"Programming is hard..."

"... so we'll make it easier specifically to attract more women to it! What a brain wave!"
Yes, we know there aren’t enough women in tech. [Didact: According to what metric, exactly? Other than the author's ridiculous view that men and women are "equal", that is.] Yes, we know we need to change the ratio. 
One college has found the answer. 
With a three-step method, Harvey Mudd College in California quadrupled its female computer science majors. The experiment started in 2006 when Maria Klawe, a computer scientist and mathematician herself, was appointed college president. That year only 10% of Harvey Mudd’s CS majors were women. The department’s professors devised a plan. 
They no longer wanted to weed out the weakest students during the first week of the semester. The new goal was to lure in female students and make sure they actually enjoyed their computer science initiation in the hopes of converting them to majors. This is what they did, in three steps.

1. Semantics count
They renamed the course previously called “Introduction to programming in Java” to “Creative approaches to problem solving in science and engineering using Python.” Using words like “creative” and “problem solving” just sounded more approachable. Plus, as Klawe describes it, the coding language Python is more forgiving and practical. [Didact: It is also significantly less useful than a real high-level programming language like C++ or JAVA.]
As part of this first step, the professors divided the class into groups—Gold for those with no coding experience and Black, for those with some coding experience. [Didact: And no one screamed "RAAAACISSS!!!"? I'm actually surprised.] Then they implemented Operation Eliminate the Macho Effect: guys who showed-off in class were taken aside in class and told, “You’re so passionate about the material and you’re so well prepared. I’d love to continue our conversations but let’s just do it one on one.” 

Literally overnight, Harvey Mudd’s introductory CS course went from being the most despised required course to the absolute favorite, says Klawe. 

But that was just the beginning. 
2. Visualize success
After successfully completing the introductory class, how to ensure female students voluntarily signed up for another CS class? The female professors packed up the students and took them to the annual Grace Hopper Conference, which bills itself as a celebration of women in technology. Klawe says the conference is a place for students to visualize women in technology [Didact: who wants to place odds on the wager that the people doing the actual work are blokes?]; humans who happened to be female who love computers. Not everyone looks like the dudes in the trailer for HBO’s Silicon Valley. 
3. Make it matter

Finally, the college offered a summer of research between freshman and sophomore years so female students could apply their new skills and make something. “We had students working on things like educational games and a version of Dance Dance Revolution for the elderly. They could use computer technology to actually work on something that mattered,” says Klawe. 
The three-step strategy resulted in a domino effect. Female students loved the CS introductory course. They loved going to the conference. So they took “just one more course” and they loved that. 
Before they knew it, women were saying, “‘I could be a computer science major, I guess.’ And so they are!” says Klawe. 
By the time the first four-year experiment was over the college had gone from 10% female computer science majors to 40% female. UC Berkeley, Duke, Northwestern have had some success with similar tactics.
(h/t Vox)

Wow. Just... wow. The FAIL is strong with this one.

Let's start with this conceit that we "need" more women in technology. This is nonsense. What we need is more smart, talented, hard-working people who- and this is key- know what the f*** they're DOING. You don't get to "know what the f*** you're doing" by taking "creative approaches to problem solving". You get there by learning the basics, putting in the graft and the grit required to power through the hard stuff, and learning what it takes to succeed.

And it is a simple fact that most women will not be able to do even those things. That is why most of the really hard courses offered in university- maths, physics, real computer science, chemistry, engineering, that sort of thing- are total sausage fests.

Next we get to this idea of doing "easy" programming languages like Python. Now, I have some programming experience myself. I'm proficient in at least four programming languages- VBA (toy language, very dangerous), MATLAB (very rusty), R (also rusty), and my company's proprietary scripting language. I got there by learning, the hard way, just how difficult real programming is in C++. But I persevered, put in the hard yards, and got to be pretty good at understanding programming structures, control statements, and object-oriented programming. I didn't get there by taking fluff courses and starting with an easy programming language like Python (which, I will readily admit, is actually very useful or things like parsing large text files).

The entire article just demonstrates the sheer stupidity of dumbing down a tough major like Computer Science in order to serve the female imperative. If women can't hack it at the highest levels of difficulty, then tough, it's their problem. And it's not like there are no women who can't program or think analytically- of the top 10 students in my Master's program in Mathematical Finance, three were girls, and they were all extremely sharp. (They were all also extremely Russian/Slavic or Asian, which goes a long way to explaining their smarts.)

Ultimately, the women who fall for this, and the colleges who create this absurd situation, are the ones who will suffer. Women who go through these fluff courses will emerge into the real world unable to perform the kinds of tasks that male engineers and computer programmers get paid to do, and will rapidly discover that their "educations" were in fact very expensive failed journeys in self-affirmation. Companies that hire these women will discover, at great expense, that they don't know what they're doing.

And universities that dumb down their curricula will be responsible for all of it. They will be held accountable for it, eventually. Until then, though, they are doing tremendous damage, both potential and real, to students and to employers. And all in the name of "equality".

No deep message here, it's just a good song sung
by a chick who knows what she's doing


  1. I agree with much of your posting. I think weeding out weak students with tough introductory courses actually does them a favor. They don't waste 2-3 years of their life (and expensive tuition payments) to pursue a degree then fail in the end.

    I beg to differ about Python. My understand is that Python, along with Matlab, are the most common languages used to implement the "deep learning" algorithms (really nothing more than multi-layered neuro-nets) that are purported to be the next advance in robotics and machine vision. Nevertheless, you are correct that one should learn structured programming to begin with (C/C++) in order to get the basic concepts down. Matlab is a good language to use as it is the best for scientific programming. The google search engine is implemented in Python.

    I agree that replacing "introduction to programming in" with "creating approaches to problem-solving" is just fluff. We have far too much fluff in this country.

    1. I beg to differ about Python. My understand is that Python, along with Matlab, are the most common languages used to implement the "deep learning" algorithms (really nothing more than multi-layered neuro-nets) that are purported to be the next advance in robotics and machine vision.

      It depends on the industry you work in, to be honest. I work in banking, so my experiences colour my judgement on which languages are most important. In order to become a quant or developer at most banks, you need to be proficient in AT LEAST one of: C/C++, C#, JAVA, and possibly Ocaml. (If you know C/C++, the next two are pretty easy to pick up.) If however you're working in biomedical/pharmaceutical research, or robotics, or machine-learning, then the unforgiving nature of C++ combined with its need to compile first means that you'd be better off with something like MATLAB, R, Python, or SAS (for really big datasets).

      As you say, the greatest benefit of learning a really hard language like C++ (or APL) is that, once you know how to program using those beasts, most other programming languages become a doddle to use. My experience has been primarily in banking and consulting, and that's where skill with C++ is a huge benefit. Female programmers who don't know C++, or who study a dumbed-down version of a CS major, simply won't hack it in the industry.

      Matlab is a good language to use as it is the best for scientific programming.

      I like it a lot, especially the later versions which are fully object-oriented. Personally I prefer using R for most applications, even financial ones, largely because I really like its syntax and its flexibility. But then, I'm a FLOSS nerd; MATLAB has better help files and a much slicker interface. If it were up to me I'd use R for everything- some commodity hedge funds actually do.


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