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.