Course/Curriculum Resource Sites

Last week I posted about specific websites you might use to host or pull assignments from. This week I want to take a broader look at overall curriculum design. This is by no means a comprehensive posting of sites that have curriculum available, instead it’s intended to help reduce your search time for this kind of material.

If you are looking to find wholesale curriculums, including course materials, there are a few options available to start the creative juices flowing. The first, and probably most academic, is the European Data Science Academy (EDSA). The EDSA is grant funded with a large number of academic (university) and research institute partners from across Europe. The thing I like best about this work is that they started with a demand analysis study of the skills needed and current jobs in data science across the EU. Furthermore, from the start the project built in a feedback and revision cycle to improve and enhance the topics, delivery, etc. To understand their vision, see the image below.

This idea of continual improvement was more than just a grant seeking ploy as shown by their list of releases, revisions, and project deliverables. While the current site still lists four learning modules as unreleased, they are expected July 2017.

Overall, their curriculum structure (I haven’t evaluated their deeper content) has a fairly high emphasis on computational topics, with less statistics/mathematical underpinnings. You can experience their curriculum directly (it’s free/open access) through their online course portal. What might be far more valuable though is their actual grant’s deliverables. These deliverables include details on the overall design principles in their structure with learning objectives, individual courses with their own learning objectives, descriptions of lesson topics/content and more. Using their outlines and ideas to guide your own construction of a curriculum is both reasonable and a great way to make sure you aren’t missing any major, important topic, however, this should be done with proper attribution and license checking (of course).

The other two places to look for curricular inspiration are also in the ‘open source’ category, but not funded by grants or (traditional) academic institutions. The Open Source Data Science Masters was constructed by Clare Corthell, who has gone on to found his own data science consulting firm and other initiatives. While not every link on the site is actually to a free resource (there’s several books to buy etc), it does a pretty nice job of highlighting the topics that will need to be covered (if possible), and provides lots of places to start pulling course materials from (or getting inspiration/ideas for content). The primary curriculum is python focused, however he also has a collection of R resources.

Corthell isn’t the only one though with an “open source” or “free” data science (masters) degree. Another collection of relatively similar material was collected by David Venturi, who’s now a content developer at Udacity (writing data science curriculum of course). For those designing curriculums, both Corthell and Venturi provide excellent resources and places to frame your learning. However if you hit this page trying to get into data science, read this Quora post that I think accurately highlights the challenges of learning from/with these open source programs.

Another similar alternative, that I’d peg closer to an undergraduate degree, is the Open Source Society University‘s data science curriculum. Their curriculum assumes a lot less pre-knowledge in mathematics and statistics, providing links for Calculus, Intro Statistics, etc. This content is probably more in-line with the recommendations for curriculum from the Park’s paper (see my Curriculum Resources page). What I particularly like about this (from a learning perspective) is that it actually details the amount of work per week required to learn from each course. You’ll see a large repetition of topics, but the OSS-Univ’s curriculum has a lot less advanced material, with only a few courses in big data, wrangling, etc.

At the end of the day, if you are looking to implement an undergraduate or graduate degree in data science, your university is going to have to offer duplicates of a significant subset of classes from these curriculums. While emulation might be the highest form of praise, we’ll each need our own, unique take on these courses while striving for sufficient similarity to have a semi-standardized knowledge base for practitioners. Good luck!

 

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