Course Materials for Econ Classes I Take in 2019 Fall Semester
Everything shown under this folder is credited to STP. If you would like to use or cite any materials, pls MESSAGE me first. Any violation of use is subjected to plagiarism and will yield the rights of law to be reserved by myself.
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Dunder challenge is full of very interesting questions with regarding to python and pandas, and the solution is under /data folder.
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DI chanllenge is an interesting data challenge I did for datacamp incubator with Python packages, and the solution is open for discussion.
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Real Analysis are consist of written homework assignments typed in LaTex.
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RPorject, including supportive R scripts for beginners' learning. Please contact if you want to cite or use for business purposes.
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qProject, including some homework, research, and interview projects, which is kept just for my own record, pls do not check or quote.
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Forecasting: Principles and Practice Hyndman, R.J., & Athanasopoulos, G. (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. OTexts.com/fpp2. Accessed on Oct, 2019.
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R for Data Science Garrett Grolemund and Hadley Wickham. (2017)
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An Introduction to Statistical Learning with Applications in R Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. (2009)
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NABE(National Association for Business Economics) consists of seminar notes that I attended.
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http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf James G., Witten, D., Hastie, T., Tibshirani R. 2013. An Introduction to Statistical Learning with Applications in R. 7th Ed. Abbreviation: ISLR
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http://www.deeplearningbook.org/ Goodfellow I., Bengio Y., Courville A. 2016. Deep Learning. MIT Press. Abbreviation: DL