2019 Exercises in fundamentals of data science

Font size  SML

Register update notification mail Add to favorite lecture list
Academic unit or major
School of Computing
Kabashima Yoshiyuki  Kanamori Takafumi  Sekijima Masakazu  Murata Tsuyoshi  Ono Shunsuke  Kawashima Takayuki  Komiya Ken  Kobayashi Norimasa  Yanagisawa Keisuke 
Class Format
Media-enhanced courses
Day/Period(Room No.)
Mon7-8(学術国際情報センター3F第1実習室,, すずかけ台情報ネットワーク演習室)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
Access Index

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 findings.

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.T487 Fundamentals of data science".

Course schedule/Required learning

  Course schedule Required learning
Class 1 Prerequirement exam Check basic knowledge about mathematics and Python language
Class 2 Arrangement of computing environment and warming-up of programming Arrange computing environment and carry out simple excercises of programming
Class 3 Classification Do exersises 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 exersises on methods for dimension reduction such as multidimensional scaling and canonical correlation analysis
Class 7 Advanced topics Do exersises on methods for ensemble learning
Class 8 General discussion Discuss possible applications of data analysis in various fields


Not specified

Reference books, course materials, etc.

Distributed via OCW-i

Assessment criteria and methods

Based on reports for given assignments.

Related courses

  • XCO.T487 : 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.)

Take a prerequirement exam on "linear algebra", "analysis", and "basic grammar and functions of Python3" in the first class on Monday, December 2, 2019. Make sure to come to W531 or G115 no later than 15:05. Not allowed to take this course if you skip this exam, and may not be allowed depending on its score. It is also mandatory to take ``XCO.T487 Fundamentals of data science'' and "T490 Exercises in fundamentals in artificial intelligence" in parallel.


A prerequirement test is conducted in irregular class rooms W531 and G115 in the first class on Monday, December 2nd. Exercises are carried out using Google Colaboratory. Students are required to get Google accounts and to get ready for using functions of "file upload/download" in Google Drive.

Page Top