Request for Comments: Data Science Curriculum v2

[waciumawanjohi]

Problem:
The curriculum has not been maintained and does not represent best practice.

Duration:
2020-08-31

Background:
OSSU recommends courses that would constitute an undergraduate major in Data Science. It is our responsibility to ensure that we follow best practice. To do so, we must bring the curriculum into alignment with external guidelines. A candidate set of guidelines has been identified and previously proposed.

In 2017, the Annual Review of Statistics and Its Application published the report "Curriculum guidelines for undergraduate programs in data science." The report was authored by “25 undergraduate faculty from a variety of institutions in the United States, primarily from the disciplines of mathematics, statistics, and computer science.” It had a goal of providing “structure for institutions planning for or revising a major in data science.”

The current state of OSSU Data Science is one of disrepair. The curriculum has had 1 change in 3 years. That change deleted a link to a broken application. But there remained many links to courses that are no longer offered. A list of these can be found here. Prospective students have posted in the issues asking if the Data Science curriculum is still maintained. Updating the curriculum must ensure that all courses are available for students.

Proposal:
OSSU Data Science should adopt “Curriculum guidelines for undergraduate programs in data science” (CGUPDS) as our guidelines. The curriculum should be updated to match. The exact changes can be reviewed in this pull request.

[spamegg1]

It might be helpful to have a direct link to the guidelines so it's easier to read and comment:
https://www.amstat.org/asa/files/pdfs/EDU-DataScienceGuidelines.pdf

Very short and nice read, just 16 pages.

Looks like you already made all the necessary course changes in the pull request. All the links are alive and the courses cover the Key Competencies on Page 6, and the Six Main Subject Areas & Outline on Page 9 very well. Really excellent work!

The topic progression path in the pull request looks somewhat different than the possible path in Figure 1, Page 12 of the guidelines. I think it's fine, but maybe you could explain a little bit for those that are curious?

One question to which I do not see an immediate answer is, where would the "Capstone Experience" and "Course in an outside discipline" mentioned in the Outline on Page 9 come from? Are they contained in some of the courses in the curriculum?

By the way I don't know much about data science at all, and I don't have a horse in this race. Just trying to be helpful.

[mattjperez]

For suggested changes at #60 (comment) regarding Algorithms Part 1 and 2

These courses are completely in Java and have a steep learning curve from the start. Those coming straight from python/r/julia will have a hard time adjusting to both the course materials and programming syntax. Suggest an optional course on Java as pre-requisite, specifically Java Programming I and II by University of Helsinki. Gives college credit for Finland residents.

[waciumawanjohi]

I certainly think adding resources in Extras for teaching Java would be appropriate. As well, adding a note in the main curriculum that those resources are available.

The University of Helsinki courses are high quality and I have no objection to listing them as a resource.

One other option to keep in mind is Computer Science: Programming with Purpose. One thing to recommend this alternative is that it is taught by the same instructor as the Algorithm courses. This could be used instead of Introduction to Computer Science and Programming Using Python and Introduction to Computational Thinking and Data Science, or in addition to them.

[mattjperez]

Yeah, that's not an easy choice. The MITx pair go well together as a series just like Sedgewick's series.

On one hand, MITx uses python which is what most people will be programming with but Intro to Computational Thinking might not be as rigorous as the alternative and covers a range of things implemented in python (distributions, monte carlo, etc). This would be helpful for the DS student as it'll give more practice in a language they'll definitely encounter. It'll also reinforce topics covered in probability and statistics.

On the other, Sedgewick's will give you a very thorough understanding of algorithms specifically and the textbooks are available online, with updates and resources. Learning Java will also be good for anyone that will work at larger companies and be exposed to these types of codebases, so this route would be good for something you 'might' encounter.

Personally, I think the Sedgewick combination would be best in the CS curriculum, mainly because it's more aligned with CS than DS in my opinion as I don't think they're as necessary for machine/deep learning. They would be if you were programming the libraries themselves, but that's why I think they're more relevant for CS.

Definitely would suggest having in the DS curriculum as an extra though.

[staycul]

For suggested changes at #60 (comment) regarding Algorithms Part 1 and 2

These courses are completely in Java and have a steep learning curve from the start. Those coming straight from python/r/julia will have a hard time adjusting to both the course materials and programming syntax. Suggest an optional course on Java as pre-requisite, specifically Java Programming I and II by University of Helsinki. Gives college credit for Finland residents.

Head first Java might be an excellent option and beginner-friendly

[jromani-ds]

Regarding the Algorithms section.

The OSSU route for CS suggests the Algorithms specialization from Stanford on Coursera: https://www.coursera.org/specializations/algorithms

The DS major suggests Algorithms 1 & II from Princeton on Coursera: https://www.coursera.org/learn/algorithms-part1

Would there be value in using the same set of courses to cover algorithms between both programs?

[waciumawanjohi]

Would there be value in using the same set of courses to cover algorithms between both programs?

Yes, there would be. While Discord channels for the Data Science individual courses have not been added yet, they will be in the future. If Data Science and Computer Science students are in the same course, they can be in the same discussion rooms, increasing critical mass for productive peer learning.

The natural next question is: Why does the proposal include a different algorithms course for Data Science?

Essentially, computer scientists need to know more about complexity and computability than data scientists do. Some CS2013 requirements are:

• Greedy algorithms
• Dynamic Programming
• Introduction to the P and NP classes and the P vs. NP problem
• Introduction to the NP-complete class and exemplary NP-complete problems (e.g., SAT, Knapsack)

These match up with the 3rd and 4th Stanford algorithms courses, which teach:

• Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming
• Shortest Paths Revisited, NP-Complete Problems and What To Do About Them

The CGUPDS, by contrast requires:

• Programming concepts and data structures: Students should have the knowledge to implement their algorithms using procedural and functional programming techniques and their associated data structures, including lists, vectors, data frames, dictionaries, trees, and graphs.**

This is a decent fit for Princeton's Algorithms which teaches:

• Chapter 1: Fundamentals introduces a scientific and engineering basis for comparing algorithms and making predictions. It also includes our programming model.
• Chapter 2: Sorting considers several classic sorting algorithms, including insertion sort, mergesort, and quicksort. It also features a binary heap implementation of a priority queue.
• Chapter 3: Searching describes several classic symbol-table implementations, including binary search trees, red–black trees, and hash tables.
• Chapter 4: Graphs surveys the most important graph-processing problems, including depth-first search, breadth-first search, minimum spanning trees, and shortest paths.
• Chapter 5: Strings investigates specialized algorithms for string processing, including radix sorting, substring search, tries, regular expressions, and data compression.
• Chapter 6: Context highlights connections to systems programming, scientific computing, commercial applications, operations research, and intractability.

I think that curricular fit here is an overriding concern, but I'm interested to hear the opposing case.