Teaching quantitative and computational skills to undergraduates using Jupyter Notebooks.

As the data that we collect dramatically increases in both quantity and complexity, all college graduates will need more quantitative and computational skills to be productive and successful members of society. I will present my experience using the open source Jupyter Notebook system as an undergraduate instructor and mentor. My colleagues and I have developed inquiry based, active learning materials in Jupyter Notebooks to teach coding and quantitative skills to undergraduate students at several different levels. Our materials address basic calculation and graphing skills, reproducible research, and include a semester long scientific computing course using Python. Jupyter Notebooks have several advantages for teaching and learning over traditional coding in the shell or an IDE. The system is relatively easy to install, combines text, code, and output all in one place that is easily exportable. It also makes it easy to guide students through solving problems with code and to see students’ thought process as they work through everything from simple exercises to complex data analysis projects. We see Jupyter Notebooks as an easily accessible tool to get students at various levels engaged in doing data science.
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