Notebooks and data to accompany Python instruction for Data in Social Context Fall 2018
Material is designed for teaching data and statistical thinking with Python to an undergraduate audience with mixed levels of code literacy (none to computer science major). The foundations lesson is adapted from Software Carpentry. The remaining lessons and assignments were created by Nathaniel Porter, Data Education Coordinator in the Virginia Tech University Libraries.
The NSync/BSB Datafile was created from the last.fm Million Song data subset. The Foundations_grades datafile is an anonymized version of scores for the Foundations and Dataframes in Python Assignment from three sections (taught together) in Fall 2018. Codebook for the grades data are in a separate xlsx file in the repository. The files for correlation and the final assignment are created from the million-song-data subset using Python.
I appreciate knowing if you use or adapt these, and will attept to respond to requests and issues. More files and documentation will be added throughout Fall semester 2018.
For reference, the commands to read two example datafiles directly into Python as Pandas dataframes are below: pd.read_excel('https://github.com/ndporter/pythonDiSC/raw/master/ns-bsb-data.xls') pd.read_excel('https://github.com/ndporter/pythonDiSC/raw/master/Foundations_grades_F18_anon.xlsx')