A programming language is a tool for handling design complexity. That’s what all of computer science is, really — languages, libraries, type systems, garbage collectors, everything you learn about programming. They’re ways to build more and more complex designs without losing your grip.

The way you manage complexity is to be able to ignore it. A good programming tool lets you forget about some part of the problem, so that you can focus on some other part. And it ensures that when you return to the parts you forgot, you haven’t accidentally broken them.

–Andrew Potkin

Years ago, When I was taking to programmers about what college I wanted to attend, I had in interesting conversation about how Computer Science education is an utter failure at preparing students for real-world programming. Outside of Software Development, no technical field accepts (sometimes prefers) candidates with “N years of experience” in place of a degree. I’m not sure I know why CS education fails so badly and universally. But my current best guess is that it’s because school never exposes you to enough complexity. Projects have to end in a semester. You never have to deal with a multimillion-line program, written by hundreds of co-workers, dozens of which you need to collaborate with, at unexpected times, for surprising reasons.

…I’d rather have the text of Clay’s speech than the video. For things that matter, written words are unambiguously better than speech. To start with, anything that matters isn’t just written, it’s usually rewritten repeatedly (and more important, condensed). Plus, it has hyperlinks. Plus, it’s smaller and cheaper to ship around. Plus, it’s searchable. Plus, it works on more devices. (I acknowledge that only the first of these is fundamental; but that alone would be enough).

–Tim Bray

Videos, speech, etc. will always carry more emotional content. But for consuming ideas, text offers the highest bandwidth and most precision. Unfortunately, writing well takes time, and can hinder conversation.

Conceptually, I believe illustrative pictures and infographics are valid elements of modern text, like links, or typography.

For many people the first word that comes to mind when they think about statistical charts is “lie.” No doubt some graphics do distort the underlying data, making it hard for the viewer to learn the truth. But data graphics are no different from words in this regard, for any means of communication can be used to deceive. There is no reason to believe that graphics are especially vulnerable to exploitation by liars; in fact, most of us have pretty good graphical lie detectors that help us see right through frauds.

Much of twentieth-century thinking about statistical graphics has been preoccupied with the question of how some amateurish chart might fool a naive viewer. Other important issues, such as the use of graphics for serious data analysis, were largely ignored. At the core of the preoccupation with deceptive graphics was the assumption that data graphics were mainly devices for showing the obvious to the ignorant. It is hard to imagine any doctrine more likely to stifle intellectual progress in a field…

–Edward Tufte, The Visual Display of Quantitative Information, page 53.

You will be lied to more often, and more subtly, with words than with figures. But unlike empty words, the data behind a chart is verifiable, and can be objectively redrawn. As I see it, quantitative analysis is our best chance to reason truthfully and without ego. Sometimes infographics are a better tool than words, especially for summarizing large datasets objectively. If anything, we should be scared when there aren’t graphs and statistics.

I gripe about Excel a lot, as we’re more or less forced to use it for data analysis in the intro labs (students who have taken the intro engineering course supposedly are taught how to work with Excel, and it’s kind of difficult to buy a computer without it these days, so it eliminates the “I couldn’t do anything with the data” excuse for not doing lab reports). This is a constant source of irritation, as the default settings are carefully chosen so as to make it difficult for students to do a good job of data presentation.

…

Now, you might be saying “Well, of course Excel isn’t appropriate for scientific data analysis. It’s not really for scientists, though.” Which is true, but here’s the thing: the things I’ve complained about here aren’t good for anything. The color schemes and axis settings lead to illegible plots no matter what sort of data you’re working with. And I’m completely at a loss as to the purpose of the “Line” plot, or making it difficult to find uncertainties in fitted quantitites.

–Professor Chad Orzel, Why Does Excel Suck So Much?

There’s no question in my mind that a lot of serious analysis is done in(spite) Excel. I’ve worked with some very smart programmers, with PhDs in experimental science, who have “numerics” in their job description, and used Excel to make quick graphs.

The best solution I can recommend is reading The Visual Display of Quantitative Information. It’s probably the best guide to honestly presenting data graphically.

Unfortunately I don’t have a good recommendation for a better software program. The excellent redrawings at the chartjunk blog were done in Adobe Illustrator (more info in this comment). But Illustrator costs $599, and is a complex drawing program. Honestly the sticker price, and ease of use, have kept me from trying it.

What do you use to draw graphs?

The New Turing Omnibus: Sixty-Six Excursions in Computer Science

The book covers some dense territory, but is still fairly accessible. When my mother asked me, “how can a computer make a random number, if it only does what it’s told?” I pointed her to the chapter 8, “RANDOM NUMBERS: The Chaitin-Kolmogoroff Theory” (page 49). The math was a bit over her head, but she could still read the chapter, and it answered her question. I recommend it to The New Turing Omnibus, without reservation, to anyone who’s considering Computer Science.

What are your favorite introductory Computer Science books?

Patrick Thomson suggests Godel, Escher, Bach: An Eternal Golden Braid. It’s an excellent and fun introduction to the essential theory behind computer science.

Here is an excellent overview of the current state of the P=NP question.

Science is not a monument of received Truth but something that people do to look for truth.

That endeavor, which has transformed the world in the last few centuries, does indeed teach values. Those values, among others, are honesty, doubt, respect for evidence, openness, accountability and tolerance and indeed hunger for opposing points of view. These are the unabashedly pragmatic working principles that guide the buzzing, testing, poking, probing, argumentative, gossiping, gadgety, joking, dreaming and tendentious cloud of activity — the writer and biologist Lewis Thomas once likened it to an anthill — that is slowly and thoroughly penetrating every nook and cranny of the world.

…It is no coincidence that these are the same qualities that make for democracy and that they arose as a collective behavior about the same time that parliamentary democracies were appearing. If there is anything democracy requires and thrives on, it is the willingness to embrace debate and respect one another and the freedom to shun received wisdom. Science and democracy have always been twins.

–Dennis Overbye

Learning happens when attention is focused. …
If you don’t have a good theory of learning, then you can still get it to happen by helping the person focus. One of the ways you can help a person focus is by removing interference.

–Alan Kay, Doing With Images Makes Symbols.

She’s never lived in a world that wasn’t densely populated with Macintoshes. …She literally learned to use it by sitting on her mother’s lap while her mother was working. So for this child, the Macintosh is not a piece of technology, but simply more material in the environment to manipulate.

Another interesting point to note is that even though she can’t read, she’s able to recognize standard textual menu items.

And in fact we discovered that she was about 70% literate — that about 70% of the generic window commands that are found in any Macintosh application, she’s able to use. Not just in a visual program like Mac Paint.

One message Alan Kay really drove home is not just that that computers can be simple enough for children, but that designing for children can lead to better designs for adults. But remember that that just because a child can use a computer does not make it a silver bullet for education.

After the experience I’ve had with working with both children and adults with computers (and at least dabbling in the areas of learning and education), I think that one of the best ways of thinking of a computer is very similar to thinking of what a piano means when teaching music.

The piano can amplify musical impulse. We can only sing with one voice. If we want to play a four-part fugue, we have to use something mechanical, like a piano to do it. And it can be done very beautifully.

But for most people the piano has been the biggest thing that turns millions of people away from music for the rest of their lives. And I think the best way to sum it up is just to say that all musicians know that the music is not inside the piano…

So, in any situation where education and learning is involved, you first have to develop a curriculum based on ideas, not on media. Media can be an amplifier of those ideas, but you have to have the ideas first.

And I think the reason computers have failed is that almost everybody, no matter which way they have tried to use computers, have wanted the computer to to be some sort of magic ointment over the suppurating wound of bad concepts. … But first you have to have the ideas.

This was exactly my experience as a student. I am dysgraphic — I have trouble writing legibly by hand, and spelling. So I took a laptop to all my classes, from 8th grade (1997) through college. The laptop solved a particular problem for me. But outside of that, it did not enhance my education; in some cases it got in the way. (One professor found students using laptops the most during class did 11% worse on tests compared to the rest of the class). If I wasn’t dysgraphic, I would have been better-off with a Moleskine.