Spring 2022
Introduction to Big Data (빅데이터로보는세상)
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 analytics using big data technology
- Allow student to form a group project (max 3 student for each group), to propose idea/case study and develop/utilize big data technology to solve them.
Lecture
Tue, Thu at 9:00‐10:30 am KST via Hybrid class: Online Real-time Webex & Offline class at B209, starting May 3rd till the end of the semester
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 blackboard 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 introduction and prospects | 강의 (Lecture) |
| 2 | Introduction to Big Data | 강의 (Lecture) |
| 3 | Big Data storage | 강의 (Lecture) |
| 4 | NoSQL database | 강의 (Lecture) |
| 5 | Big Data processing | 강의+실습 (Lecture + Practice) |
| 6 | Hadoop and its ecosystems | 강의+실습 (Lecture + Practice) |
| 7 | Big Data analytics | 강의+실습 (Lecture + Practice) |
| 8 | Mid exam | 시험 (Exam) |
| 9 | Big Data analytics with machine learning | 강의+실습 (Lecture + Practice) |
| 10 | Cluster analysis | 강의+실습 (Lecture + Practice) |
| 11 | Big Data visualization | 강의+실습 (Lecture + Practice) |
| 12 | Big Data practice I | 실습 (Practice) |
| 13 | Big Data practice II | 실습 (Practice) |
| 14 | Group project discussion and development | 실습 (Practice) |
| 15 | Group project discussion and development | 실습 (Practice) |
| 16 | Project presentation/demonstration | 실습 (Practice) |
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 blackboard).
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 blackboard/group chat and hopefully your peers will answer. Note that in blackboard 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 blackboard), 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 blackboard. All homework and quizzes will be posted in blackboard. Also all feedback forms. Important note: make sure you have your settings set so you can receive emails from blackboard.
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.