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School of Engineering
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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 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)
- Yamada Takuji Yamashita Yukihiko Manzhos Sergei Kelly Shane Murata Tsuyoshi Nitta Katsumi Nagahashi Hiroshi Kobayashi Takao Miyake Yoshihiro
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Tue5-6()
- Group
- -
- Course number
- XCO.T487
- Credits
- 1
- Academic year
- 2021
- Offered quarter
- 4Q
- Syllabus updated
- 2021/9/17
- 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 gives fundamental knowledges and basic skills for handling large-scale data sets with the aid of computers.
Student learning outcomes
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 that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
All classes are given in both Ookayama and Suzukakedai campuses with the use of video conference systems.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Class guidance | Guidance for class flow, computing environment, and used 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 multidimensional scaling and canonical correlation analysis |
| Class 7 | Advanced topics | Learn methods for ensemble learning |
| Class 8 | General discussion | Discuss possible applications of data analysis in various fields |
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 OCW-i.
Assessment criteria and methods
Based on quizzes in class/reports.
Related courses
- XCO.T488 ： Exercises in 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 (i.e., required knowledge, skills, courses, etc.)
Preferred to have basic knowledge about linear algebra, analysis, and mathematical statistics.
Students of the doctor course are required to register XCO.T677"Funfamentals of progressive data science" instead of XCO.T487"Fundamantals of data science."
