Curriculum Mapper

Context:

With the overarching intention of democratizing learning of data science (and adjacent fields), listing core competencies and noting inquiry practices is a first step. Mastery over a domain reveals interconnections between constructs, often revealed only after immersing in the topic for a long time.

A core challenge when self teaching as a novice in a domain is the visualization and articulation of dependencies between core competencies.

This python class allows learners or researchers to create (through a python script) a self guided learning map, choosing with interest but also expert guidance on dependencies.

For example, if a student wishes to independently learn about stochastic models, they can find the course, and use the visual map to trace out all the prerequisite skills and topics.

Next Steps:

• Compositing multiple Universities’ curricula: Can these networks be used to study the underlying topics? Perhaps a LDA analysis? Can this project be extended to compare different schools’ topics?
• Adding sources: Can we tie the topic information to scholarly articles and textbooks?
• Combining scholarly crawler information to look at citation maps and topics and how that relates to university curricula summary map