2021 (Exercises in fundamentals of progressive data science)

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Academic unit or major
School of Computing
Instructor(s)
Yanagisawa Keisuke  Kanamori Takafumi  Sekijima Masakazu  Murata Tsuyoshi  Nitta Katsumi  Nagahashi Hiroshi  Kobayashi Takao  Miyake Yoshihiro 
Course component(s)
Exercise     
Day/Period(Room No.)
Thr7-8()  
Group
-
Course number
XCO.T678
Credits
1
Academic year
2021
Offered quarter
3Q
Syllabus updated
2021/9/17
Lecture notes updated
-
Language used
Japanese
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.

Keywords

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 exercises of programming
Class 3 Classification Do exercises on methods for extracting discrimination rules from labeled data
Class 4 Principal component analysis Do exercises on principal component analysis with mathematical issues related to it
Class 5 Clustering Do exercises on methods for categorizing unlabeled data into several categories
Class 6 Dimension reduction Do exercises on methods for dimension reduction such as multidimensional scaling and canonical correlation analysis
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.

Textbook(s)

None

Reference books, course materials, etc.

Based on reports for given assignments.

Assessment criteria and methods

Based on reports for given assignments.

Related courses

  • XCO.T677 : Fundamentals of progressive data science
  • XCO.T483 : Advanced Artificial Intelligence and Data Science A
  • XCO.T485 : Advanced Artificial Intelligence and Data Science C
  • XCO.T486 : Advanced Artificial Intelligence and Data Science D
  • XCO.T679 : Fundamentals of progressive artificial intelligence
  • XCO.T490 : Exercises in fundamentals of artificial intelligence

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Only doctor course students can register this exercise.
When you apply this exercise, take "XCO.T677 Fundamentals of advanced data science'' , "XCO.T679 Fundamentals of advanced artificial Intelligence" and "T680 Exercises in fundamentals in advanced artificial intelligence" of the same quarter of the same year in parallel. If there are many applicants, a lottery may be held.

Other

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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