2024 Faculty Courses School of Computing Major courses
Exercises in Fundamentals of 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.T488
- Number of credits
- 010
- Course offered
- 2024
- Offered quarter
- 3Q
- Syllabus updated
- Mar 14, 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.T487 Fundamentals of 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 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 |
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 T2SCHOLA
Evaluation methods and criteria
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
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.