KA218001 Big Data

Hiroshima University Syllabus

Japanese

Academic Year 2025Year School/Graduate School School of Informatics and Data Science

Lecture Code KA218001 Subject Classification Specialized Education

Subject Name ビッグデータ

Subject Name
（Katakana）ビッグデータ

Subject Name in
English Big Data

Instructor RAYTCHEV BISSER ROUMENOV

Instructor
(Katakana) ライチェフ　ビセル　ルメノフ

Campus Higashi-Hiroshima Semester/Term 3rd-Year, Second Semester, 4Term

Days, Periods, and Classrooms (4T) Tues1-4：ENG 219

Lesson Style Lecture Lesson Style
(More Details) Face-to-face, Online (simultaneous interactive)

First part will be lectures and second part labs (hybrid of online and face-to-face). Details will be explained at the guidance.

Credits 2.0 Class Hours/Week 4 Language of Instruction B : Japanese／English

Course Level 3 : Undergraduate High-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 02 : Information Science

Eligible Students

Keywords Big data, Data analysis, Large-scale algorithms

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Computer Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.

Data Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
（Comprehensive Abilities）
・D3. Ability to overlook social needs and issues that are intertwined in a complex manner and to solve issues with quantitative and logical thinking based on data, a multifaceted perspective, and advanced information analysis ability.

Intelligence Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
（Comprehensive Abilities）
・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science.

Class Objectives
/Class Outline In this course we will study the basic principles of some of the most important algorithms and data structures for storing, processing, analysis and visualization of very large-scale and high-dimensional data, together with related applications.

Class Schedule lesson1 Guidance and Overview of Big Data Concepts
lesson2 Large-Scale File Systems
lesson3 Map-Reduce 1
lesson4 Map-Reduce 2
lesson5 Locality-Sensitive Hashing 1
lesson6 Locality-Sensitive Hashing 2
lesson7 Link Analysis 1 (Google's PageRank algorithm)
lesson8 Link Analysis 2
lesson9 Recommendation Systems 1
lesson10 Recommendation Systems 2
lesson11 Lab1 Data Analysis
lesson12 Lab2 Data Analysis for Big Data
lesson13 Lab3 Data Visualization
lesson14 Lab4 Big Data Visualization
lesson15 Lab5 Large-Scale Machine Learning

reports, end-of-term examination

Text/Reference
Books,etc. J. Leskovec, A. Rajaraman, J. D. Ullman： "Mining of Massive Datasets" 3ed, Cambridge University Press.

PC or AV used in
Class,etc. Handouts, Visual Materials, Microsoft Teams, moodle

(More Details) Power Point slides, videos, Jupyter notebooks

Learning techniques to be incorporated Quizzes/ Quiz format, PBL (Problem-based Learning)/ TBL (Team-based Learning), Project Learning, Post-class Report

Suggestions on
Preparation and
Review No specific preparation is necessary for the lecture, but active participation is encouraged.
Regarding the labs and reports, you need to do some self-study.

Requirements Python will be used for the labs.

Grading Method reports and end-of-term exam

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other Lecture in Japanese, slides in Japanese/English

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2025Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA218001	Subject Classification	Specialized Education
Subject Name	ビッグデータ
Subject Name （Katakana）	ビッグデータ
Subject Name in English	Big Data
Instructor	RAYTCHEV BISSER ROUMENOV
Instructor (Katakana)	ライチェフ　ビセル　ルメノフ
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, Second Semester, 4Term
Days, Periods, and Classrooms	(4T) Tues1-4：ENG 219
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face, Online (simultaneous interactive)
First part will be lectures and second part labs (hybrid of online and face-to-face). Details will be explained at the guidance.
Credits	2.0	Class Hours/Week	4	Language of Instruction	B : Japanese／English
Course Level	3 : Undergraduate High-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students
Keywords	Big data, Data analysis, Large-scale algorithms
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Computer Science Program （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. Data Science Program （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. （Comprehensive Abilities）・D3. Ability to overlook social needs and issues that are intertwined in a complex manner and to solve issues with quantitative and logical thinking based on data, a multifaceted perspective, and advanced information analysis ability. Intelligence Science Program （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. （Comprehensive Abilities）・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science.
Class Objectives /Class Outline	In this course we will study the basic principles of some of the most important algorithms and data structures for storing, processing, analysis and visualization of very large-scale and high-dimensional data, together with related applications.
Class Schedule	lesson1 Guidance and Overview of Big Data Concepts lesson2 Large-Scale File Systems lesson3 Map-Reduce 1 lesson4 Map-Reduce 2 lesson5 Locality-Sensitive Hashing 1 lesson6 Locality-Sensitive Hashing 2 lesson7 Link Analysis 1 (Google's PageRank algorithm) lesson8 Link Analysis 2 lesson9 Recommendation Systems 1 lesson10 Recommendation Systems 2 lesson11 Lab1 Data Analysis lesson12 Lab2 Data Analysis for Big Data lesson13 Lab3 Data Visualization lesson14 Lab4 Big Data Visualization lesson15 Lab5 Large-Scale Machine Learning reports, end-of-term examination
Text/Reference Books,etc.	J. Leskovec, A. Rajaraman, J. D. Ullman： "Mining of Massive Datasets" 3ed, Cambridge University Press.
PC or AV used in Class,etc.	Handouts, Visual Materials, Microsoft Teams, moodle
(More Details)	Power Point slides, videos, Jupyter notebooks
Learning techniques to be incorporated	Quizzes/ Quiz format, PBL (Problem-based Learning)/ TBL (Team-based Learning), Project Learning, Post-class Report
Suggestions on Preparation and Review	No specific preparation is necessary for the lecture, but active participation is encouraged. Regarding the labs and reports, you need to do some self-study.
Requirements	Python will be used for the labs.
Grading Method	reports and end-of-term exam
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other	Lecture in Japanese, slides in Japanese/English
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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