Machine learning × data science
Tomoki Yanagishita, 4th year student School of Political Science and Economics
The intuition developed through data science led to a smooth understanding of economics
I usually major in machine learning at the Faculty of School of Political Science and Economics. In the world of shogi and chess, battles between famous players and AI are a hot topic, and this is the theme of this study. Specifically, I have the AI play the puzzle game "Puyo Puyo." I chose "Puyo Puyo" when I was thinking of a theme for a research project with a friend, and remembered that an acquaintance of mine liked "Puyo Puyo," so I suggested it to him.
Since I was in junior high and high school, I was interested in doing something using programming, such as data science, and I attended programming classes and developed Android apps. However, I had a strong impression that "data science is not a goal, but merely a tool," so when I was choosing a university, I didn't think about choosing a department that specialized in data science. I thought that when I learned various things at university and encountered something that interested me, data science would help me delve deeper into it.
There are two reasons why I started taking classes on data science. The first is that in an article in Waseda Weekly, someone who was actually taking the curriculum of the Data Science Education Program said that it "helped me study my major." The second reason was that the COVID-19 pandemic at the time meant that I was spending more time at home, and with the free time I had, I wanted to get serious about something. I was also attracted by the fact that all the classes were available online, and that I could create a schedule at my own pace.
What I learned in the Data Science Education Program had a strong affinity with the economics classes I had taken at my undergraduate level, and there were many times when I thought to myself, "It's good that I studied this," especially in the fields of econometrics and corporate analysis. Also, by repeatedly practicing interpreting data, I was able to sharpen my instincts for finding the key points and trends of assignments, which I think helped me complete assignments more smoothly.
In addition to regular classes, students also have the opportunity to participate in internships at IT companies.
Mr. Yanagishita says he deepened his understanding of data science from multiple angles
Lectures in data science education programs are a good opportunity for output
The most memorable data science lecture was "Data Science Practice." In the literacy-level courses "Introduction to Data Science α/β" that I took in my second year and "Introduction to Data Science γ/δ" that I took in my third year, we learned a lot of basic formulas and language, and the quizzes after the classes were mainly to check our understanding. On the other hand, the beginner's course "Data Science Practice" involved interpreting data and thinking for ourselves about what we should do to actually use it. By making full use of the knowledge I had gained up until then, I realized that I had really acquired it. I also think that the opportunity to "output and have the teacher check it," which was difficult to do when I was studying as a hobby, was a good experience.
I feel that my attitude towards data has changed in my daily life as well. For example, when I look at online news or social media, I often see rankings based on surveys. However, when I look at them objectively, many of them have problems, such as biased classification of items or old data. When I see such materials, I can now question the assumptions and ask, "Is that really true?" This is thanks to the various methods of using information I learned in the data science education program. I think that in the coming age, each individual should have solid information literacy, so in that sense, data science is worth learning.
He is a member of a local community group and recently went to a forest in Chiba Prefecture to set up a trap to catch a wild boar in order to deepen his understanding of animal damage. The group also created a card game to help children understand animal damage.
Knowledge of data science makes university learning and research more interesting
After graduation, I plan to work as an engineer at a consulting firm. I think I will have many opportunities to develop systems and conduct corporate research, so I would like to make use of the programming languages and data analysis I learned in the data science education program. I'm not sure if it will directly connect to my job, but I would like to continue learning about data utilization, such as AI, which I researched in my graduation thesis, even after I enter the workforce.
The Data Science Education Program has a curriculum that teaches basic content, so even those who think "data science seems difficult" can take it with confidence. It doesn't use difficult mathematics, so if you're interested, I don't think it's difficult. If you're unsure, I would say to give it a try and "it's sure to broaden your horizons."
I want you to experience many things at university and become interested in many things. When you come across something that you want to learn, if you have data science skills, you will be able to dig deeper and it will be more interesting. Learning is a complementary role to your undergraduate major, without becoming a goal. This style suited me.
