2024 Faculty Courses School of Computing Major courses
Exercises in Fundamentals of Progressive Data Science
- Academic unit or major
- Major courses
- Instructor(s)
- Kei Miyazaki / Norio Tomii / Keisuke Yanagisawa / Takafumi Kanamori / Masakazu Sekijima / Tsuyoshi Murata / Katsumi Nitta / Yoshihiro Miyake / Isao Ono
- Class Format
- Exercise (HyFlex)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Thu
- Class
- -
- Course Code
- XCO.T678
- Number of credits
- 010
- Course offered
- 2024
- Offered quarter
- 3Q
- Syllabus updated
- Mar 17, 2025
- Language
- English
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 | 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 exercises 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 exercises on methods for dimension reduction such as canonical correlation analysis and graph embedding |
| 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.
Distributed via T2SCHOLA.
Evaluation methods and criteria
Based on reports for given assignments.
Related courses
- XCO.T677 ： Fundamentals of Progressive Data Science
- XCO.T687 ： Progressive Applied Artificial Intelligence and Data Science A
- XCO.T688 ： Progressive Applied Artificial Intelligence and Data Science B
- XCO.T689 ： Progressive Applied Artificial Intelligence and Data Science C
- XCO.T690 ： Progressive Applied Artificial Intelligence and Data Science D
- XCO.T679 ： Fundamentals of Progressive Artificial Intelligence
- XCO.T680 ： Exercises in Fundamentals of Progressive Artificial Intelligence
Prerequisites
Only doctor course students can register this exercise.
When you apply this exercise, it is strongly recommended to 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.
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.