Let's dig up the data together and find a radical insight.
도움을 주실 온라인 사이트/문서/서비스/도구들...
학습
- R, Python 분석과 프로그래밍의 친구 [ https://rfriend.tistory.com ]
- 데이터 사이언스 스쿨 [ https://datascienceschool.net/ ]
데이터
- AI Hub [ https://www.aihub.or.kr/ai_data ]
- 공공데이터 포털 [ https://www.data.go.kr/ ]
- Kaggle [ https://www.kaggle.com/ ]
도서
- 김영호 역, Hyndman, R.J., & Athanasopoulos, G. (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia [ https://otexts.com/fppkr/ ]
- 박해선 역, Aurélien Géron, Hands-On Machine Learning(2nd Eds.) ipynb [ https://github.com/rickiepark/handson-ml2 ]
- 이현열, R을 이용한 퀀트 투자 포트폴리오 만들기https://hyunyulhenry.github.io/quant_cookbook/ ]
- 이웅원, Fundamental of Reinforcement Learning [ https://dnddnjs.gitbooks.io/rl/content/ ]
- 온라인 R Cookbook - R for Data Science et al. [ https://bookdown.org/ ]
- The caret package in R [ https://topepo.github.io/caret/index.html ]
강좌
- Data Science with SeoungBum Kim - 김성범 교수, 산업경영공학부 [ https://www.youtube.com/channel/UCueLU1pCvFlM8Y8sth7a6RQ/featured ]
- iAI POSTECH - 이승철 교수, 기계공학과 [ https://www.youtube.com/channel/UCPHqDHsMG22FwzvKb0san7g ]
- 모두를 위한 머신러닝/딥러닝 강의 - 김성훈 교수, 컴퓨터공학과 [ http://hunkim.github.io/ml/ ]
도구
- R [ https://cran.r-project.org/ ]
- RStudio [ https://rstudio.com/ ]
- Anaconda [ https://www.anaconda.com/ ]
- PyCharm [ https://www.jetbrains.com/ ]
- Docker [ https://www.docker.com/ ]
- Github [ https://github.com/ ]
- RStudio Bookdown [ https://bookdown.org/yihui/bookdown/theming.html ]
- Docker Image with RStudio and Python for Data Science [ $docker pull auditoris/ds_dock ]
커뮤니티
- Kaggle [ https://www.kaggle.com/ ]
There's always another way.
2021.02.15.
