QT:{{”
“It was a huge amount of fun watching them take that raw data and put it through their own pipelines,” Zimmer told me, but he also felt uncomfortable pointing out discrepancies to the scientists he worked with. “I still remember, I was sitting down with Chris Mason at Weill Cornell. He and his students were so enthusiastically going through their findings with me… and they showed me, among other things, how many SNPs I had. Not too long beforehand I’d gone through the same experience with Mark Gerstein and his team at Yale, and their numbers for my SNPs were off by hundreds of thousands. … It was a little awkward with Chris, but I just said, ‘Hey, I got a very different number from Mark Gerstein,’ and Chris just shrugged and said, ‘Oh yeah, that happens.’”
It turns out, there’s a lot about our current understanding of our genes and how we pass them on that isn’t perfectly clear cut. “}}
http://catalog.yale.edu/handbook-instructors-undergraduates-yale-college/grades/reporting/
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Term grades for the spring term are due seven days after the end of final examination period; in 2017–2018, this date is May 16, 2018. In the spring term, grades are due for seniors within forty-eight hours of the end of final examination period; in 2017–2018, this date is May 11, 2018.
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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
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/
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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)
