The Computational Social Science Workshop is a forum to bring together faculty and graduate students from a wide array of disciplines to discuss and learn practical applications of diverse computational methodologies with social data.
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.
- Mini-Conference CFP
- List of Summer CSS Opportunities
- Women in Data Science Event
- Resources for Python Data Science Skills Sessions
- Skills Sessions: Data Science with Social Science Data
- Princeton Resources for CSS
- Lunch with Gary King
- CSCAR Data Science Skill Series
- Graduate Student Discussion with Dr. Mathew Salganik