Dr. Gary King is the Director of Harvard’s Institute for Quantitative Social Science and will be giving a talk titled “Big Data Is Not About the Data!” as part of the MIDAS Seminar Series.
This Friday, October 7th, at 12:30pm, MIDAS and the CSS workshop have teamed up to bring you lunch with Dr. King!
If you are interested in joining us for lunch, please RSVP with this link.
CSCAR is offering skills sessions of interest to CSS workshop participants. Upcoming sessions in the CSCAR Data Science Skills Series include Data Management with Python and Pandas (Sept. 28), and Machine Learning in Python (Scikit-Learn) (Oct. 12). They’re also offering an entire series on high performance computing (HPC).
Meet Dr. Mathew Salganik and talk with him about his new book. Bit by Bit is in open review right now, and it can be freely accessed online here. Those planning to come are encouraged to read the book in advance, and should try to read at least the “Introduction” and “Observing Behavior” chapters. We will have a brief group discussion before the video conference with Dr. Salganik begins.
When: Monday, October 10, 2016, 3:30 – 5:00 PM
Where: Ross School, B4584
Come to our Launch Event, Thursday, October 6, 2016, 7-9 PM! Learn what the workshop is all about, hear about resources to support your work from a variety of campus offices, and most importantly, meet other people from across disciplines who are also interested in computational approaches to social science research. Plus, there will be free food.
Please RSVP Here
We are proud and grateful to announce that the Computational Social Sciences Workshop has been funded as a Rackham Interdisciplinary Workshop. This means more events, more guests, and more snacks! Stay tuned for calendar updates.
CSCAR has released its schedule of workshops for Fall 2016. Topics include R, Structural Equation Modeling, Survival Analysis, Hadoop, Parallel Programming, and more! These are great introductory how-to’s for tools and methods, so be sure to check them out!
The CSS has set up its own organization on Github to host code from workshop events. Check it out here: github.com/UM-CSS Email the organizers if you have something you would like to contribute!
We have also set up a google calendar for our upcoming events. Subscribe now so you’re always up to date!
As human activities increasingly migrate online, new forms of data emerge as traces of digital activity. Researchers are only beginning to understand the implications of this transition, and how models, methods, and questions in social science may be transformed as a result of it. Our Rackham Interdisciplinary Workshop would bring together a multidisciplinary community to undertake a study of the emerging field of Computational Social Science. We will couple learning about the data and methods germane to this area with discussion of their limitations, biases, and potential. Among our three collaborators we have an MS in computer science, an MA in statistics, and a decade of experience in technology heavy industry and research environments. We also recognize the importance of a critical perspective: What types of data, behaviors, or people are excluded in research using Big Data? How can we create alliances with data holding private companies and how might that change the research process? How can academics collaboratively build our own digital platforms to collect data and how can those platforms be validated? How can new forms of data contribute to theoretical conversations on poverty, democracy, efficiency, and power? Do these new data require new modeling frameworks, different from what has been historically used to analyze survey and administrative data? We propose to combine an examination of techniques and published research with constructive discussions of student research. Our aim is to build a constructive environment where students and faculty can learn about this new field, and talk through potential limitations and unresolved issues. In the coming years, Computational Social Science will greatly influence the pantheon of qualitative and quantitative methods.