The scientific Python ecosystem has made a Python a great language for data analysis, encouraging readable and expressive code, but data analysts struggle with applying Python test frameworks to their data transformation and analysis code. This talk will give a good starting point: how to compare different versions of tabular data and turn that into automated tests.
Stephen Childs has been working with Python for the past three years, primarily using the language for data analysis. Originally from Thunder Bay, Ontario, Stephen has called Toronto, Vancouver, Kitchener-Waterloo, Ottawa home. Currently he works at the University of Calgary as an Institutional Analyst. Stephen uses Python for many aspects of the job, but it's main use is to apply predictive models to university data. Stephen is the founder and one of the organizers of PyData Calgary, which promotes of the use of Python and other Open Source tools for data analysis.
