Ever since we wrote an article in which we analyzed the articles which were been published in the Statistics Education Research Journal (Zieffler et al., 2011), I have been thinking about the relationships within the network of literature published on statistics education. What are the pivotal articles? Which are foundational? How inter-connected are the articles?
This spring I started documenting those relationships by putting together a social network of articles published in Technology Innovations in Statistics Education and the articles they referenced.
I just finished reading An Accidental Statistician: The Life and Memories of George E. P. Box. The book reads like he is recounting his memories (it is aptly named) rather than as a biography. I enjoyed the stories and vignettes of his work and his intersections with other statisticians. The book also included pictures of many famous statisticians (George’s friends and family—Fisher was his father-in-law for a bit) in social situations.
Research Hacks are a series of blog posts about some of the tools, applications, and computer programs that I use in my workflow. Some of these I began using when I was a graduate student, and others I have picked up more recently. This is the second post in the series (see the first post [Feedreaders and Aggregators](http://citizen-statistician.org/2013/04/22/research-hack-…nd-aggregators/ ).)
Electronically managing the absurdly large volume of articles, reports, book chapters and other writings that academics procure is a huge way to save time and increase production.
DataFest is growing larger and larger. This year, we hosted an event at Duke (Mine organized this) with teams from NCSU and UNC, and at UCLA (Rob organized) with teams from Pomona College, Cal State Long Beach, University of Southern California, and UC Riverside. We are very grateful to Vaclav Petricek at eHarmony for providing us with the data, which consisted of roughly one million “user-candidate” pairs, and a couple of hundred variables including “words friends would use to describe you”, ideal characteristics in a partner, the importance of those characteristics, and the all-important ‘did she email him’ and ‘did he email her’ variables.
I have been thinking for several years that I should put together a series of blog posts about some of the tools, applications, and computer programs that I use in my workflow. Some of these I began using when I was a graduate student, and others I have picked up more recently.
I wanted to initially do this to share these tools with our graduate students at the University of Minnesota.
Daniel Kaplan and Libby Shoop have developed a one-credit class called Data Computation Fundamentals, which was offered this semester at Macalester College. This course is part of a larger research and teaching effort funded by Howard Hughes Medical Institute (HHMI) to help students understand the fundamentals and structures of data, especially big data. [Read more about the project in Macalester Magazine.]
The course introduces students to R and covers topics such as merging data sources, data formatting and cleaning, clustering and text mining.
I’m often on the hunt for datasets that will not only work well with the material we’re covering in class, but will (hopefully) pique students' interest. One sure choice is to use data collected from the students, as it is easy to engage them with data about themselves. However I think it is also important to open their eyes to the vast amount of data collected and made available to the public.
