(EDIT: images updated with 2174 data.)
Here’s a picture of (lecture, non-supervisory) sections taught by CSSE faculty since Fall 2008:
Because the number of sections taught has gone up rather dramatically, here’s the same data, normalized by sections per year, to show the fraction of classes taught in each category:
There are many caveats and things to explain about these pictures.
Following Zoe’s excellent suggestion, I added color to the course dependency chart:
The arrows should be the same as the previous chart, though you’ll notice that because of the way I generated it, the chart now includes the classes that don’t have any edges connected to them.
The colors relate to the number of students that have enrolled in each class. Specifically, the log of the number of students that have taken the class from fall 2010 through spring 2016. Classes with zero enrollment are given log(0.5). The class with the highest value is red, the one with the lowest value is green. Yes, there should be a legend. Classes that aren’t in the Computer Science catalog are gray.
Note that I have no idea how many of these enrolls are repeats. That would be interesting, but I’ll need a different data source to answer that question.
The numbers for each class are interesting, and I think I’ll just publish them separately.
(EDIT: updated with 2174 data.)
This picture shows the evolution of class sizes in classes taught by Computer Science faculty since Fall 2008. Specifically, it shows the likelihood that a randomly chosen scu will belong to a class of a given size.
So, for instance, you can see that in Fall 2008, more than 50% of our SCUs were delivered in classes of size less than 30, and that in Winter 2017 (2172), about 90% of our SCUs were delivered in classes of size 40 or less.
Here’s the picture for the College as a whole:
You may be wondering why I keep yammering on about SCUs, rather than saying things like “50% of classes…”.
Here’s another interesting picture. (Well, I thought it was interesting, anyway.) It shows the number of WTUs taught by the CS department faculty, from Fall 2008 up through Spring 2016. It includes courses with a bunch of different prefixes: CSC, CPE, HNRS, EE, ENGR, LAES, ME, and DATA.
This graph is broken up by the level of the course. The lowest (white) region shows courses whose names start with “01” (like “0123” and “0101”), the second region shows courses whose names start with “02”, and so forth. The “Sup” region shows the supervisory courses; senior project, master’s thesis, etc.
One note on “adjusted WTUs”: this data is taken from the FAD report, which misclassifies senior projects as lab courses, resulting in some very broken data. I’ve corrected this by re-assigning WTUs according to the CSU’s formulae.
Also, these are all classes taught by faculty associated with the department, so it includes lots of courses taught to nonmajors, as well as some courses taught by department faculty with other prefixes (for instance, a Mechanical Engineering course).
To me, the most interesting thing about this picture is frankly how flat it is. Our enrollments have gone way up, but the number of WTUs we’re teaching is pretty much unchanged.
I think the next picture to draw is how class sizes have changed over the years.
All parsing and rendering done in Racket. Isn’t it time that you learned Racket for yourself? :).
Here’s an SVG showing all of the dependencies associated with CSC courses:
Yes, it’s a little small to read. Click on it to see a bigger version. It’s an SVG, so you can blow it up arbitrarily. (Note: this picture is a lot more readable since Aaron Keen made the eminently sensible suggestion that it be left-to-right rather than bottom-to-top.)
Things to know about this data:
- It’s scraped from the 2015–2017 course catalog in HTML format.
- All cross-listed courses are normalized to their CSC equivalents.
- Arrows are shown to all courses mentioned in the prereqs.
The last of these is significant. If a course has a pre-requisite like “Both CSC 124 and one of MATH 117 or MATH 118”, I just draw arrows to all of them. So don’t assume that the number of outgoing arrows is an indication of the number of courses required to take this course.
- There are lots of courses shown here that haven’t been taught in a long time. CSC 108 jumps out at me, but there are others.
- Some courses have a prerequisite that can be fulfilled by a no-longer-existing course. For instance, CSC 141 changed into 348, but there are still a bunch of courses that list CSC 141. Since 141 is not displayed as a hyperlink in the catalog, we assume that it’s defunct, and we don’t show it.
- No dependencies are shown for non-CSC courses.
All scraping and processing done in Racket, natch. Graph drawn with Dot.
Hello, wild yeast people!
It appears that I have not recently documented how exactly it is that you “capture,” and (more importantly) feed and care for a wild yeast culture.
I should start by saying that most of what I know comes from
- “Cooked,” by Michael Pollan,
- “Tartine Bread,” by Chad Robertson,
- … and certain brief conversations with biologists at Cal Poly
The fundamental assumption behind the effectiveness of what I’m going to tell you comes from the ‘everything is everywhere: but the environment selects’ hypothesis, “promulgated by Dutch microbiologist Martinus Wilhelm Beijerinck early in the twentieth century.”1 The basic idea is that most microorganisms are present in varying densities nearly everywhere on earth, and that it’s possible, by providing the right environment, to breed pretty much any microorganism that you want.
In this case, we want a yeast culture. Some people take great care to preserve a particular culture that has been handed down for generations. My reading suggests that this is … well, kind of pointless. You can create your own culture in a week or two (or maybe less) without much fuss, and without anything but flour and water.
With that said, it’s certainly easier to preserve a culture than it is to create one, so once you’ve got it going well, it makes sense to me to keep it going. Honestly, it’s a lot like a fire was to primitive culture: starting it is a pain, and feeding it isn’t too expensive.
Okay, here’s my “recipe.” I’ve started from scratch on this twice, and I’ll probably do it again.
Hello, my fellow Americans.
How’s it going?
If you’re like me, you’re feeling really, really bad right now. Loss, despair, a pain in your chest. Walking around alone at night, feeling absolutely awful. Really bad. A horror-clown has just been elected president. A man who doesn’t seem to have any plans beyond simple self-aggrandizement, who will say or do anything to inflate his own self-importance.
At the end of the day, you’re probably asking this: What does this mean for America?
Well, I’ll tell you what it means.
It means that we’re not done yet.
This is not yet the America that we want it to be.
It means that we’ve still got a heck of a lot of work to do. No one said this was going to be easy.
So: make a list of priorities. Here’s mine:
- Human Rights
- Climate Change
- Nuclear Proliferation
Then—and this is the hard part—start doing something about them.
I’m going to try. I’ll let you know how it goes.
Hey, today was the day! The day of the annual swim from Long Island to the mouth of Blue Hill Harbor, the current incarnation of the Granite Wo-Mon Challenge.
Before the swim:
Thanks to Chris Guinness for this excellent shot.
After the swim:
Charlotte Clews posted this picture, although she’s in the picture so I suspect she didn’t hit the button. Was this Henry?
Low tide was at 8:47 AM today, so by rights we should have started the swim at about 11:00 AM, but no one wanted to wait quite that long. We gathered at the KYC at 9:00 (or 9:10… or 9:15) and headed out to Long Island. I think we managed to start swimming at about 10:00.
Let me just say: choppy. Not end-of-the-world choppy but still chopping pretty good. Which is to say: bad. I think we all swallowed quite a bit of seawater.
My strava log suggests that the swim took about 1:38, which is quite a bit more than last year.
A special thanks this year to Tricia Sawyer, who pretty much organized the event, and didn’t get to swim. (N.B.: “Organized” = “sent an email to the rest of the slackers”.) It wouldn’t have happened without you!
For those of you completely confused: Granite Wo-Mon Summary Page
So! The swimmers! (In alphabetical order. I love it because Clementses go first. Unless Sara starts swimming.)
- Alice Clements
- John Clements
- Mary Clews
- Amanda Herman
- Charlotte Clews Lawther
Amazing chase boat crew and welcoming committee:
- Sara Ardrey
- Henry Becton
- Jeannie Becton
- Anika Clements
- Kitty Clements
- Tom Clements
- Henry Clews
- Hal Clews
- Chris Guinness
- Sean Guinness
- Stephen Labrum
- Jerome Lawther
- Jenney Wilder
- Eliza Wilmerding
- Renee (last name ?)
Finally, I note with mild dismay the absence of any first-time swimmers. Maybe Lucas will swim next year?
- July 20, 2012 - Aurora, CO – 12 people dead - Smith & Wesson AR–151
- December 14, 2012 - Sandy Hook elementary school, Newtown, CT – 20 people dead - Bushmaster AR–152
- December 2, 2015 - San Bernardino, CA – 14 people dead - Smith & Wesson AR–153
- June 12, 2016 - Orlando, FL – 49 people dead - Sig Sauer AR–154
Earlier this year, I was talking to Kurt Mammen, who’s teaching 357, and he mentioned that he’d surveyed his students to see how much time they were putting into the course. I think that’s an excellent idea, so I did it too.
Specifically, I conducted a quick end-of-course survey in CPE 430, asking students to estimate the number of weekly hours they spent on the class, outside of lab and lecture.
Here are some pictures of the results. For students that specified a range, I simply took the mean of the endpoints of the range as their response.
Density of responses
Then, for those who will complain that a simple histogram is easier to read, a simple histogram of rounded-to-the-nearest-hour responses:
Histogram of responses
Finally, in an attempt to squish the results into something more accurately describable as a parameterizable normal curve, I plotted the density of the natural log of the responses. Here it is:
Density of logs of responses
Sure enough, it looks much more normal, with no fat tail to the right. This may just be data hacking, of course. For what it’s worth, the mean of this curve is 2.13, with a standard deviation of 0.49.
(All graphs generated with Racket.)