Hiroshima University Syllabus

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Japanese
Academic Year 2022Year 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
 
Criterion referenced
Evaluation
Informatics and Data Science Program
(Abilities and Skills)
・A. Skills related to the development of an information infrastructure,information processing techniques, and technology for producing new added value through data analysis.

(Comprehensive Abilities)
・D3. Ability to examine social needs and issues which are interlinked in a complex manner, using a top-down view to solve the problems through quantitative and logical thinking based on data, diverse perspectives, and advanced skills in information processing and analysis.
 
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 and Map-Reduce 1
lesson3 Large-Scale File Systems and Map-Reduce 2
lesson4 Workflow Systems
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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