KA218001 Big Data

Hiroshima University Syllabus

Japanese

Academic Year 2024Year 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) 　

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

Credits 2.0 Class Hours/Week 　 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, Machine learning

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
lesson8 Link Analysis 2
lesson9 Mining Social-Network Graphs
lesson10 Lab1 Data Analysis 1
lesson11 Lab2 Data Analysis 2
lesson12 Lab3 Data Analysis for Big Data 1
lesson13 Lab4 Data Analysis for Big Data 2
lesson14 Lab5 Large-Scale Machine Learning 1
lesson15 Lab6 Large-Scale Machine Learning 2

programming assignments, 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. 　

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

Learning techniques to be incorporated 　

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

Requirements Python will be used for the labs.

Grading Method programming assignments, 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	2024Year	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)
First half will be lectures and second half labs (hybrid of online and face-to-face). Details will be explained at the guidance.
Credits	2.0	Class Hours/Week		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, Machine learning
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 lesson8 Link Analysis 2 lesson9 Mining Social-Network Graphs lesson10 Lab1 Data Analysis 1 lesson11 Lab2 Data Analysis 2 lesson12 Lab3 Data Analysis for Big Data 1 lesson13 Lab4 Data Analysis for Big Data 2 lesson14 Lab5 Large-Scale Machine Learning 1 lesson15 Lab6 Large-Scale Machine Learning 2 programming assignments, 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.
(More Details)	Power Point slides, videos, Jupyter notebooks
Learning techniques to be incorporated
Suggestions on Preparation and Review	No specific preparation is necessary for the lecture, but active participation is encouraged. Regarding the labs, programming assignment and reports, you need to do some self-study.
Requirements	Python will be used for the labs.
Grading Method	programming assignments, 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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