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
Lifelong Learning
http://www.Economist.com/news/special-report/21714169-technological-change-demands-stronger-and-more-continuous-connections-between-education Future for colleges? Microcredentails & Nanodegrees inspired by albums unbundled into iTunes songs
interesting view of where short “workshops” fit relative to the traditional course
QT:{{”
Scott DeRue, the dean of the Ross School of Business at the University of Michigan, says the unbundling of educational content into smaller components reminds him of another industry: music. Songs used to be bundled into albums before being disaggregated by iTunes and streaming services such as Spotify. In Mr DeRue’s analogy, the degree is the album, the course content that is freely available on MOOCs is the free streaming radio service, and a “microcredential” like the nanodegree or the specialisation is paid-for iTunes.
How should universities respond to that kind of disruption? For his answer, Mr DeRue again draws on the lessons of the music industry. Faced with the disruption caused by the internet, it turned to live concerts, which provided a premium experience that cannot be replicated online. The on-campus degree also needs to mark itself out as a premium experience, he says.
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Cracking the code of when #genetic variants matter, by @CarlZimmer https://www.StatNews.com/2016/08/17/genetic-variants-ex-ac-sequence/ Underscores need for realistic guidelines on risk
Education in #CompBio, by @bffo & @joannealisonfox
http://journals.PLOS.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003391 Keeping up in a rapidly changing field. Will implement some @Yale
QT:{{”
“These initiatives help to extend computational biology beyond the domain of specialized laboratories. Researchers, at all levels, need to keep themselves up-to-date with the quickly changing world of computational biology, and trainees need programs where bioinformatics skills are embedded so they can have comprehensive training. New bioinformatics workflows can be adopted more widely if education efforts keep pace. As previously pointed out , starting early is also very important. There is still room for programs that capture the excitement and enthusiasm of secondary school students and convey the potential of computational biology to the public. We welcome additions to the PLOS Computational Biology “Bioinformatics: Starting Early” collection (www.ploscollections.org/cbstartingearly).
We would like to involve the community in this endeavor. With this editorial, we are calling out to educators and researchers who have experience in teaching, specifically, those keen to raise the expectations and the inquisitiveness of the next generation of biologists. The Education collection will continue to publish leading edge education materials in the form of tutorials that can be used in a “classroom” setting (whatever that may mean nowadays: stated more generically, “the places where people learn”). We will continue to encourage articles set in the context of addressing a particular biological question and, as mentioned above, we welcome new “primers” and “quick guides.” We will also be inviting tutorials from the various computational meetings. A new category of papers that is in the pipeline for the Education collection is the “Quick Tips” format, the first of which was just published . The “Quick Tips” articles address specific tools or databases that are in wide use in the community.
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https://en.wikipedia.org/wiki/Needleman%E2%80%93Wunsch_algorithm
relates to
https://en.wikipedia.org/wiki/Wagner%E2%80%93Fischer_algorithm
https://en.wikipedia.org/wiki/Michael_J._Fischer
QT:{{"
The original purpose of the algorithm described by Needleman and Wunsch was to find similarities in the amino acid sequences of two proteins.[1]
Needleman and Wunsch describe their algorithm explicitly for the case when the alignment is penalized solely by the matches and mismatches, and gaps have no penalty (d=0). The original publication from 1970 suggests the recursion …
A better dynamic programming algorithm with quadratic running time for the same problem (no gap penalty) was first introduced[3] by David Sankoff in 1972. Similar quadratic-time algorithms were discovered independently by T. K. Vintsyuk[4] in 1968 for speech processing ("time warping"), and by Robert A. Wagner and Michael J. Fischer[5] in 1974 for string matching.
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Visualization of Power Analysis http://amarder.GITHUB.io/power-analysis/ Useful sliders giving one a feel of the #statistics
How does multiple-testing correction work
http://www.nature.com/nbt/journal/v27/n12/abs/nbt1209-1135.html Intuition for teaching: genome-wide error rate on a single gene v family
