2024 Fundamentals of Progressive Data Science

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Academic unit or major
School of Computing
Miyazaki Kei  Tomii Norio  Kanamori Takafumi  Sekijima Masakazu  Murata Tsuyoshi  Nitta Katsumi  Miyake Yoshihiro  Ono Isao 
Class Format
Media-enhanced courses
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Offered quarter
Syllabus updated
Lecture notes updated
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Course description and aims

In the current society, it is essential in all fi elds to appropriately exploit "big data" for finding rules and/or makingpredictions/decisions. This course gives fundamental knowledges and basic skills for handling large-scale data sets with the aid ofcomputers.

Student learning outcomes

Students will be able to apply basic knowledges on statistics for analyzing data and evaluating the obtained results mathematically.


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

Lecture is given via Zoom

Course schedule/Required learning

  Course schedule Required learning
Class 1 Class guidance Guidance for class flow, computing environment, and 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 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, canonical correlation analysis and graph embedding
Class 7 Ensemble learning Learn methods for ensemble learning

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.



Reference books, course materials, etc.

Distributed via T2SCHOLA

Assessment criteria and methods

Based on quizzes in class/reports.

Related courses

  • XCO.T678 : Exercises in 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 (i.e., required knowledge, skills, courses, etc.)

Basic knowledge of linear algebra, differential and integral calculus, and mathematical statistics is required.
Students of the doctor course are required to register XCO.T677 "Fundamentals of progressive data science" instead of XCO.T487"Fundamentals of data science."

Attendance is mandatory from the first lecture class, as the course will cover matters necessary for understanding the subsequent lecture classes, in addition to guidance. Students who wish to take the course must take and pass a placement test to be administered in advance.

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