The listed resources are mostly free. I find that self-paced learning has advantages: efficiency, effectiveness, convenience, scalability, and reusability, so I try to find resources that match with my personal learning interests and make study plan. You may check CS video lectures for more extensive lists.
Programming:
- I have been using R for statistics since my sophomore year, so the self-learning for this part won't include much about R. But the size of available R packages increases dramatically, I often learn from the package manual or vignette directly, and I find that good documentation is very important. So I read and learn how to write R packages.
- (04/16/2020) This is a special time that I have to stay home all day: work, house work, and then learn something when I have little personal time. I watch some webinars/videos to refresh my mind; they may not be very technical, but very inspiring.
- Webinars/Videos:
- Packages:
- R programming: ggplot2, data.table, tidyr, CARET, randomForest, e1071, Rpart, KernLab, nnet, dplyr, Worldcloud, shiny, tm, MICE, mboost
- Shiny: https://shiny.rstudio.com/tutorial/
- Books:
- Wickham H. R packages: organize, test, document, and share your code. " O'Reilly Media, Inc."; 2015 Mar 26.
- R Shiny: https://mastering-shiny.org/
- Courses:
Machine Learning:
- Courses:
- Textbooks:
- Introduction to Statistical Learning: pdf available at Book page
- The Elements of Statistical Learning: pdf available at Book page
- Python Machine Learning: Amazon
- Modern Multivariate Statistical Techniques Regression, Classification, and Manifold Learning: Springer
Deep Learning:
- Courses:
- Andrew Ng's Deep Learning Specialization: Coursera page
- fast.ai's 7 week course, Practical Deep Learning For Coders, Part 1: Course page
- Textbooks:
- Deep learning book by Ian Goodfellow: http://www.deeplearningbook.org/. This is a very detailed reference book. (reading)
- ArXiv for research updates: https://arxiv.org/;
- https://deeplearn.org/; http://www.arxiv-sanity.com/top.
Reinforcement Learning (topic to learn):
- Courses:
- Reinforcement Learning: Udacity
- UCL Course on RL by David Silver: Course page
- CS 294: Deep Reinforcement Learning by UC Berkeley, Fall 2017: Course page
- Textbooks:
- Reinforcement Learning: An Introduction (2nd): pdf
Experiment Design and Analysis:
- Courses:
Topics:
- Genomic Data Science Specialization by Johns Hopkins University: Coursera page
Clinical Trials
- Textbooks:
- Friedman LM, Furberg C, DeMets DL, Reboussin DM, Granger CB. Fundamentals of clinical trials. New York: Springer; 2010 Sep 21.
- Piantadosi S. Clinical trials: a methodologic perspective. John Wiley & Sons; 2017 Aug 28.