2024 Faculty Courses School of Computing Major courses
Fundamentals of Data Science
- Academic unit or major
- Major courses
- Instructor(s)
- Kei Miyazaki / Norio Tomii / Kengo Sato / Atsushi Ishikawa / Sergei Manzhos / Tsuyoshi Murata / Katsumi Nitta / Yoshihiro Miyake / Isao Ono
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Tue
- Class
- -
- Course Code
- XCO.T487
- Number of credits
- 100
- Course offered
- 2024
- Offered quarter
- 4Q
- Syllabus updated
- Mar 14, 2025
- Language
- Japanese
Syllabus
Course overview and goals
In the current society, it is essential in all fields to appropriately exploit "big data" for finding rules and/or making predictions/decisions. This course gives fundamental knowledges and basic skills for handling large-scale data sets with the aid of computers.
Course description and aims
Students will be able to apply basic knowledges on statistics for analyzing data and evaluating the obtained results mathematically.
Keywords
classification, clustering, principal component analysis, dimension reduction, training/generalization errors, cross validation
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Lectures are given by Zoom.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Class guidance | Guidance for class flow, computing environment, and programming language (Python) |
| Class 2 | Fundamentas of data analysis | Learn basic knowledge about statistics and data science |
| Class 3 | Classification and model evaluation | Learn methods for extracting discrimination rules from labeled data. Learn about difference between training error and generalization error, and methods of model evaluation. |
| Class 4 | Clustering | Learn methods for categorizing unlabeled data into several categories |
| Class 5 | Principal component analysis | Learn principal component analysis together with mathematical issues related to it |
| Class 6 | Dimension reduction | Learn methods for dimension reduction such as canonical correlation analysis and graph embedding |
| Class 7 | Advanced topics | Learn methods for ensemble learning |
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)
Not specified.
Reference books, course materials, etc.
Distributed via T2SCHOLA.
Evaluation methods and criteria
Based on quizzes in class/reports.
Related courses
- XCO.T488 ： Exercises in Fundamentals of Data Science
- XCO.T483 ： Applied Artificial Intelligence and Data Science A
- XCO.T484 ： Applied Artificial Intelligence and Data Science B
- XCO.T485 ： Applied Artificial Intelligence and Data Science C
- XCO.T486 ： Applied Artificial Intelligence and Data Science D
- XCO.T489 ： Fundamentals of Artificial Intelligence
- XCO.T490 ： Exercises in Fundamentals of Artificial Intelligence
Prerequisites
Basic knowledge of linear algebra, differential and integral calculus, and mathematical statistics is required.
Students of the doctor course are required to register XCO.T677"Funfamentals of progressive data science" instead of XCO.T487"Fundamantals of data science."