2021 Fundamentals of progressive data science

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Academic unit or major
School of Computing
Instructor(s)
Kanamori Takafumi  Sekijima Masakazu  Murata Tsuyoshi  Nitta Katsumi  Nagahashi Hiroshi  Kobayashi Takao  Miyake Yoshihiro 
Course component(s)
Lecture     
Day/Period(Room No.)
Thr5-6()  
Group
-
Course number
XCO.T677
Credits
1
Academic year
2021
Offered quarter
3Q
Syllabus updated
2021/9/17
Lecture notes updated
-
Language used
English
Access Index

Course description and aims

In the current society, it is essential in all fi elds to appropriately exploit "big data" for fi nding rules and/or makingpredictions/decisions. This course gives fundamental knowledges and basic skills for handling large-scale data sets with the aid ofcomputers.

Student learning outcomes

Students will be able to apply basic knowledges on statistics for analyzing data and evaluating the obtained results mathematically.

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

Lecture is given via Zoom

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 Fundamentals 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 Principal component analysis Learn principal component analysis together with mathematical issues related to it
Class 5 Clustering Learn methods for categorizing unlabeled data into several categories
Class 6 Dimension reduction Learn methods for dimension reduction such as multidimensional scaling and canonical correlation analysis
Class 7 Ensemble learning 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.

Textbook(s)

None

Reference books, course materials, etc.

None
その他
個別に入力
(日本語)
(英語)

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.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.)

Preferred to have basic knowledge about linear algebra, analysis, and mathematical statistics.
Students of the doctor course are required to register XCO.T677 "Fundamentals of progressive data science" instead of XCO.T487"Fundamentals of data science."

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