A New Wiki for Computer Science Symbols

Computer science is increasingly relevant to a wide range of professional fields, yet many working programmers today do not have a formal CS education. This makes it difficult for the uninitiated to read academic research in computer science and related fields. Keeping up with the latest research is not a job requirement for most programmers, but understanding fundamental papers (such as the ones listed on Papers We Love) is important for building on established knowledge.

However, jargon and unfamiliar symbols present a non-trivial barrier to entry. This came up in the discussion on a recent episode of the Turing Incomplete podcast. A few existing resources were mentioned such as Wikipedia’s math symbols page and Volume I of The Art of Computer Programming. None of these is ideal for new programmers who may not know the names of the symbols, though.

That’s why I started a CS notation wiki. There are currently four pages, one each for computational symbols, linguistic symbols, logical symbols, and mathematical operators. Each page currently only has a few entries, but requests for additional ones can be filed as Github issues. New contributions are certainly welcome, and should be submitted as pull requests. Contribution guidelines can be found on the wiki’s home page. Other suggestions can be submitted as comments here, via email, or on Twitter. Let me know how this could be more useful to you!

Academia to Industry

Last week, Brian Keegan had a great post on moving from doctoral studies to industrial data science. If you have not yet read it, go read the whole thing. In this post I will share a couple of my favorite parts of the post, as well as one area where I strongly disagreed with Brian.

The first key point of the post is to obtain relevant, marketable skills while you are in grad school. There’s just no excuse not to, regardless of your field of study–taking classes and working with scholars in other departments is almost always allowed and frequently encouraged. As Brian puts it:

[I]f you spend 4+ years in graduate school without ever taking classes that demand general programming and/or data analysis skills, I unapologetically believe that your very real illiteracy has held you back from your potential as a scholar and citizen.

Another great nugget in the post is in the context of recruiters, but it is also very descriptive of a prevailing attitude in academia:

This [realizing recruiters’ self-interested motivations] is often hard for academics who have come up through a system that demands deference to others’ agendas under the assumption they have your interests at heart as future advocates.

The final point from the post that I want to discuss may be very attractive and comforting to graduate students doing industry interviews for the first time:

After 4+ years in a PhD program, you’ve earned the privilege to be treated better than the humiliation exercises 20-year old computer science majors are subjected to for software engineering internships.

My response to this is, “no, you haven’t.” This is for exactly the reasons mentioned above–that many graduate students can go through an entire curriculum without being able to code up FizzBuzz. A coding interview is standard for junior and midlevel engineers, even if they have a PhD. Frankly, there are a lot of people trying to pass themselves off as data scientists who can’t code their way out of a paper bag, and a coding interview is a necessary screen. Think of it as a relatively low threshold that greatly enhances the signal-to-noise ratio for the interviewer. If you’re uncomfortable coding in front of another person, spend a few hours pairing with a friend and getting their feedback on your code. Interviewers know that coding on a whiteboard or in a Google Doc is not the most natural environment, and should be able to calibrate for this.

With this one caveat, I heartily recommend the remainder of the original post. This is an interesting topic, and you can expect to hear more about it here in the future.

How Much Math is Enough?

Regular readers know that education policy is not my forte (although I have expressed some opinions), but there was a confluence of articles over the weekend that I feel the need to discuss here. The first was Andrew Hacker’s outrageous op-ed in the New York Times suggesting that freshman college algebra is unnecessary. It contained such doozies as this:

It’s not hard to understand why Caltech and M.I.T. want everyone to be proficient in mathematics. But it’s not easy to see why potential poets and philosophers face a lofty mathematics bar. Demanding algebra across the board actually skews a student body, not necessarily for the better.

OK, not everyone is going to be an engineer. But algebra? Come on. Virtually everyone who goes to college should have encountered algebra in high school. Now, I might be biased by this because lately I have found myself wishing I had taken much more math as an undergraduate. But even though calculus might not be necessary for everyone, the ability to solve for a single variable seems essential in everyday life. (I could provide examples, but they would cover only a small subset of the things for which algebra can be used.)

John Patty has a better response than anything I could come up with at the moment, so go read it. If anything Patty is softer on Hacker than I would be, but he does a great job pointing out the logical flaws in Hacker’s argument.

And finally, here is a piece from 2008 on the innumeracy of college professors. The bias against math and science in humanities departments is palpable. To be fair, many physicists and mathematicians have a similar antipathy toward the liberal arts. But if we are going to require art history, music appreciation and the like to broaden the minds of students (a requirement that I am not opposed to), then it would be a crime to omit such basic life skills as algebra. I am not saying that we all need to become engineers, but encouraging students to forgo math does not bode well for our future.

Wednesday Nerd Fun: How Real is Your Field of Study?

Nothing gets a good nerdfight going like whose academic discipline is more real. Since the Gawker published this list earlier this month, the heat has hopefully died down enough for people to enjoy the rivalry. My own field comes in at #26, just below “Foreign language (Useless type)” but right above “Drama or film.”

At least we weren’t consigned to the group of “completely fake fields of study,” which were left off the list entirely. Scroll down for Sheldon Cooper’s take on the social sciences.

1. Physics
2. Astronomy or other Space Science
3. Philosophy
4. Engineering
5. Math
6. History
7. Chemistry
8. Biology or other Life Science
9. Foreign language (Useful type)
10. Computer Science
11. Agriculture
12. Geology or other Earth Science
13. Architecture
14. Literature
15. Law
16. Geography
17. Music
18. Economics
19. Study of Some Foreign Place or Culture
20. Archaeology
21. Anthropology
22. Religion or Theology
23. Art
24. Education
25. Foreign Language (Useless type)
26. Political Science
27. Drama or Film
28. Phys Ed, Sports Management or other Major Designed For Athletes
29. Journalism or “Communications”
30. Business
31. Psychology
32. Sociology

Micro-Institutions Everywhere: Time and Lateness

Readers familiar with Latin America or the Middle East will recognize phrases like “mañana,” “Arab time,” or “island time.” All of these connote a local understanding of time that differs from the to-the-minute accuracy  (well, almost) that the West has grown accustomed to since clocks became commonplace. It turns out that these differences influence the amount of precision expected when asking someone the time, or in measuring lateness. From Psychology Today:

For most Americans, “there are eight time sets in regards to punctuality and length of appointments: on time, five, ten, fifteen, twenty, thirty, forty-five minutes, and one hour early or late.” …

Moroccans in the study were more likely than Americans to mentally partition an hour into 15-minute segments.  This may explain, at least in part, why Moroccans and other Arabs are often less punctual than Americans.  An American who arrives 10 minutes after the appointed time is late by “two units of psychological time.”  A Moroccan who is also running late by two units will arrive 30 minutes (“two quarters of an hour”) after the appointed time.

The expression “psychological time” feels a little fuzzy but the overall idea seems plausible: that lateness depends on how many chunks of an hour your culture breaks time into. It can be very hard for travelers from the West to adjust to a different understanding of time in a foreign culture, which often leads to frustration. I know less about how time differences challenge others when they come here. My Arabic teachers were often late, but that may be due to another stereotype–the absent-minded professor. Do these differences operate at a macro-scale, affecting the amount of time considered reasonable for accomplishing a political goal?

The Structure of Academic Conversation

Or, “What is the purpose of academic journals?”

[Please note: This is a question that has been on my mind for a while, so this is the first part of what is likely to become a multi-post series. It has been on many other, smarter, more experienced academic minds as well, as you will see from the links in each post.]

Tyler Cowen and John Sides have both given some attention to a paper Julie Suleski and Motomu Ibaraki, both of The Ohio State University. The paper (here, gated–Tyler notes the irony) looks at three questions: 

1. How many scientific papers that were published in peer-reviewed journals made it to a mainstream audience?

2. What percentage of papers was represented in the mainstream news media?

3. Since the number of papers published has increased, has the number of papers reported on increased?

Rather than answer their questions–not all of which I’m sure are particularly relevant, for reasons that should become obvious later in the series–I will selfishly merely use this as a jumping off point for some reflection on the purpose of academic journals. My own research as you know is in the area of social sciences, but I will attempt to make remarks that are applicable to the academic community in general, and offer a disclaimer when I have social or political science exclusively (rather than primarily) in mind.

The first principle to keep in mind is that disagreement saps energy. In Christopher Groskopf’s inaugural post over at Hack Tyler earlier this year, describing his decision to move from Chicago to Tyler, Texas, he explained that he was going in with the expectation that he would have “to spend a great deal of time actively disagreeing with people.” Christopher stated this with full awareness that the energy devoted to disagreement would not be considered productive in the traditional sense (I say this with respect for both his decision and his project). People tend to exhibit a basic understanding of this principle when they express disdain for partisanship in Washington, wishing that politicians would “stop arguing and get things done,” but I for one am fine if they keep arguing and do very little else–but I digress.

This same energy required by disagreement draws upon reserves that could otherwise be put toward creative pursuits. If you think of a finite supply of resources–say, a 24 hour day–any one of those that you devote to arguing with someone detracts a unit of the resource that could be spent painting/writing/filing taxes/whatever. The point is not that disagreements are always bad. Indeed, sometimes disagreement and creativity are complementary; for instance, Galileo’s disputes with the Catholic hierarchy were necessary. But could he have gotten more done if he were permitted to spend more time looking at the stars and less time arguing with bishops? Probably. The point is that people who are engaged in creative/productive pursuits will want to avoid debates over matters that they regard as settled or trivial in order to move on to new problems that they consider a better use of their time.

Having to retrace every step along the development of the modern scientific method, while not a debate per se (it was, but that isn’t the point), would be a distraction or a waste of time. At some point these settled or trivial matters accumulate, words take on technical meanings, and the discourse of the problem-solving or -exploring community becomes too sophisticated for the general educated person.

Basic rhetoric tells us that any piece of language consists of three primary elements: audience, purpose, and genre. By directing their writing to an audience with whom the author shares at least a minimum of basic assumptions about the world, the pursuit of knowledge, and the professional conduct of contemporary scientists, authors are able to significantly reduce the effort required to generate their reports/articles. Rather than retrace each of the elements learned in their decade-long process of becoming scientists that are crucial to the topic at hand, they can simply address individuals who already share that common pool of basic science education, and can thus read the article intelligently without a lot of background. Thus, more time can be spent at the frontiers of research rather than retracing old arguments. Thomas Kuhn describes this process in his book, The Structure of Scientific Revolutions:

In the sciences (though not in fields like medicine, technology, and law, of which the principal raison d’être is an external social need), the formation of specialized journals… [has] usually been associated with a group’s first reception of a single paradigm…. No longer will [the scientist’s work] usually be embodied in books addressed… to anyone who might be interested in the subject matter of the field. Instead they will usually appear as brief articles addressed only to professional colleagues, the men whose knowledge of a shared paradigm can be assumed and who prove to be the only ones able to read the papers addressed to them. (1996: 19-20, see also p. 163ff.)

This argument is simply stated, and of course its implications are nontrivial (which is not to say original with me), but that’s why we have the rest of this series.