Faculty of Data Science
Department of Data Science
Opened in April 2024
In addition to the knowledge and skills of data science, we offer diverse and free learning.
We will foster human resources who will lead to the realization of a sustainable future.
- Faculty and Department Names
- Faculty of Data Science Department of Data Science
- Capacity
- 80 people (capacity 320 people)
- Training period
- 4 years
- Acquisition degree
- Bachelor of Data Science
New school building Lecture Block D
Notice
Information
-
TV commercials are being broadcast!
-
Click here for the latest information on Faculty of Data Science!
-
Lecture Block D has finally come to figure out!
-
Lecture Block D construction is progressing steadily! (October 2023)
-
"Faculty of Data Science Open Campus 2023"
-
Faculty of Data Science Open Campus 2023
-
Click here for past announcements of Faculty of Data Science Department of Data Science.
Data Science?
What is Data Science?
Today, both in Japan and abroad, and in a wide range of fields such as business, health care and welfare, and administration,
It is common sense to analyze data and constantly formulate new strategies.
To that end, "finding issues," "collecting information," "analyze," and "finding new knowledge."
You have to feed the power.
Although it is an "information science" that has a systematic theory,
Data science is an essential part of business and other activities.
Human resources who have acquired data science to solve problems through statistical thinking based on data.
The so-called "data scientists" are expected to play an active role in all fields around the world.
Sales and service industries…
We analyze customer information, purchase history, Web site browsing history, etc., and propose "products that are likely to be purchased" to users on the site.
In the medical field,…
Analyzing the vast amount of medical data accumulated at hospitals, it is useful for early detection, prevention and treatment of illness while reducing the burden on doctors and nurses as much as possible.
Human resources to be trained and Diproma policy
Personnel to be trained and Diploma Policy
The Faculty of Data Science of Shimonoseki City University develops human resources who can contribute to the creation of new value by learning theories and practices related to mathematical statistics, intelligence and social science necessary for design, analysis and utilization of diverse data. Bachelor of Data Science will be awarded to human resources (data scientists) with the following four qualities (A to D).
Through knowledge of statistics and related mathematical sciences, and the experience of analysis utilizing them, he acquires the ability to collect, organize, and analyze data and logically consider the knowledge obtained from it.
Having knowledge about algorithms such as information management and analysis and artificial intelligence, and through the experience of expressing them on computers, acquire the ability to analyze and utilize various forms of data and handle them appropriately. I have.
By learning how data analysis is performed in the field of business or healthcare, along with the knowledge unique to each field, we understand the role that data can play in society and have the necessary ethics and sense of responsibility (moral).
Appropriate communication can be carried out in cooperation with various people, and the statistical interpretation of the analysis method used and the statistical interpretation of the results can be conveyed in an easy-to-understand manner.
Characteristics of Curriculum
Features of the Curriculum
Our curriculum <educational> consists of three pillars: basic education, liberal arts education, and specialized education.
At Faculty of Data Science, Shimonoseki City University, students will learn mathematical statistics and information sciences mainly as basic subjects of data science. From the second year onwards, we will establish a group of subjects such as "Business Data Science" and "Health Data Science" as specialized application subjects, and develop practical skills. In addition, by taking foreign language education subjects, liberal arts courses, career education subjects, etc. as basic education and liberal arts subjects common to all universities, students will acquire rich culture and excellent employment skills, and utilize various data to develop data scientists who can create new value and solve various issues in the field.
Curriculum to develop practical skills through business and health.
Through lectures, exercises, and project-based learning subjects, you will acquire basic knowledge and skills in mathematics, information and programming.
Especially over the next few years, you will learn about "business data science" and "health data science" to develop specialized knowledge and practical skills.
| First Year | 2nd year | Third year | 4th year | |
|---|---|---|---|---|
|
Specialized foundations
|
Mathematics and Information Systems Acquire knowledge such as mathematics, intelligence, statistics, programming and algorithms |
|||
|
Introduction to Data Science and Technology Understand a series of data science and acquire basic skills and thinking methods. |
||||
|
Use of data analysis Acquisition a wide range of knowledge and skills related to various analytical methods |
||||
|
Exercise and Graduation Research Acquire communication skills, presentation skills, and creative thinking skills |
||||
|
Specialized application
|
Business Data Science You will learn how to use data for the management of various companies and organizations. |
|||
|
Health Data Science Learn how to use data to achieve a healthy life for people. |
||||
Examples of models taken
Basic education to nurture the basics of learning at universities and basic education to nurture morals, liberal arts education with a wide range of knowledge to play an active role in society, specialized basics for learning the basics to enhance data science, and knowledge necessary to apply in the real world Learn by combining specialized applications to learn skills. It is necessary to use a personal computer and develop classes that make use of them.
First Year's spring semester
| Month | Fire | Water | Tree | Money | |
|---|---|---|---|---|---|
| 1st term | Academic literacy | ||||
| 2nd term | Management Studies | Japanese Constitution | English | Shimonoseki's industry and Mirai | Mathematics Fundamental |
| 3rd term | Introduction to Data Science | Information Society and Ethics | Sports practice | ||
| 4th term | Literature | English | |||
| 5th term | The law of nature |
3rd year of spring semester
| Month | Fire | Water | Tree | Money | |
|---|---|---|---|---|---|
| 1st term | Machine learning | Marketing research | Categoryical data analysis exercises | ||
| 2nd term | Data marining | E.commerce theory | Sensitive data processing | ||
| 3rd term | Operations Research | ||||
| 4th term | DS Project | Statistical Social Studies Law | Quantitative data analysis exercises | ||
| 5th term |
Expected course
After graduation, it is assumed that he will use his expertise in data science to engage in planning and marketing and system engineers in a wide range of industries, or in healthcare-related work at public institutions and medical institutions.
Examples of course
- ● Manufacturing, Retailing, Advertising, and publishing
- ● IT and telecommunications industry
- ● Administration
- ● Health and Medical Institutions (including university hospitals)
- ● Research Laboratories and think tanks
- ● Pharmaceutical companies
- ● Financial institutions (banks, insurance, securities, etc.)
- ● Entering graduate school
Qualifications available
The following licenses and qualifications can be obtained by predetermined credits (selection system).
- ● Junior high school teacher type license (mathematics)
- ● High school teacher type license (mathematics)
- ● High school teacher type license (information)
- ● Social Investigators
Policy on Acceptance of Students (Admission Policy)
Admission Policy
In this faculty, based on the image of human resources to be trained, in addition to basic knowledge and skills, as well as those with independence, diverse students, and collaboration as enrollees based on the image of human resources to be trained. Therefore, the advocacy policy was set as follows.
| A | As basic knowledge and skills necessary to learn data science is knowledge of mathematics and foreign languages studied at high schools. |
|---|---|
| B | Communication skills that are interested in daily social issues as abilities such as thinking, judgment, expression skills, etc., and who are interested in daily social issues and discover solutions to think about solutions using data, as well as exercises classes etc. |
| C | Independent, diversity, and collaboration, the attitude of learning with a sense of purpose, the ability to understand different cultures and values, and a public spirit obtained through activities in local communities |
Q&As
Question and Answer
- Q
I was a literature in high school, can I learn data science?
- A
Data scientists are aimed at regardless of literature or science. In the first place, data science is a study that analyzes data and creates new value while utilizing theories such as mathematics, statistics, machine learning, and programming. To do so, it is necessary to understand society, economy, and human beings, and a literary sense is useful.
- Q
I'm not very good at mathematics. Can I keep up with my class?
- A
You need a minimum knowledge of mathematics for college exams, but you do not always need advanced math knowledge. After enrollment, you will learn the basics of mathematics related to data science as a "specialized basic subject". We are also preparing a remedial study system.
- Q
What kind of field will you be able to play an active part after graduation?
- A
Human resources who can utilize the enormous accumulated big data will be required in a wide range of fields, both public and private sectors. In particular, the university is expected to learn practically on the two pillars of "Business Data Science" and "Health Data Science", and will be active as planning, marketing analyst, SE, etc.
