital Art (2) | Understanding Data Science and AI Applications in Digital Art |
Class 4 | Application Examples of Data Science and AI in Chemical Company | Understanding Data Science and AI Applications in Chemical Companies |
Class 5 | Applications of Data Science and AI in Financial Companies | Understanding Data Science and AI Applications in Financial Companies |
Class 6 | Application of Data Science and AI in a Construction Company | Understanding Data Science and AI Applications in Construction Companies |
Class 7 | Application of Data Science and AI in Electric Power Companies | Understanding Data Science and AI Applications in Electric Power Companies |
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.
None required.
No final exam will be given. The evaluation will be based on the reports of each assignment.
Reports at the end of each class and a term-end report will be considered
Only students of doctor curse are acceptable. Other students must take DSA.P431 " Applied Practical Data Science and AI 3A" instead of this course.
Asako Kanezaki, Katsumi Nitta, Norio Tomii
lecture_ap[at]dsai.titech.ac.jp
Contact by e-mail in advance to schedule an appointment.
・This class is a technical course that can be considered an entrepreneurship course. The GAs that this subject corresponds to are GA0M and GA1M (added March 29, 2024).
・This syllabus may be revised before the start of the third quarter.
・This course corresponds to Progressive Applied AI and Data Science A (XCO.T687), which was offered until FY2023. Students who took Progressive Applied AI and Data Science A may not register for this course.