I completed the "University of Tokyo Global Consumer Intelligence (GCI) 2024 Winter" and "Deep Learning Fundamentals (DL Fundamentals) 2025 Spring" courses
From October 2024 to January 2025, I completed the University of Tokyo Global Consumer Intelligence (GCI) course (Reference: University of Tokyo Global Consumer Intelligence - Chair for Global Consumer Intelligence). Furthermore, from April to August 2025, I also managed to complete the Deep Learning Fundamentals (DL Fundamentals) course (Reference:
Deep Learning Fundamentals Course 2025 | Autumn - The University of Tokyo Matsuo-Iwasawa Laboratory (Matsuo Lab)). This time, I would like to introduce GCI a little.
What is GCI?
While I will leave the details to their website, it is a course that "learns the basics of AI through data science."
The target audience is mainly students, but it also includes working professionals, so I enrolled as well.
Moreover, it is free and fully online (no need to watch in real-time!).
Why did I take it?
Originally, I wanted to use the Education and Training Benefit System (Specialized Practical Education and Training Benefit) to learn data science, so I was looking for courses and attending information sessions.
I found the idea of being able to explain things logically with numbers interesting, and since I thought that pursuing that would lead to data science, I considered it.
However, even with the benefits, many courses were expensive, and I was hesitant. Then I discovered GCI through an Instagram ad and decided to take it (it's free!).
What is the content like?
The curriculum is as described on the website.
I hadn't even opened Python before, but introductory learning materials were distributed before the lectures started, so I managed to keep up.
Briefly, it is as follows:
Listen to a lecture of just under 2 hours every week (watching the recording is fine)
Submit surveys and homework (not every time)
In addition to homework, submit assignments (competitions, etc.) about 3 times (working on these improves your understanding)
TAs hold office hours to talk about how to approach assignments (they provide support)
You can consult with other students on Slack (I was helped a lot with homework and assignments)
Through the six months of study, I think I have acquired the basic content. I feel that I particularly gained strength through working on the assignments (in a competition format, where you build and train a machine learning model on a certain dataset and compete to see how accurately you can predict).
Effort status
During the time I was taking the course, I was waiting for the results of the Certified Public Tax Accountant exam, so I worked on graduate school assignments and my master's thesis in parallel with GCI.
My child was approaching one year old and taking fewer naps, so I allocated the 2-3 hours during nap time to these tasks.
What I spent the most time on was the assignments (competitions).
It became fun to see my score go up, and I would sometimes get absorbed in it night after night...
(From the beginning of December, when I found out I had failed the tax accountant exam, I gradually shifted to studying for that exam as well.)
What are my thoughts?
I was at a "nice to meet you, Python" level, but I reached a level where I can "proceed while researching on my own!"
I think that is because I gained the experience of trying things out for myself (not just listening to lectures) by working on homework and competitions.
It was fully online and didn't require being on time, so I was able to learn at my own pace, even though my "child's nap time" varies every day and is the only time I have for myself.
I was helped a lot by the TAs and other students.
For those interested in data science who meet the eligibility requirements, such as students, I can confidently recommend these courses!