Michigan’s a vibrant place, and there are many CSS-related events this fall besides those our workshop organizes. We’ve gathered a partial list of them here:
* Free CSCAR workshops are only announced one month in advance, so stay tuned for more.
We are excited to announce two more python skills workshops in partnership with CSCAR! In order to attend, participants should register for them as soon as they become available on the CSCAR website. Registration is free to UM affiliated people.
Data Science with Social Science data: an introduction to Python’s Pandas
Thursday, March 30th, 2-4 pm, MLB 2001A
This workshop introduces participants to Python’s NumPy, Pandas DataFrames, Matplotlib and StatsModels using an advertising dataset. Participants will use these tools to model (OLS) associations between advertising expenditures and product sales in example data. We will start with an introductory explanation of Anaconda and the Jupyter notebook environment (although not required for the participant, the instructor will be using these tools). We will proceed with topics including: reading data files; creation, indexing and slicing of Pandas DataFrames; creation and handling of Matplotlib objects; and creation and interpretation of models using Python’s StatsModels. Although not required, we recommend that participants have a basic knowledge of Python.
Data Science with Social Science data: building predictive models using Python’s Scikit-learn
Thursday, April 6th, 2-4 pm, MLB 2001A
We will use Python’s Pandas DataFrames, Matplotlib and Scikit-learn to analyze census data. Participants will use Scikit-learn tools to predict whether income exceeds a particular dollar amount based on the census data. This workshop covers the essential steps to building a predictive model in Python. We will start with an introductory explanation of Anaconda and the Jupyter notebook environment (although not required for the participant, the instructor will be using these tools). We will proceed with topics including: data analysis; creation and manipulation of Pandas DataFrames and Matplotlib objects and; creation and interpretation of predictive models using Python’s Scikit-learn. Although not required, we recommend that participants have a basic knowledge of Python and Pandas DataFrames.
We are excited to announce that the Computational Social Science RIW will be hosting a mini-conference on Thursday and Friday, April 6-7, 2017!
- The conference is open to people from the UM community at all levels and departments.
- We particularly encourage submissions from graduate students looking to get feedback on their works in progress.
- Work in early stages (e.g. project proposal or data analysis) is welcome–that’s often when we need feedback.
- Late-stage work is also welcome–this is a great opportunity to get feedback on that conference or journal paper before you send it out.
- Work about CSS (e.g. STS or research on data privacy) is welcome, even if you don’t personally use CSS methods.
- Sunday, February 5: CFP released.
- Wednesday, March 15, 11:59pm: Proposal and registration deadline.
- Thursday, April 6th, 2pm: Skills workshop: python for data analysis (with CSCAR)
- Thursday, April 6th, 6pm: Invited panel (more details soon!)
- Friday, April 7th, 11am-4pm: Open sessions and round tables (central campus)
Please share this invitation with anyone who may be interested.
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.
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.