2026 (Current Year) Faculty Courses School of Computing Undergraduate major in Mathematical and Computing Science
Introduction to Computer Science
- Academic unit or major
- Undergraduate major in Mathematical and Computing Science
- Instructor(s)
- Youyou Cong / Yasuhiko Minamide
- Class Format
- Lecture/Exercise (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Mon (W8E-307(W833)) / 5-8 Thu (W8E-307(W833))
- Class
- -
- Course Code
- MCS.T204
- Number of credits
- 210
- Course offered
- 2026
- Offered quarter
- 1Q
- Syllabus updated
- Mar 5, 2026
- Language
- Japanese
Syllabus
Course overview and goals
This course consists of computer science lectures, programming lectures, and programming exercises. The computer science lectures cover topics related to computers and the theory of computation. The programming lectures and exercises introduce recipes for solving problems through programming in the Scala language. The last lecture discusses ethical issues in science. This course is the starting point of all studies in the Department of Mathematical and Computing Science.
Course description and aims
Students will acquire basic knowledge and skills in computer science and programming.
Keywords
computer science, programming, scientific ethics
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
The course consists of lectures and programming exercises.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | (Programming Lecture 1) Primitive data and function definition |
Instructed in the class. |
| Class 2 | (Programming Exercise 1) Primitive data and function definition |
Instructed in the class. |
| Class 3 | (Computer Science Lecture 1) Basics of computer science |
Instructed in the class. |
| Class 4 | (Programming Lecture 2) Conditionals and algebraic data types |
Instructed in the class. |
| Class 5 | (Programming Exercise 2) Conditionals and algebraic data types |
Instructed in the class. |
| Class 6 | (Computer Science Lecture 2) How programs are executed |
Instructed in the class. |
| Class 7 | (Computer Science Lecture 3) Various data types |
Instructed in the class. |
| Class 8 | (Computer Science Exercise 3) Various data types |
Instructed in the class. |
| Class 9 | (Computer Science Lecture 3) Review test (1) |
Instructed in the class. |
| Class 10 | (Programming Lecture 4) Recursive data types |
Instructed in the class. |
| Class 11 | (Programming Exercise 4) Recursive data types |
Instructed in the class. |
| Class 12 | (Programming Lecture 5) Lists and higher-order functions |
Instructed in the class. |
| Class 13 | (Programming Exercise 5) Lists and higher-order functions |
Instructed in the class. |
| Class 14 | (Computer Science Lecture 5) Exercise on computing environments: Linux |
Instructed in the class. |
| Class 15 | (Programming Lecture 6) Sorting algorithms |
Instructed in the class. |
| Class 16 | (Programming Exercise 6) Sorting algorithms |
Instructed in the class. |
| Class 17 | (Computer Science Lecture 6) Exercise on computing environments: Windows |
Instructed in the class. |
| Class 18 | (Programming Lecture 7) Advanced forms of recursion |
Instructed in the class. |
| Class 19 | (Programming Exercise 7) Advanced forms of recursion |
Instructed in the class. |
| Class 20 | (Computer Science Lecture 6) Review test (2) |
Instructed in the class. |
| Class 21 | (Computer Science Lecture 7) Wrap-up & ethics of science |
Instructed in the class. |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
N/A
Reference books, course materials, etc.
M. Felleisen, R. Findler, M. Flatt, and S. Krishnamurthi. How to Design Programs, Second Edition. (https://htdp.org/)
Evaluation methods and criteria
Evaluation is done based on the performance on in-class quizzes (20%), programming exercises (40%), and review tests (40%).
Related courses
- LAS.I111 ： Information Literacy I
- LAS.I112 ： Information Literacy II
- LAS.I121 ： Computer Science I
- LAS.I122 ： Computer Science II
Prerequisites
Only students in Department of Mathematical and Computing Science may take this course.
Students must have successfully completed Information Literacy I and II (LAS.I111, LAS.I112) or have equivalent knowledge.