2022 Faculty Courses School of Computing Major courses
Exercises in Fundamentals of Progressive Data Science
- Academic unit or major
- Major courses
- Instructor(s)
- Duy Phuoc Tran / Haruka Tomobe / Yuki Terazawa / Maki Kishimoto / Naoki Miyazawa / Makoto Uchida / Keisuke Yanagisawa / Tsuyoshi Murata / Katsumi Nitta / Hiroshi Nagahashi / Takao Kobayashi / Yoshihiro Miyake / Isao Ono
- Class Format
- Exercise (Livestream)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Tue
- Class
- -
- Course Code
- XCO.T678
- Number of credits
- 010
- Course offered
- 2022
- Offered quarter
- 4Q
- Syllabus updated
- Jul 10, 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 aims to help students to manipulate computer software tools for data analysis to get new
findings.
Course description and aims
Students will be able to understand the basis of data processing mechanisms and make use of various data analysis software tools appropriately.
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
In class, students are required to solve exercise problems that are linked with the contents of taught course ``XCO.T677
Fundamentals of progressive data science".
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Prerequirement exam | Check basic knowledge about mathematics and Python language |
| Class 2 | Arrangement of computing environment and warming-up of programming | Arrange computing environment and carry out simple exercises of programming |
| Class 3 | Classification | Do exercises on methods for extracting discrimination rules from labeled data |
| Class 4 | Principal component analysis | Do exercises on principal component analysis with mathematical issues related to it |
| Class 5 | Clustering | Do exercises on methods for categorizing unlabeled data into several categories |
| Class 6 | Dimension reduction | Do exercises on methods for dimension reduction such as multidimensional scaling and canonical correlation analysis |
| Class 7 | Ensemble learning | Do exercises on 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)
None
Reference books, course materials, etc.
Based on reports for given assignments.
Evaluation methods and criteria
Based on reports for given assignments.
Related courses
- XCO.T677 ： Fundamentals of progressive data science
- XCO.T483 ： Advanced Artificial Intelligence and Data Science A
- XCO.T485 ： Advanced Artificial Intelligence and Data Science C
- XCO.T486 ： Advanced Artificial Intelligence and Data Science D
- XCO.T679 ： Fundamentals of progressive artificial intelligence
- XCO.T490 ： Exercises in fundamentals of artificial intelligence
Prerequisites
Only doctor course students can register this exercise.
When you apply this exercise, take "XCO.T677 Fundamentals of progressive data science'' , "XCO.T679 Fundamentals of progressive artificial Intelligence" and "T680 Exercises in fundamentals in progressive artificial intelligence" of the same quarter of the same year in parallel. If there are many applicants, a lottery may be held.
Other
Exercises are carried out using Google Colaboratory. Students are required to get Google accounts and to get ready for usingfunctions of "fi le upload/download" in Google Drive.