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School of Environment and Society
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- Liberal arts and basic science courses
- Academic unit or major
- School of Computing
- Instructor(s)
- Kabashima Yoshiyuki Kanamori Takafumi Sekijima Masakazu Murata Tsuyoshi Ono Shunsuke Kawashima Takayuki Komiya Ken Kobayashi Norimasa Yanagisawa Keisuke
- Class Format
- Exercise
- Media-enhanced courses
- Day/Period(Room No.)
- Mon7-8(学術国際情報センター3F第1実習室,, すずかけ台情報ネットワーク演習室)
- Group
- -
- Course number
- XCO.T488
- Credits
- 1
- Academic year
- 2019
- Offered quarter
- 4Q
- Syllabus updated
- 2019/11/15
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
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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".
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| 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 |
Textbook(s)
Not specified
Reference books, course materials, etc.
Distributed via OCW-i
Assessment criteria and methods
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 (i.e., required knowledge, skills, courses, etc.)
Take a prerequirement exam on "linear algebra", "analysis", and "basic grammar and functions of Python3" in the first class on Monday, December 2, 2019. Make sure to come to W531 or G115 no later than 15:05. Not allowed to take this course if you skip this exam, and may not be allowed depending on its score. It is also mandatory to take ``XCO.T487 Fundamentals of data science'' and "T490 Exercises in fundamentals in artificial intelligence" in parallel.
Other
A prerequirement test is conducted in irregular class rooms W531 and G115 in the first class on Monday, December 2nd. 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.
