This repository is to maintain and share all the bookmark that I've been using for my UIUC MCS-DS study. It's English/Korean language mixed, and I'll be adding "(KR)" for those written in Korean for the conveinence.
The content will be updated until my graduation(expecting sometime in 2020), so fork the current repository rahter than clone if you want to keep up-to-date contents for your program.
제가 공부하고 있는 University of Illinois at Urbana-Champaign Online MCS - Data Science 과정 학습중에 참고한 자료들을 링크해 둡니다. 햔글/영어 자료가 섞여있으며 한글자료들은 "(KR)"로 표기해 두었습니다.
- STAT 420 syllabus(not updated for2020 summer one though): https://cs.illinois.edu/sites/default/files/docs/syllabi/STAT%20420%20Syllabus.pdf
- Textbook, Applied statistics in R : https://daviddalpiaz.github.io/appliedstats/
- Markdown table generator : http://www.tablesgenerator.com/markdown_tables
- STAT 400 syllabus 2018 : https://daviddalpiaz.github.io/stat400sp18/syllabus.html
- Bayesian Data Analysis 3rd edition: https://amzn.to/2XHcuSx ( this is kind of official ABM textbok, also author of the book released this book for free for non-commercial usage in April, 2020 )
- Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan(The Dog Book): https://amzn.to/2XTg4cH ( maybe you wnat to read this one if BDA3 is too much for you )
- Not a material for ABM but good enough. It's the book for other Bayesian class on Coursera : https://statswithr.github.io/book/
- Probably good for the course prep 1 : https://www.coursera.org/learn/bayesian-statistics
- Probably good for the course prep 2 : https://www.coursera.org/learn/mcmc-bayesian-statistics
- Syllabus: https://cs.illinois.edu/sites/default/files/docs/syllabi/CS498_CN_Syllabus.pdf
- Beej's Guide to Network Programming: https://beej.us/guide/bgnet/
- Not mandatory but recommended textbook : "Computer Networks: A Systems Approach". I am pretty sure you can get e-book version of this through UIUC library proxy link but here is Amazon link just in case if you want to buy a physical book : https://amzn.to/37Ov2lW
- A video lecture for one of the PSL textbook "An Introduction to Statistical Learning with Applications in R", by authors - https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/
- Same as above, but on different location https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/
- The Elements of Statistical Learning: Data Mining, Inference and Prediction(textbook): https://web.stanford.edu/~hastie/ElemStatLearn/
- An Introduction to Statistical Learning with Applications in R(textbook): http://www-bcf.usc.edu/~gareth/ISL/
- World Bank Dataset : http://datatopics.worldbank.org/world-development-indicators/
- Lakner-Milanovic (2013) World Panel Income Distribution (LM-WPID) : http://www.worldbank.org/en/research/brief/World-Panel-Income-Distribution
- Tableau student account request : https://www.tableau.com/academic/students
- Public datasets for one of the assignment( Dashboard ) for summer 2019 DV.
- Google Cloud Public Datasets : https://cloud.google.com/public-datasets/
- Kaggle : https://www.kaggle.com/
- World Bank Open Data : https://data.worldbank.org
- D3 library : https://d3js.org/
- D3 charts gallary: https://github.com/d3/d3/wiki/gallery
- JSFiddle : https://jsfiddle.net (for D3 code quick testing)
- Mapping with D3 - A Friendly Introduction : https://maptimeboston.github.io/d3-maptime ( confused with data mapping?, take a look this!!! )
- Basic line chart in D3 : https://www.d3-graph-gallery.com/graph/line_basic.html
- Regexp : https://regexr.com, not mentioned in the video but the other good one http://regex101.com
- OpenRefine : http://openrefine.org
- UIUC MCS-DS CS498 Applied Machine Learning syllabus : https://courses.engr.illinois.edu/cs498aml/sp2019/
- Becoming Human : https://becominghuman.ai
- Andrew Ng's Machine Learning Coursera lectures : https://www.coursera.org/learn/machine-learning/
- SunJin Park's prof. Andrew Ng ML course Korean summary and explanation : https://wikidocs.net/book/587 (KR)
- Gilbert Strang's Linear Algebra : https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/
- An Idiot’s guide to Support vector machines (SVMs): http://web.mit.edu/6.034/wwwbob/svm.pdf
- Python Programming : https://pythonprogramming.net
- Practical Machine Learning Tutorial with Python Introduction : https://pythonprogramming.net/machine-learning-tutorial-python-introduction/
- Prof. Sung Kim's Deep Learning for Everybody season 1 : https://www.youtube.com/playlist?list=PLlMkM4tgfjnLSOjrEJN31gZATbcj_MpUm (KR)
- Prof. David Forsyth's "Probability and Statistics for Computer Science" PDF downloadable through UIUC proxy : https://link-springer-com.proxy2.library.illinois.edu/book/10.1007/978-3-319-64410-3 ( Need UIUC student account to access )
- PRML(Pattern Recognition & Machien Learning, Bishop) : http://norman3.github.io/prml/ (KR)
- Linear Regression vs. Logistic Regression : https://stackoverflow.com/questions/12146914/what-is-the-difference-between-linear-regression-and-logistic-regression
- Mathmatics fo Machine Learning by Garrett Thomas : http://gwthomas.github.io/docs/math4ml.pdf
- Principal Component Analysis in Python (from the scratch and with scikit-learn) : https://plot.ly/ipython-notebooks/principal-component-analysis/
- Affine transformation : https://www.youtube.com/watch?v=DSmXIYkp024 (KR)
- Eigenvalue and eigenvector : https://www.youtube.com/watch?v=Nvc7ZRVjciM (KR)
- Definitions of Eigenvalues and Eigenvectors(Jupyter Notebook) : https://wikidocs.net/4050 (KR)
- How to reverse PCA and reconstruct original variables from several principal components? : https://stats.stackexchange.com/questions/229092/how-to-reverse-pca-and-reconstruct-original-variables-from-several-principal-com
- How would you explain covariance to someone who understands only the mean? : https://stats.stackexchange.com/questions/18058/how-would-you-explain-covariance-to-someone-who-understands-only-the-mean
- Pattern Recognition and Machine Learning : https://www.microsoft.com/en-us/research/people/cmbishop/#!prml-book
- Dive into Deep Learning : https://d2l.ai/
- Data Science School https://datascienceschool.net (KR)
- Machine Learning 101 : https://medium.com/machine-learning-101
- Towards Data Science : https://towardsdatascience.com
- Google Seedbank : https://research.google.com/seedbank/
- Tim Roughgarden Lectures: https://www.youtube.com/channel/UCcH4Ga14Y4ELFKrEYM1vXCg/
- Algorithms part 1 - from Princeton Univ on Coursera: https://www.coursera.org/learn/algorithms-part1
- Algorithms part 2 - from Princeton Univ on Coursera: https://www.coursera.org/learn/algorithms-part2
- Introduction to Algorithms, 3rd Edition (The MIT Press): https://amzn.to/2VdxHlu (This is one big 1290 page books!!! Know what you are trying to buy or read!)
- Algorithms Unlocked, MIT press: https://amzn.to/34FnK3w (If above is too much, the lightweight version from one of the author above book)
- Building Blocks for Theoretical Computer Science by Margaret M. Fleck: http://mfleck.cs.illinois.edu/building-blocks/version-1.3/whole-book.pdf
- Algorithms by Jeff Erickson: http://jeffe.cs.illinois.edu/teaching/algorithms/book/Algorithms-JeffE.pdf
- Make your own internet startup company easily : https://stripe.com/atlas
- Data Science Interview Guide : https://medium.com/better-programming/the-data-science-interview-study-guide-c3824cb76c2e
- MathJax : https://www.mathjax.org/
- statistical engineering : https://statistical-engineering.com
- Prof Gibert Strang's Linear Algebra : https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010
- Prof Jhevon Smith's Linear Algebra : https://www.youtube.com/watch?list=PLYoxM3oLTvxL_24BB4bc8kHbasgmN1eQl&v=wa4xRnH-nXo
If you want to contribute in this repo, plese open pull request and I'll merge it once checked.