2023 Fundamentals of 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  Kobayashi Takao  Miyake Yoshihiro  Ono Isao 
Class Format
Lecture    (Livestream)
Media-enhanced courses
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Syllabus updated
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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 gives fundamental knowledges and basic skills for handling large-scale data sets with the aid of computers.

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

Lectures are given by 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 Fundamentas 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 canonical correlation analysis and graph embedding
Class 7 Advanced topics 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.


Not specified.

Reference books, course materials, etc.

Distributed via T2SCHOLA.

Assessment criteria and methods

Based on quizzes in class/reports.

Related courses

  • XCO.T488 : Exercises in 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 (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"Funfamentals of progressive data science" instead of XCO.T487"Fundamantals of data science."

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