2025 (Current Year) Special graduate degree programs Specially Offered Degree Programs for Graduate Students Center of Data Science and Artificial Intelligence 2
Basics and Applications of Data Science and Artificial Intelligence I
- Academic unit or major
- Center of Data Science and Artificial Intelligence 2
- Instructor(s)
- Kei Miyazaki / Katsumi Nitta / Norio Tomii / Keiji Okumura / Jun Sakuma / Isao Ono / Yoshihiro Miyake
- Class Format
- Lecture/Exercise (HyFlex)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Wed
- Class
- -
- Course Code
- DSA.B201
- Number of credits
- 0.50.50
- Course offered
- 2025
- Offered quarter
- 1Q
- Syllabus updated
- Mar 19, 2025
- Language
- Japanese
Syllabus
Course overview and goals
This course gives basic theories, methods, and algorithms of data science, data engineering, and AI to students those who have finished a literacy-level course study of data science and AI and wish to study a higher-level course. The curriculum is designed so that it provides an intermediate-level course study of data science and AI between literacy- and expert-level ones. The course would enable students to understand theories and methods deeply and achieve practical skills in problem solving through a variety of examples and exercises. Students are strongly recommended to successfully complete both the courses "Basics and Applications of Data Science and Artificial Intelligence I and II".
Course description and aims
Students will be able to:
1) Understand significance of studying data science, as well as data analysis methods, and choose appropriate data analysis and visualization methods.
2) Understand roles of data engineering, representation methods of various data on a computer, and data acquisition/processing/accumulation techniques.
3) Understand history of AI, its technical background, AI ethics, machine learning and learning algorithms, neural networks and deep learning algorithms, and apply AI technology to problem solving.
Keywords
Data-driven society, big data, data structure, database, Python, representative values, correlation, variance, probability distribution, normal distribution, random number, linear regression, least-squares method
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
To check students’ understanding, students are assigned exercises at every class.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction to data science and AI | Learn fundamentals of data science and AI, and also understand their histories and roles. |
| Class 2 | Fundamentals of data engineering | Learn techniques of data acquisition, data processing, and data accumulation, and also understand representations of various data on computer. |
| Class 3 | Python tools for data science and AI, part 1: Libraries | Understand basics of Python programming language and useful libraries such as NumPy, SciPy, and matplotlib. |
| Class 4 | Python tools for data science and AI, part 2 | Learn how to use functions, classes and methods in the Python language and utilize the Python/pandas library, powerful and flexible tool for data analysis and manipulation, with using open data. |
| Class 5 | Fundamentals of mathematics for data science and AI | Learn basic knowledge of mathematics and the fundamentals of probability required for utilizing data science and AI. |
| Class 6 | Fundamentals of data science, part 1 | Learn probability distributions widely used in data analysis, and also learn random numbers. |
| Class 7 | Fundamentals of data science, part 2 | Understand data analysis process and also learn data analysis methods such as linear regression and multiple regression. |
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)
None. Lecture materials and exercise materials will be given in the class.
Reference books, course materials, etc.
Lecture materials and exercise materials will be found on Science Tokyo LMS in advance.
Evaluation methods and criteria
Grading is based on exercises and term-end report.
Related courses
- LAS.I111 ： Information Literacy I
- LAS.I112 ： Information Literacy II
- LAS.I121 ： Computer Science I
- LAS.I122 ： Computer Science II
- LAS.I131 ： Basics of Data Science and Artificial Intelligence
- LAS.M101 ： Calculus I / Recitation
- LAS.M102 ： Linear Algebra I / Recitation
- LAS.M105 ： Calculus II
- LAS.M106 ： Linear Algebra II
Prerequisites
Students are assumed to have the knowledge given in Calculus I and II, Linear Algebra I and II, and Computer Science I.
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
MIYAZAKI, Kei (lecture_ba_2025[at]dsai.isct.ac.jp)
Office hours
The instructor and TAs will accept questions in person or via Zoom immediately after the lecture. After the lecture, questions should be asked via e-mail.
Other
Science and engineering fields students will attend in person, while medical and dental fields students will participate live via Zoom.