Fall 2025 - 데이터사이언스학과
Big Data Processing (대용량데이터처리)
Instructor: Dr. Muhammad Syafrudin
Course information
This course is intended for students who want to learn big data and its technology. Objectives of this course are as follows:
- Introduce students the concept of big data and its technology
- Allow students to experience some of existing big data technology
- Assist students to apply data visualization, data analytics using big data technology
- Allow student to form a group project (max 3 students for each group), to propose idea/case study and develop/utilize big data technology to solve them.
- Final group project (to be discussed later)
- This course aims to align closely with industry requirements and to enhance understanding of the latest advancements in the rapidly evolving field of big data.
Lecture
Fri at 09:00-12:00 KST via 금09:00-12:00 센B122
Office hours
Appointment by email (Office at AI Center 502호)
Prerequisites
Familiar/has experience with data analytics, programming and basic Linux commands
Notes
Course material can be downloaded in eCampus and please be aware, that we will not publicly release the homework assignments this year.
Schedule
| 주차(Week) | 강의내용(Class Topic & Contents) | 강의활동유형(Class Type) |
|---|---|---|
| 1 | Course overview | 강의 (Lecture) |
| 2 | Introduction to Big Data | 강의 (Lecture) |
| 3 | Big Data storage | 강의 (Lecture) |
| 4 | NoSQL database | 강의+실습 (Lecture + Practice) |
| 5 | Public holiday | No class |
| 6 | Big Data processing | 강의+실습 (Lecture + Practice) |
| 7 | Hadoop and its ecosystems | 강의+실습 (Lecture + Practice) |
| 8 | Mid exam | 시험 (Exam) |
| 9 | Big Data analytics | 강의+실습 (Lecture + Practice) |
| 10 | Big Data analytics with machine learning | 강의+실습 (Lecture + Practice) |
| 11 | Cluster analysis | 강의+실습 (Lecture + Practice) |
| 12 | Big Data visualization | 강의+실습 (Lecture + Practice) |
| 13 | Group project discussion and development I | 실습 (Practice) |
| 14 | Group project discussion and development II | 실습 (Practice) |
| 15 | Group project discussion and development III | 실습 (Practice) |
| 16 | Project presentation/demonstration | Presentation |
Grading
The final grade will be calculated using the following weights:
| # | Final Grade Weight |
|---|---|
| Attendance | 15% |
| Assignment(quiz/homework/weekly assignment) | 15% |
| Mid exam | 30% |
| Final group project | 40% |
| Total | 100% |
Assignment
There will be a quiz/homework/weekly assignment to complete. Some of them will be due in a week and some of them in two weeks. You have the option to work and submit the homework in pairs for all the assignments except two which you will do individually.
Submitting an assignment
Instructions for turning in assignments will be posted when the semester starts (in eCampus).
Getting help
For questions about homework, course content, installation, and after you have tried to troubleshoot yourselves, the process to get help is: Post the question in eCampus/group chat and hopefully your peers will answer. Note that in eCampus questions are visible to everyone. For private matters send an email to helpline: udin [at] sju [dot] ac [dot] kr.
Course Policies
Collaboration policy
We encourage you to talk and discuss the assignments with your fellow students (and on eCampus), but you are not allowed to look at any other students assignment or code outside of your pair. Discussion is encouraged, copying is not allowed.
Late day policy
Homework is due before each class. Late submission are not allowed.
Communication to students
Class announcements will be through eCampus. All homework and quizzes will be posted in eCampus. Also all feedback forms. Important note: make sure you have your settings set so you can receive emails from eCampus.
Academic honesty
We give a strong emphasis to Academic Honesty. As a student your best guidelines are to be reasonable and fair. We encourage teamwork for problem sets, but you should not split the homework and you should work on all the problems together.