Tagged data science curriculum

Keeping Data Science Broad-Webinar

Please join me and other data science program directors for an educational webinar exploring undergraduate programs.

Keeping Data Science Broad: Data Science Education in Traditional Contexts | Aug 31, 2017 | Virtual
This webinar will highlight data science undergraduate programs that have been implemented at teaching institutions, community colleges, universities, minority-serving institutions, and more. The goal is to provide case studies about data science degrees and curricula being developed by primarily undergraduate serving institutions. Such institutions are crucial connectors in the establishment of a robust data science pipeline and workforce but they can have different constraints than large research-focused institutions when developing data science education programming.

More details about the webinar will be posted soon on the South Hub website: http://www.southbdhub.org/datadivideworkshop.html

(Other) Official Curriculum Guides

Last week I discussed several places from which you could pull curriculum planning materials. This week will continue that theme, but with a bit more of an ‘official’ flavor, by discussing several professional societies’ curricular guides. While there is no (clear) leading data science professional society (and none with curricular guidelines to my knowledge), there are a few closely related societies with official guidelines. Depending on what path you took into data science, you may be more or less familiar with the following societies: Association of Computing Machinery (ACM), Institute of Electrical and Electronics Engineers (IEEE), Mathematical Association of America (MAA), and the American Statistical Association (ASA), . There are several other societies relevant to data science, but not as vital in terms of official curricular guidelines (SIAM, INFORMS, AMS, ASEE). All four major societies (ACM, IEEE, MAA, and ASA) have released curricular guidelines relevant to data science. This post will give a very high level overview of those guidelines and why you might care about what’s in them.

ACM and IEEE jointly released Curriculum Guidelines for Undergraduate Programs in Computer Science in 2013 (CS2013). The most valuable component of CS2013 for me is the specification of ‘Knowledge Areas’ that are obviously related to Data Science, and being able to see the professional community’s consensus on central learning objectives in these areas. Some clearly important/relevant areas are:

  • Computational Science
  • Discrete Structures
  • Graphics and Visualization
  • Information Management
  • Parallel and Distributed Computing

Other areas such as Algorithms and Complexity, Information Assurance and Security, or Programming Languages probably include specific learning objectives that are relevant to data science, but may not be needed in their entirety. Additionally, CS2013 allows you to to examine the suggested course hours expected to be devoted to these topics. From an industry perspective, this can provide valuable insight into whether a data scientist or computer scientist might be more knowledgeable about a particular subject. This differentiation in knowledge is important as data science strives to define itself independently of its founding disciplines. If you are interested in throwing your net a bit wider, ACM also has guides for other programs like Computer Engineering and Information Technology (coming in 2017) on their guidelines site.

The MAA’s 2015 Committee on the Undergraduate Programs (CUPM) in Mathematics Curriculum Guide to Majors in the Mathematical Sciences — CUPM Guide for short — can serve in largely the same way the CS2013 guide does, but from a mathematical/statistical approach. With more detailed reports on Applied Mathematics, Computational Science, Operations Research, and other areas of mathematics that data science often operates in, the CUPM Guide makes it possible to understand what exactly (from a mathematician’s or computational mathematician’s perspective) are the most relevant areas of mathematics to understand for success. This guide can also serve to help clarify exactly what sorts of mathematics courses a data science curriculum should require, by explaining where in the course structure specific topics like sets, relations, and functions, or other ideas get covered. In addition to their extensive undergraduate guide the MAA also provides a lot of interesting materials related to masters/Ph.D preparation, etc. These might be particular interesting as you consider what sorts of students to recruit or include in a master’s program.

Finally, the ASA has perhaps the most relevant and diverse, but in many ways least detailed, set of curriculum guides. The set of undergraduate guidelines and reports include how to assess instruction, program guidelines for statistical sciences, and even the Park 2016 Data Science guidelines (which I have commented on in other posts). They also have two sets of graduate guidelines from 2009 and 2012 for statistics masters/Ph.D. programs. What the ASA guidelines provide are much bigger, sweeping statements about the sorts of skills and knowledge that a statistics major should have. It includes side notes that give more details such as encouraged programming languages and even file formats. In many ways, I think the majority of the ASA guidelines could just replace “Statistics Major” with “Data Science Major” and remain nearly as applicable. The biggest difference might be in the level/depth required in “Statistical Methods and Theory” (less) and “Data Manipulation and Computation” (more). In a sense, this is at the heart of many statistician’s argument that “Data Science” isn’t really its own field. In practice though, I think the final implementation and mindset behinds a statistics major and a data science major will be very different, and certainly heavily influenced by the ‘host’ department.

That covers the breadth of the major professional societies’ curricular recommendations. I wasn’t able to find any (official) guidelines for a “business analytics” major from a professional society (see my resource page for a few unofficial documents), so if you know of one, please let me know.

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!

 

Blog Intro and Information

Welcome to “From the Director’s Desk” a blog about data science education and curriculum. If you are interested in receiving regular updates when new posts appear you can use the RSS feed link above, or subscribe to the google-group (read more for the link, you don’t need a gmail-account to subscribe!). You can find a bit more about me, Karl Schmitt on the about page. If you are looking for full degree curriculum development materials I’ve created a resource page and tracked posts with a Program Development category. Individual course materials are tracked either generally with the “Course Development” category, or individually by each course the post relates to. Please feel free to email me or leave comments if you have questions, thoughts or something to share!

The original blog introduction, with a bit of why the blog exists and what it seeks to cover is here.