2021 Faculty Courses School of Computing Major courses
Exercises in fundamentals of data science
- Academic unit or major
- Major courses
- Instructor(s)
- Keisuke Yanagisawa / Takafumi Kanamori / Masakazu Sekijima / Tsuyoshi Murata / Katsumi Nitta / Hiroshi Nagahashi / Takao Kobayashi / Yoshihiro Miyake
- Class Format
- Exercise
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Thu
- Class
- -
- Course Code
- XCO.T488
- Number of credits
- 010
- Course offered
- 2021
- Offered quarter
- 3Q
- 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.T487 Fundamentals of 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 excercises of programming |
| Class 3 | Classification | Do exersises on methods for extracting discrimination rules from labeled data |
| Class 4 | Clustering | Do exersises on methods for categorizing unlabeled data into several categories |
| Class 5 | Principal component analysis | Do exersises on principal component analysis with mathematical issues related to it |
| Class 6 | Dimension reduction | Do exersises on methods for dimension reduction such as multidimensional scaling and canonical correlation analysis |
| Class 7 | Advanced topics | Do exersises on methods for ensemble learning |
| Class 8 | General discussion | Discuss possible applications of data analysis in various fields |
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 OCW-i
Evaluation methods and criteria
Based on reports for given assignments.
Related courses
- XCO.T487 ： Fundamentals of data science
- XCO.T483 ： Advanced Artificial Intelligence and Data Science A
- XCO.T484 ： FinTech and Data Science
- XCO.T485 ： Advanced Artificial Intelligence and Data Science C
- XCO.T486 ： Advanced Artificial Intelligence and Data Science D
- XCO.T489 ： Fundamentals of artificial intelligence
- XCO.T490 ： Exercises in fundamentals of artificial intelligence
Prerequisites
When you apply this exercise, take "XCO.T487 Fundamentals of data science'' , "XCO.T489 Fundamentals of artificial Intelligence" and "T490 Exercises in fundamentals in artificial intelligence" of the same quarter of the same year in parallel. If there are many applicants, a lottery may be held. In the case of students of Tokyo Tech Academy for Convergence of Materials and Informatics, take “TCM.A404 Materials Informatics” instead of “XCO.T487 Fundamentals of data science” and “XCO.T488 Exercises in fundamentals of data science."
Students of the doctor course are required to register XCO.T678 "Exercises in fundamentals of advanced data science" instead of XCO.T488"Exercises in fundamentals of data science."
Other
Exercises are carried out using Google Colaboratory. Students are required to get Google accounts and to get ready for using functions of "file upload/download" in Google Drive.