데이터사이언스융합전공 특강 (영어강의) 공지: Data Classification and Clustering Basics
- 데이터사이언스융합전공
- 조회수490
- 2024-04-16
데이터사이언스융합전공 특강 (영어강의) 공지
Special Lecture: Data Classification and Clustering Basics
1. 일시
- 온라인 강의: 2024년 5월 10일 ~ 16일 / 5월 17일 ~ 23일
- 오프라인 강의: 2024년 5월 17일(금) / 5월 24일(금) (이틀간 진행)
2. 시간
- 온라인 강의: 오프라인 수업 전 일주일 내 학습(1시간 30분)
- 오프라인 강의: 15:00 ~ 16:30(1시간 30분)
3. 장소: 퇴계인문관 31709호 / Toegye Hall of Humanities, No. 31709
4. 개요(국문)
- 데이터 사이언스 기본 수업으로 코딩 경험이 없는 학생도 참여 가능
- 교수 강의는 비디오 파일로 미리 제공하여 강의 전 시청 필수
- 강의실에서는 실습 진행, 노트북 준비 필수
- 이틀 모두 참석 가능한 학생만 참여 가능
5. 개요(영문)
- Students without coding experience can participate in this basic data science lecture.
- Professor lectures are provided in advance as video files, and watching them before the lecture is mandatory.
- Practical sessions will be conducted in the classroom, so bringing a laptop is required.
- Only students available to attend both days are eligible to participate.
※ RSVP by May 6th: https://forms.gle/iSoFBmHviBtR2x9J9
6. 교수님 약력
Dr. Seungwon Yang is an Associate Professor at Louisiana State University's School of Information Studies and the Center for Computation and Technology. He holds a Ph.D., M.S., and B.S. degrees from Virginia Tech's Department of Computer Science, and also has the B.S. degree from the Department of Electronic Engineering at SKKU. His research interests include crisis informatics, social media, and data science.
For more information, please visit the following link: (https://www.lsu.edu/chse/slis/about_us/bios/yang.php)
7. Time table
Session Title | Learning Period | Details |
Setting up a data science environment | 5/10-5/16 Video lectures | Anaconda for Python and Python packages, Jupyter Notebook, and Markdown Pandas for data preprocessing |
5/17 In-class activities | Installing Anaconda and Python packages Creating Jupyter Notebook with Markdown and Python code Initial data analysis using Pandas and Matplotlib | |
Classification and clustering basics | 5/17-5/23 Video lectures | Classification (e.g., Random Forest algorithm) Clustering (e.g., K-means) |
5/24 In-class activities | Creating a classification pipeline (e.g., initial data analysis, running the algorithm, and evaluation of results) Clustering of data (e.g., initial data analysis and running the algorithm with evaluation metric) |