2022 Fundamentals of Data Science

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Academic unit or major
School of Computing
Kanamori Takafumi  Sekijima Masakazu  Murata Tsuyoshi  Nitta Katsumi  Nagahashi Hiroshi  Kobayashi Takao  Miyake Yoshihiro 
Class Format
Lecture    (Livestream)
Media-enhanced courses
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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

All classes are given in both Ookayama and Suzukakedai campuses with the use of video conference systems.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Class guidance Guidance for class flow, computing environment, and used 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 multidimensional scaling and canonical correlation analysis
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 : Advanced Artificial Intelligence and Data Science A
  • XCO.T484 : FinTech and Data Science
  • XCO.T485 : Advanced Artificial Intelligence and Data Science C
  • XCO.T486 : Advanced 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.)

Preferred to have basic knowledge about linear algebra, analysis, and mathematical statistics.
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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