Department of Data Science Close-up

Taking advantage of the strengths of a comprehensive university, students will study data science related to medical and management data in collaboration with the medical and Faculty of Economics faculties at Itabashi Campus
In Department of Data Science, we use problem-solving learning methods based on the theoretical foundations of mathematics, statistics, and information engineering to develop talent who are equipped with the ability to create valuable information from data and use it in decision-making. Students will learn the appropriate methods for analyzing, utilizing, and storing big data such as management data and medical data, which is necessary for data scientists.

カリキュラム

In addition to mathematics, statistics, and information engineering theory, students will also learn the theory of artificial intelligence and programming required for big data analysis, and acquire practical problem-solving skills.

Syllabus

Department of Data Science Syllabus

Class Introduction

Programming 1 & 2
Students will learn programming, an essential technology for developing software, step by step. In this class, students will learn basic knowledge about programming and aim to develop simple software on their own. Students will mainly study Python, which is widely used in the fields of AI and machine learning, and in 2nd year 's "Practical Machine Learning" class, students will carry out exercises in analyzing big data using programs they have written themselves.

Data Science Applied Fundamentals 1 & 2
This course develops practical skills to solve problems using mathematics, data science, and AI. The goal is to acquire the basic skills to extract meaning from data and provide feedback to the field, and to use AI to solve problems, and to gain a broad perspective to apply mathematics, data science, and AI to one's own field of expertise. This course corresponds to the Ministry of Education, Culture, Sports, Science and Technology's "Mathematics, Data Science, and AI Education Program Certification System (Applied Basic Level)."

Practical Machine Learning
Students will learn the basics of machine learning and artificial intelligence, examples of their real-world applications based on big data, the underlying mathematical and statistical theories, and practical data processing involving programming. Students will acquire practical data processing techniques by repeatedly participating in exercises such as "Management Data Value Creation Exercise," which creates value from management data, and "Medical Data Value Creation Exercise," which creates value from medical data.

Image Information Processing
Image information processing is the basis for creating various systems related to images, such as image recognition and transmission. Students will understand the methods of transforming images and the methods of making them clearer or blurred, based on the mathematical foundations of each. In addition, students will learn about image processing techniques such as image transformation and spatial filtering by running actual programs.

Economics and Business Information
In this class, you will acquire the ability to solve various economic problems using IT technology and analytical methods from economics and business management. Economic informatics is an academic field that combines economics and informatics, and students learn how to predict the economy and solve problems. Business informatics utilizes computer knowledge and technology to solve problems faced by modern corporate management.

Information Technology Seminar
National examinations related to information technology include the IT Passport Examination and the Fundamental Information Technology Engineer Examination. This class focuses on the contents of the Fundamental Information Technology Engineer Examination and aims to acquire basic knowledge as an engineer regarding information technology and its applications. Previous test papers will be practiced and the results will be explained. Previous test papers are stored on an e-learning system and can be accessed online.

Grade Assessment and Credit Recognition

Grade Assessment and Credit Recognition

To earn credits

Credit system
University classes are taught on a credit system. Credits are determined based on the number of study hours, and one credit is set at 45 hours of study (15 hours of class, 15 hours of preparation, and 15 hours of review) taking into account the teaching method and educational effect of the class.

1. For each course, students will be awarded credits if they attend more than two-thirds of the required class hours and receive a grade of 60 points (C grade) or higher. For practical training, the number of hours may be increased to more than two-thirds.
2. Grades are as follows: 90 points or more = S, 80 points or more = A, 70 points or more = B, 60 points or more = C, and less than 60 points = D. Points less than 60 points (D grade) are considered a failing grade and credits will not be awarded.
3. As a general rule, credits and grades that have been transferred cannot be cancelled.
4. Separate approval from the Dean may be required. Please refer to the course guidelines for details.
5. There are no exceptions to promotion or graduation decisions.

About our GPA System

The purpose of introducing the GPA (Grade Point Average) system is to 1. create a unified standard for the faculty, 2. create a standard with excellent fairness, and 3. create a standard that is internationally accepted, and to evaluate the results of learning with an objective numerical value called GPA. This system is roughly based on the grading system adopted by many universities overseas, and is an internationalized grading system that serves as an indicator of academic ability when studying abroad, advancing to Graduate School overseas, or finding employment at a foreign company.

GPA Calculation Method

Number of credits required for graduation

• * 12 credits are required
• * Select from 5 subject categories and earn 10 or more credits

研究室

Students are studying a variety of research topics under the guidance of experienced faculty members.

Department of Data Science Laboratory