This is a nice infographic about gamification in education. I am curious to watch how gamification plays a role in the online education scene at places like Udacity and Coursera. I think gamification is going to lead to many new and interesting problems in bigdata.
Created by Knewton and Column Five Media
Thomas Thurston put together a very nice video about data science, its uses, and the possible impacts.
Data Science – Beyond Intuition from Thomas Thurston on Vimeo.
A virtual information session about the Data Science Certificate Program at the University of Washington is today. If you are interested in the program, it is probably worth your time to attend.
David Easley and Jon Kleinberg, both of Cornell University, have placed the contents of their social networking textbook online. All 24 chapters of Networks, Crowds, and Markets: Reasoning About A Highly Connected World are available for download. This could serve as a wonderful learning resource or an excellent reference tool. The material covered is quite extensive, and it provides many real applications of social network analysis. Not all the examples are online social networks.
Paco Nathan put together a nice slide deck about Data Science for Enterprise Big Data.
Slide 9 contains a great list of valuable skills for a data scientist. Also, it is worth going through the entire set of slides, since slides 48 and 49 contain a valuable list of tools and algorithms. Enjoy!
Data Collective is a new investment fund. It is strictly for early-stage Big Data startups. See the TechCrunch announcement for more detailed information.
The University of California at Berkeley is hosting AMP Camp, the Big Data Bootcamp, starting today. The conference is sold out for in-person attendace, but registration is free and live streaming is available. The agenda looks good (including machine learning, parallel programming, Mesos, and hands-on exercises), so this might be a good opportunity for some learning.
Thanks to Mark Nickel for the link.
During the Spring 2012, Alex Smola taught a course at Berkeley on Scalable Machine Learning. Alex is an Adjunct Professor at the University of California at Berkeley and a Visiting Scientist at Google.
Alex was kind enough to put all the course materials on the internet. That includes papers, slides, links, and video lectures. Like the title suggests, the course appears to focus on large-scale machine learning. Below is one of the lectures from the Statistics portion of the course.
A while back, Strata hosted a web conference titled Data in Motion. The slides and audio are now available online. The conference is focused on unique applications of data used for movement. Examples are: trains, aerospace, and even car racing. The first talk on formula one car racing was fascinating. I had never thought about the amount of data analysis that goes into racing.