Difference between revisions of "Conferences"

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== Open Data Science Conference 2016 ==
 
== Open Data Science Conference 2016 ==
  
* reproducible computing in data science, by [https://github.com/dougmet Douglas Ashton]
+
* reproducible computing in dathttp://pipeline.io/a science, by [https://github.com/dougmet Douglas Ashton]
 
** [https://github.com/dougmet/vagrantR/ Using Vagrant (with R)]
 
** [https://github.com/dougmet/vagrantR/ Using Vagrant (with R)]
 
* An intro into [https://www.opendatascience.com/blog/single-layer-neural-networks-and-gradient-descent/ Single Layer ANNs and Gradient Descent]
 
* An intro into [https://www.opendatascience.com/blog/single-layer-neural-networks-and-gradient-descent/ Single Layer ANNs and Gradient Descent]
 
* [http://inverseprobability.com/talks/lawrence-osdc16/three-challenges-for-open-data-science.html 3 Challenges for Open Data Science] by Neil Lawrence -- mostly stuff that I found relatively well known already, but the mention of the ratio of information processing to information transmission was very welcome to me.
 
* [http://inverseprobability.com/talks/lawrence-osdc16/three-challenges-for-open-data-science.html 3 Challenges for Open Data Science] by Neil Lawrence -- mostly stuff that I found relatively well known already, but the mention of the ratio of information processing to information transmission was very welcome to me.
 
* [https://www.opendatascience.com/blog/the-role-of-constructivism-in-teaching-data-science-for-iot/ The Role of Constructivism in Data Science] -- should there be one, really? Constructivism has two faces / interpretation, one is the outright denial of any objectivity and the other is a kind of acknowledgement of subjectivity. There should be no role for the former in any kind of science, but the latter may have some points.
 
* [https://www.opendatascience.com/blog/the-role-of-constructivism-in-teaching-data-science-for-iot/ The Role of Constructivism in Data Science] -- should there be one, really? Constructivism has two faces / interpretation, one is the outright denial of any objectivity and the other is a kind of acknowledgement of subjectivity. There should be no role for the former in any kind of science, but the latter may have some points.
 +
* [https://github.com/orgs/fluxcapacitor/people Chris Fregly] gave a talk / demo of a [https://github.com/fluxcapacitor/pipeline/wiki Data Science pipeline] comprised of a huge number of data-sciencey components. It was not clear to me whether the demo application really required any of these high performance / high throughput / highly scalable components, but it was surely interesting to see an example of tying all these together. See also [http://pipeline.io/] (I think they're related).
 +
* [https://prometheus.io/]
 +
* [https://graphiteapp.org/]

Revision as of 08:51, 13 November 2016

This page contains interesting stuff I came across at conferences.

Open Data Science Conference 2016

  • reproducible computing in dathttp://pipeline.io/a science, by Douglas Ashton
  • An intro into Single Layer ANNs and Gradient Descent
  • 3 Challenges for Open Data Science by Neil Lawrence -- mostly stuff that I found relatively well known already, but the mention of the ratio of information processing to information transmission was very welcome to me.
  • The Role of Constructivism in Data Science -- should there be one, really? Constructivism has two faces / interpretation, one is the outright denial of any objectivity and the other is a kind of acknowledgement of subjectivity. There should be no role for the former in any kind of science, but the latter may have some points.
  • Chris Fregly gave a talk / demo of a Data Science pipeline comprised of a huge number of data-sciencey components. It was not clear to me whether the demo application really required any of these high performance / high throughput / highly scalable components, but it was surely interesting to see an example of tying all these together. See also [1] (I think they're related).
  • [2]
  • [3]