2020 Exercises in fundamentals of data science

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Academic unit or major
School of Computing
Instructor(s)
Kanamori Takafumi  Sekijima Masakazu  Murata Tsuyoshi  Ono Shunsuke  Kawashima Takayuki  Komiya Ken  Kobayashi Norimasa  Nitta Katsumi  Yanagisawa Keisuke 
Course component(s)
Exercise
Mode of instruction
 
Day/Period(Room No.)
Thr7-8(Zoom)  
Group
-
Course number
XCO.T488
Credits
1
Academic year
2020
Offered quarter
3Q
Syllabus updated
2020/9/29
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 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

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)

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.)

When you apply this exercise, take "XCO.T487 Fundamentals of data science'' , "XCO.T489 Fundamentals of artificial Intelligence" and "T490 Exercises in fundamentals in artificial intelligence" of the same quarter of the same year in parallel. If there are many applicants, a lottery may be held. In the case of students of Tokyo Tech Academy for Convergence of Materials and Informatics, take “TCM.A404 Materials Informatics” instead of “XCO.T487 Fundamentals of data science” and “XCO.T488 Exercises in fundamentals of data science."

Other

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.

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