2021 Faculty Courses School of Computing Major courses
Fundamentals of progressive data science
- Academic unit or major
- Major courses
- Instructor(s)
- Takafumi Kanamori / Masakazu Sekijima / Tsuyoshi Murata / Katsumi Nitta / Hiroshi Nagahashi / Takao Kobayashi / Yoshihiro Miyake
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Thu
- Class
- -
- Course Code
- XCO.T677
- Number of credits
- 100
- Course offered
- 2021
- Offered quarter
- 3Q
- Syllabus updated
- Jul 10, 2025
- Language
- English
Syllabus
Course overview and goals
In the current society, it is essential in all fi elds to appropriately exploit "big data" for fi nding rules and/or makingpredictions/decisions. This course gives fundamental knowledges and basic skills for handling large-scale data sets with the aid ofcomputers.
Course description and aims
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
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Lecture is given via Zoom
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Class guidance | Guidance for class flow, computing environment, and used programming language (Python) |
| Class 2 | Fundamentals 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 | Principal component analysis | Learn principal component analysis together with mathematical issues related to it |
| Class 5 | Clustering | Learn methods for categorizing unlabeled data into several categories |
| Class 6 | Dimension reduction | Learn methods for dimension reduction such as multidimensional scaling and canonical correlation analysis |
| Class 7 | Ensemble learning | Learn 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.
None
その他
個別に入力
(日本語)
(英語)
Evaluation methods and criteria
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.T485 ： Advanced Artificial Intelligence and Data Science C
- XCO.T486 ： Advanced Artificial Intelligence and Data Science D
- XCO.T679 ： Fundamentals of progressive artificial intelligence
- XCO.T490 ： Exercises in fundamentals of artificial intelligence
Prerequisites
Preferred to have basic knowledge about linear algebra, analysis, and mathematical statistics.
Students of the doctor course are required to register XCO.T677 "Fundamentals of progressive data science" instead of XCO.T487"Fundamentals of data science."