2022 Faculty Courses School of Computing Common courses
Basics and Applications of Data Science and Artificial Intelligence I
- Academic unit or major
- Common courses
- Instructor(s)
- Yoshihiro Miyake / Katsumi Nitta / Hiroshi Nagahashi / Takao Kobayashi / Sho Nakajima
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Wed (W241)
- Class
- -
- Course Code
- XCO.T281
- Number of credits
- 100
- Course offered
- 2022
- Offered quarter
- 1Q
- Syllabus updated
- Jul 10, 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, database, data structure, database, annotation, Python, population, representative values, correlation, variance, probability distribution, random number
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 a quiz at the end of 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 a computer. |
| Class 3 | Python tools for data science and AI, part 1 | Learn basic mathematical knowledge and tools for data science and AI, specifically, Python programming language and useful libraries. |
| Class 4 | Python tools for data science and AI, part 2 | Understand how to utilize Python/pandas library for visual data handling and data analysis with using practical open data. |
| Class 5 | Python tools for data science and AI, part 3 | Understand how to utilize Python/scikit-learn library for machine learning by applying it to classification problems. |
| Class 6 | Fundamentals of mathematical statistics, part 1 | Understand basics of mathematical statistics such as population and samples, histogram, mean and variance, correlation, and causation. |
| Class 7 | Fundamentals of mathematical statistics, part 2 | Learn theories of mathematical statistics such as probability distribution, central limit theorem, expectation, and random numbers. |
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 will be given in the class.
Reference books, course materials, etc.
Lecture materials will be found on T2SCHOLA in advance and shared in Zoom lecture.
Evaluation methods and criteria
Grading is based on quizzes 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.M101 ： Calculus I / Recitation
- LAS.M102 ： Linear Algebra I / Recitation
- LAS.M105 ： Calculus II
- LAS.M106 ： Linear Algebra II
Prerequisites
Students must have successfully completed Information Literacy I, Information Literacy II, Computer Science I, and Computer Science II.