2023 Exercises in Fundamentals of Progressive Data Science

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Academic unit or major
School of Computing
Miyazaki Kei  Tomii Norio  Yanagisawa Keisuke  Kanamori Takafumi  Sekijima Masakazu  Murata Tsuyoshi  Nitta Katsumi  Kobayashi Takao  Miyake Yoshihiro  Ono Isao 
Class Format
Exercise    (Livestream)
Media-enhanced courses
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Offered quarter
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 aims to help students to manipulate computer software tools for data analysis to get new

Student learning outcomes

Students will be able to understand the basis of data processing mechanisms and make use of various data analysis software tools appropriately.


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

In class, students are required to solve exercise problems that are linked with the contents of taught course ``XCO.T677
Fundamentals of progressive data science". Exercises are conducted via Zoom.

Course schedule/Required learning

  Course schedule Required learning
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 exercises 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 exercises on methods for dimension reduction such as canonical correlation analysis and graph embedding
Class 7 Ensemble learning Do exercises on 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 reports for given assignments.

Related courses

  • XCO.T677 : 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.)

Only doctor course students can register this exercise.
When you apply this exercise, it is strongly recommended to take "XCO.T677 Fundamentals of Progressive Data Science'', "XCO.T679 Fundamentals of Progressive Artificial Intelligence" and "T680 Exercises in Fundamentals in Progressive Artificial Intelligence" of the same quarter of the same year in parallel.


Exercises are carried out using Google Colaboratory. Students are required to get Google accounts and to get ready for usingfunctions of "fi le upload/download" in Google Drive.

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