Statistics week
Jun. 5th, 2013 07:56 pm![[personal profile]](https://www.dreamwidth.org/img/silk/identity/user.png){kind=small}
curiouslCollection of links to materials suggested by professor and / or fellow students.
Git as a publishing platform is trendy ;).
1. "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming in data analysis with a computation/understanding-first, mathematics-second point of view. All in pure Python
https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
2. Coursera Data Analysis course (video) http://www.youtube.com/user/jtleek2007
3. Hypothesis testing and P-value from Khan Academy https://www.khanacademy.org/math/probability/statistics-inferential/hypothesis-testing/v/hypothesis-testing-and-p-values
4. Introduction to Bayes nets http://www.norsys.com/tutorials/netica/secA/tut_A1.htm, http://www.norsys.com/tutorials/netica/secA/tut_A2.htm
5. Doing Bayesian Data Analysis: a tutorial with R and BUGS http://www.indiana.edu/~kruschke/DoingBayesianDataAnalysis/
6. Bayesian estimation supersedes the t tests. http://www.indiana.edu/~kruschke/BEST/
7. Text classification and naive Bayes (Coursera lecture) https://class.coursera.org/nlp/lecture/37
8. E. Yudkowski An intuitive explanation to Bayes' theorem
9. Positive false discovery rate http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aos/1074290335
10. For the old that forgot the school: Udacity course of statistics. https://www.udacity.com/course/st095
11. Data Mining. Practical machine learning tools and techniques. http://www.cs.waikato.ac.nz/ml/weka/book.html
12 "Probability Theory: The Logic of Science" by E.T. Jaynes tries to give a first principles derivation of statistics (bayesian that is).
13. http://benmabey.com/2011/10/07/faq-what-machine-learning-book-should-i-start-with.html
14. A practical intro to data science http://blog.zipfianacademy.com/post/46864003608/a-practical-intro-to-data-science
15. DS book and course http://jsresearch.net/wiki/projects/teachdatascience
Illustration: IE use vs Murder rate :)
Git as a publishing platform is trendy ;).
1. "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming in data analysis with a computation/understanding-first, mathematics-second point of view. All in pure Python
https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
2. Coursera Data Analysis course (video) http://www.youtube.com/user/jtleek2007
3. Hypothesis testing and P-value from Khan Academy https://www.khanacademy.org/math/probability/statistics-inferential/hypothesis-testing/v/hypothesis-testing-and-p-values
4. Introduction to Bayes nets http://www.norsys.com/tutorials/netica/secA/tut_A1.htm, http://www.norsys.com/tutorials/netica/secA/tut_A2.htm
5. Doing Bayesian Data Analysis: a tutorial with R and BUGS http://www.indiana.edu/~kruschke/DoingBayesianDataAnalysis/
6. Bayesian estimation supersedes the t tests. http://www.indiana.edu/~kruschke/BEST/
7. Text classification and naive Bayes (Coursera lecture) https://class.coursera.org/nlp/lecture/37
8. E. Yudkowski An intuitive explanation to Bayes' theorem
9. Positive false discovery rate http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aos/1074290335
10. For the old that forgot the school: Udacity course of statistics. https://www.udacity.com/course/st095
11. Data Mining. Practical machine learning tools and techniques. http://www.cs.waikato.ac.nz/ml/weka/book.html
12 "Probability Theory: The Logic of Science" by E.T. Jaynes tries to give a first principles derivation of statistics (bayesian that is).
13. http://benmabey.com/2011/10/07/faq-what-machine-learning-book-should-i-start-with.html
14. A practical intro to data science http://blog.zipfianacademy.com/post/46864003608/a-practical-intro-to-data-science
15. DS book and course http://jsresearch.net/wiki/projects/teachdatascience
Illustration: IE use vs Murder rate :)
