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- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
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- School of Computing
- School of Life Science and Technology
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School of Environment and Society
- Department of Architecture and Building Engineering
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- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- School of Computing
- Instructor(s)
- Miyazaki Kei Tomii Norio Uriu Koichiro Tomobe Haruka Terazawa Yuki Kishimoto Maki Kameda Keisuke Uchida Makoto Yanagisawa Keisuke Murata Tsuyoshi Nitta Katsumi Kobayashi Takao Miyake Yoshihiro Ono Isao
- Class Format
- Exercise (Livestream)
- Media-enhanced courses
- Day/Period(Room No.)
- Tue7-8()
- Group
- -
- Course number
- XCO.T488
- Credits
- 1
- Academic year
- 2023
- Offered quarter
- 4Q
- Syllabus updated
- 2023/9/27
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
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.
Student learning outcomes
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 that will be developed
| ✔ 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". Exercises are conducted via Zoom.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Class guidance and introduction to Python programming | Variables, Control statements, Functions, etc. |
| Class 2 | Descriptive and inferential statistics | Fundamental of data analysis such as descriptive and inferential statistics using pandas, a library of Python |
| 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 canonical correlation analysis and graph embedding |
| Class 7 | Advanced topics | Do exersises on methods for ensemble learning |
Out-of-Class Study Time (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
Assessment criteria and methods
Based on reports for given assignments.
Related courses
- XCO.T487 ： 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 (i.e., required knowledge, skills, courses, etc.)
When you apply this exercise, it is strongly recommended to 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. 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.
