Internship A（DS＆AI） 1

Academic unit or major

Center of Data Science and Artificial Intelligence

Instructor(s)

Katsumi Nitta / Norio Tomii / Kei Miyazaki / Keiji Okumura / Isao Ono / Yoshihiro Miyake

Class Format

Experiment

Media-enhanced courses

-

Day of week/Period
(Classrooms)

Internship

Class

1

Course Code

DSA.C401

Number of credits

001

Course offered

2025

Offered quarter

1Q

Syllabus updated

Mar 19, 2025

Language

Japanese

Syllabus

Course overview and goals

This lecture is not a usual lecture in the classroom, but aims to cultivate the ability to solve problems by utilizing knowledge of data science and AI in practice through experience in the field of an actual company.

Course description and aims

Through internships at companies, students will gain an understanding of the knowledge and skills of data science and AI required by society and their practical application.

Keywords

Data Science, Artificial Intelligence, Internship

Competencies

• Specialist skills
• Intercultural skills
• Communication skills
• Critical thinking skills
• Practical and/or problem-solving skills

Class flow

Students who wish to enroll in the program should proceed as follows
(1) Obtain approval for participation in the internship training from the supervising professor of the institute in advance.
(2) After gathering information at the DS&AI Forum held twice a year, decide on an internship site and conduct the internship mainly during long vacations.
(3) After consulting with the training site, prepare an implementation plan suitable for DS/AI training of at least 80 hours of actual work in principle, and obtain approval from the Center for Data Science and AI Education.
(4) Registration must be done after the implementation plan has been approved and the student has decided to participate in the internship.
(5) Participate in the practical training.
(6) After the completion of the practical training, prepare and submit an implementation report and report the results of the practical training at the results debriefing session.

Please be sure to check the following internship course outline details before enrolling in the course.
Link to DS&AI Internship Course Outline (Limited to the campus network, https://www.dsai.titech.ac.jp/limited/limited-2386/)

Course schedule/Objectives

Study advice (preparation and review)

Textbook(s)

None

Reference books, course materials, etc.

None

Evaluation methods and criteria

After completing the internship training, the students will submit a report on the activities and give an oral presentation on the activities and results. The results of the evaluation will be based on the content of the internship report and the oral presentation, as well as the evaluation of the company where the trainee, if any, to determine whether the student has passed or failed.
Since a debriefing session is held after the end of the training and grades are evaluated, the disclosure of grades may be delayed.

Related courses

• DSA.P411 ： Applied Practical Data Science and Artificial Intelligence 1A
• DSA.P412 ： Applied Practical Data Science and Artificial Intelligence 1B
• DSA.P413 ： Applied Practical Data Science and Artificial Intelligence 1C
• DSA.P421 ： Applied Practical Data Science and Artificial Intelligence 2A
• DSA.P422 ： Applied Practical Data Science and Artificial Intelligence 2B
• DSA.P423 ： Applied Practical Data Science and Artificial Intelligence 2C
• DSA.P431 ： Applied Practical Data Science and Artificial Intelligence 3A
• DSA.P432 ： Applied Practical Data Science and Artificial Intelligence 3B
• DSA.P433 ： Applied Practical Data Science and Artificial Intelligence 3C

Prerequisites

It is desirable to have taken the above-mentioned related courses and to have knowledge of data science and AI, such as programming.
The student must have the consent of his/her supervisor at his/her home institution to be away from his/her research activities during the internship period. Information about the internship course will be introduced at the DS&AI Forum. Those who wish to take this course are encouraged to attend the forum.
Students who wish to take this course must have insurance such as Gakkensai (Accident Insurance for Student Education and Research) and Gakkensai Liability Insurance (Gakkensai CALI).
The student must understand and comply with the terms of the contract between the internship host company and the university, including the obligation to maintain confidentiality.
Since grades are evaluated based on the results of the implementation debriefing session after the completion of the internship, the release of grades may be delayed, and students should adjust their internship schedule in consideration of the relationship with their own study schedule.

Other

This class is a technical course that can be considered an entrepreneurship course. The GAs that this subject corresponds to is GA1M