linkstream2 microblog

https://twitter.com/carlzimmer/status/989191766745272320

https://twitter.com/carlzimmer/status/989196672503812102

https://twitter.com/carlzimmer/status/989197056060284928

https://twitter.com/carlzimmer/status/989214467882934272

https://play.google.com/store/apps/details?id=com.google.android.apps.forscience.whistlepunk&hl=en

https://itunes.apple.com/us/app/science-journal-by-google/id1251205555?mt=8

http://www.theverge.com/2016/5/22/11735532/google-science-journal-app-research-android

perhaps collect your own mhealth data

Points of significance – #MachineLearning: supervised methods https://www.Nature.com/articles/nmeth.4551 Nice discussion of the k in k-NN & the slack parm. C, penalizing misclassified points in SVM — both which act somewhat analogously as regularizers. Good for #teaching

Genic Intolerance to Functional Variation & the Interpretation of Personal Genomes
http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1003709 Nice plot of the number of rare v common variants in each gene to find outliers particularly tolerant to impactful (eg #LOF) mutations

http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1003709

Petrovski et al ’13

https://www.vertabelo.com/

Webinar Invitation
General Data Science Overview
Date: November 1st
Time: 11:00 a.m. EDT

QT:{{”

How can we effectively and efficiently teach statistical thinking and computation to students with little to no background in either? How can we equip them with the skills and tools for reasoning with various types of data and leave them wanting to learn more?

In this talk we describe an introductory data science course that is our (working) answer to these questions. ….

Webinar Recordings:

We try to record every webinar we host and post all materials on our website. http://www.rstudio.com/resources/webinars/
“}}

http://researchdata.yale.edu/training-calendarthe Research Data Support website has published a unified calendar for data and research skills training provided by the Library, Center for Research Computing, Medical Library, and Center for Teaching and Learning.

The Research Data Consultation Group (http://researchdata.yale.edu/) has considered aggregating data science training information into a unified calendar.

Also, there’s an instruction calendar at the library
(http://csssi.yale.edu/instruction/workshop-and-instruction-calendar)

Naive #Bayes Classification explained with Python code
http://www.DataScienceCentral.com/profiles/blogs/naive-bayes-classification-explained-with-python-code Nice worked example; good for #teaching HT @KirkDBorne

http://bigdatau.org/explore_erudite