Collaboration article between the School of Mechanical Engineering and Data Science
Professor Frank Chongwoo Park

Q1. What field is data science?
Data collection has become easier due to the development of computing and sensor technology, and as a much larger amount of data is generated, it is a field that studies how to utilize it. For example, by using a large amount of data obtained from factories, the efficiency and quality of factories can be improved and equipment failures can be predicted in advance.
Although it is not easy to clearly divide the fields due to the short history of data science studies, it can be broadly divided into computing-related fields that collect, analyze, and process data, and algorithmic fields such as performance prediction and optimization through machine learning.

Q2. How was the undergraduate/master's program between the School of Mechanical Engineering and the Graduate School of Data Science created?
The world is rapidly evolving around data. Many disciplines, including mechanical engineering, are developing at a rapid pace. Due to the development of computing and AI in the last 10 years, research applying them to mechanical engineering has been progressing rapidly, and there have been many studies that have shown very effective results by applying data science technology. We felt it was important to give undergraduate students the opportunity to learn, not just from graduate school.
Recognizing the importance of data science campus, we have established several undergraduate classes, such as the basics of computing and the basics of deep learning. We felt it was important for mechanical engineering students to learn data science after learning the basics and context needed in mechanical engineering. It turns out that the knowledge learned in mechanics is also highly related to constructing AI models, and learning data science based mechanical engineering will set you apart from other students.

Q3. How do you think mechanical engineering will develop in a rapidly developing world?
An engineer's goal is to create technology for humanity. Engineers need to keep up to date with the latest technologies and how to use them. The School of Mechanical Engineering is no exception.
Recently, aging, environment and clean energy are emerging. Factories have fewer workers to work with, and automated robots are becoming more important, as well as efficient clean energy is needed to preserve the environment. These goals have been around for a long time, besides data science has played an important role in getting closer to them. In the past, we had to relyly simplified machine models that performed poorly, but now we can leverage the data collected along with AI models to create more accurate models to solve problems with greater accuracy. Mechanical engineers need to know how to use the latest technology in data science.

Q4. What kind of competencies do you want graduates of data science graduate schools to have?
Knowingly data science has clear limits. Many people have the illusion that deep learning and machine learning methods can solve all problems with enough data. The reality is that the amount of data is very insufficient, and if you learn AI without basic knowledge about application problems, its performance will deteriorate. For example, robots need huge amounts of data to properly learn complex tasks, which may require incorporating methods based dynamic modeling. I want graduates to use AI correctly. I want to be equipped with the ability to efficiently solve problems by using mechanical engineering knowledge to create mechanically meaningful and systematic models and collect appropriate data.

Q5. Are there any changes to the curriculum while linking undergraduate/master's degrees?
Until the 3rd year of undergraduate studies, the curriculum is the same as that of the School of Mechanical Engineering, but in the following two years students take the 1st and 2nd year Master's classes at the Graduate School of Data Science. The goal is to write a graduation thesis by applying the knowledge of mechanical engineering and the knowledge learned in data science graduate school to real problems.

MEch-SSENGER Jaehyun Koo, Sangmin Lee