2021 Faculty Courses School of Computing Major courses
Practical Artificial Intelligence and Data Science C 1
- Academic unit or major
- Major courses
- Instructor(s)
- Tsuyoshi Murata / Katsumi Nitta / Takao Kobayashi / Hiroshi Nagahashi / Hiroshi Masumoto / Nobuyuki Koyama / Shun Zhenming / Naoki Nishimura / Shusaku Yoshizumi / Mamoru Inoue / Isaac Okada / Hirohito Okuda
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Fri
- Class
- 1
- Course Code
- XCO.T495
- Number of credits
- 100
- Course offered
- 2021
- Offered quarter
- 3Q
- Syllabus updated
- Jul 10, 2025
- Language
- Japanese
Syllabus
Course overview and goals
The purpose of this course is to understand the current status of social implementation of AI and data science technologies and cutting-edge technologies, and to examine the applicability and challenges of these technologies. Trends and issues in technology and product development in the fields of Pharmaceutical, Machine Learning, Data Utilization, New Business Development, etc. will be explained in each class as shown in the course schedule.
Course description and aims
The goal of this course is for students to acquire knowledge of AI and data science technologies in various fields, and to gain a broader perspective that will enable them to play an active role in the real world by discussing social applications and explaining new ideas in assignment reports.
Keywords
Data Science, Artificial Intelligence, Pharmaceutical, Machine Learning, Data Utilization, New Business Development
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Class1－Class7： Lectures
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | (C-1)Data science in pharmaceutical industry (1) (Daiichi Sankyo Company: Hiroshi Masumoto, Nobuyuki Koyama, Zhenming Shun) (C-2)Time series analysis using IoT data(1) (Bridgestone Corporation: Yasushi Hanatsuka, Mori Teppei ) | Instructions will be given during the lecture. |
| Class 2 | (C-1)Data science in pharmaceutical industry (2) (Daiichi Sankyo Company: Hiroshi Masumoto, Nobuyuki Koyama, Zhenming Shun) (C-2)Time series analysis using IoT data(2) (Bridgestone Corporation: Yasushi Hanatsuka, Mori Teppei) | Instructions will be given during the lecture. |
| Class 3 | (C-1)Business Application Workshop on Machine Learning and Data Utilization (1) (Recruit: Naoki Nishimura,Shusaku Yoshizumi) (C-2)Data science in real business activities (NGK Spark Plug Co., Ltd.: Kazuyuki Kimura) | Instructions will be given during the lecture. |
| Class 4 | (C-1)Business Application Workshop on Machine Learning and Data Utilization (2) (Recruit: Naoki Nishimura,Shusaku Yoshizumi ) (C-2)Examples of AI applications in the manufacturing field (manufacturing industry) (Furukawa Electric Co., Ltd.: Takehiko Nomura) | Instructions will be given during the lecture. |
| Class 5 | (C-1)Introduction to New Business Development (NEC Corporation: Mamoru Inoue) (C-2)Applications of AI and data science in the pharmaceutical industry (Eisai Co. ltd.: Ryo Dairiki) | Instructions will be given during the lecture. |
| Class 6 | (C-1)"After Corona" x "DX/AI" x "Human Resource Development" (Fujitsu Corporation: Isaac Okada) (C-2)The future of semiconductors created by AI and data science (Tokyo Electron Limited: Tsuyoshi Moriya) | Instructions will be given during the lecture. |
| Class 7 | (C-1)What Skills and Abilities Are Required for Corporate AI Engineers to Achieve Success in Product Development (Konica Minolta: Hirohito Okuda) (C-2)New Trend of SDGs・ESG finance (Sumitomo Mitsui Trust Bank, Ltd.: Tsukasa Kanai) | Instructions will be given during the lecture. |
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 textbooks and other course material.
Textbook(s)
none
Reference books, course materials, etc.
Materials will be provided on T2SCHOLA in advance and shared in the Zoom lecture
Evaluation methods and criteria
Mainly short report required in each class will be considered
Related courses
- XCO.T487 ： Fundamentals of data science
- XCO.T488 ： Exercises in fundamentals of data science
- XCO.T489 ： Fundamentals of artificial intelligence
- XCO.T490 ： Exercises in fundamentals of artificial intelligence
- XCO.T483 ： Advanced Artificial Intelligence and Data Science A
- XCO.T484 ： Advanced Artificial Intelligence and Data Science B
- XCO.T485 ： Advanced Artificial Intelligence and Data Science C
- XCO.T486 ： Advanced Artificial Intelligence and Data Science D
Prerequisites
Both credits of Practical Artificial Intelligence and Data Science C-1 and C-2 cannot be obtained. Priority may be given to students enrolled in the Progressive Graduate Minor in Data Science and Artificial Intelligence.
Other
Slide distribution and report acceptance will be done by T2SCHOLA. For more information, please refer to the following site.
http://www.dsai.titech.ac.jp/jissen.html