2022 Faculty Courses Liberal arts and basic science courses Basic science and technology courses
Basics of Data Science and Artificial Intelligence
- Academic unit or major
- Basic science and technology courses
- Instructor(s)
- Yoshihiro Miyake / Hiroshi Nagahashi / Katsumi Nitta / Takao Kobayashi
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- Courses specified
- Day of week/Period
(Classrooms) - 5-6 Wed (W241)
- Class
- -
- Course Code
- LAS.I131
- Number of credits
- 100
- Course offered
- 2022
- Offered quarter
- 4Q
- Syllabus updated
- Jul 10, 2025
- Language
- Japanese
Syllabus
Course overview and goals
The purpose of this course is to give the fundamentals of data science and AI to students those who wish to solve various problems utilizing data science and AI approaches. The course is based on a model curriculum for literacy-level study of data science and AI, and also provides advanced topics so that students can easily proceed to study an advanced-level course. 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.
Course description and aims
Students will be able to:
1) Acquire independently fundamental knowledge for utilizing data science and AI in their daily lives and tasks.
2) Make human-centered smart judgements, accept a certain benefit without anxiety on their own will, explain and utilize AI, when being in the situation of an AI user.
Keywords
Internet of Things, Information and Communication Technology, cloud computing, Society 5.0, personal information protection, anonymous processing information, copyright, open data, spread sheet, data cleansing, data summarization, histogram, scatter diagram, CSV(Comma Separated Value) representation,
data input/output, Python, NumPy, Matplotlib, Scikit-learn, machine learning, unsupervised learning
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: Social utilization of data and AI | Understand the utilization of data science and AI, and also learn problems newly caused due to the introduction of AI technique, direction of progress in data science, and data equity and ethics in the AI systems. |
| Class 2 | Knowledge: Considerations of data handling | Understand various kinds of information sources, and also learn fundamental data utilization by means of a table calculation software. |
| Class 3 | Fundamentals part 1: Data reading | Understand the outline of attribute representation, summarization, and visualization of collected data, and also understand built-in data structures in Python through exercises of actual short programing. |
| Class 4 | Fundamentals part 2: Data handling (character strings) | Understand the handling of a character string and a word segmentation technique in Python, and also learn how to input/output text data. |
| Class 5 | Fundamentals part 3: Data handling (CSV tables) | Learn a special module called NumPy which extends the ability of data representation in Python, and also learn the handling of a CSV table by the module. |
| Class 6 | Fundamentals part 4: Data explaining | Learn how to write a short Python program which summarizes, analyzes and visualizes the given data as an example of utilization of NumPy data structure. |
| Class 7 | Option: Practical utilization of data (unsupervised learning) | Learn the role and availability of unsupervised machine learning in problem solving through simple Python programming with Scikit-learn and NumPy modules. |
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.M102 ： Linear Algebra I / Recitation
- LAS.M106 ： Linear Algebra II
Prerequisites
None required.