Why there is no Hitchhiker’s Guide to Mathematics for Programmers

Math ∩ Programming

For those who aren’t regular readers: as a followup to this post, there are four posts detailing the basic four methods of proof, with intentions to detail some more advanced proof techniques in the future. You can find them on this blog’s primers page.

Do you really want to get better at mathematics?

Remember when you first learned how to program? I do. I spent two years experimenting with Java programs on my own in high school. Those two years collectively contain the worst and most embarrassing code I have ever written. My programs absolutely reeked of programming no-nos. Hundred-line functions and even thousand-line classes, magic numbers, unreachable blocks of code, ridiculous code comments, a complete disregard for sensible object orientation, negligence of nearly all logic, and type-coercion that would make your skin crawl. I committed every naive mistake in the book, and for all my obvious…

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Learning Programming — Finger-Painting and Killing Zombies

Math ∩ Programming

Zmob, my first (and only) original game.

By the end, the breadth and depth of our collective knowledge was far beyond what anyone could expect from any high school course in any subject. 

Education Versus Exploration

I’m a lab TA for an introductory Python programming course this semester, and it’s been…depressing. I remember my early days of programming, when the possibilities seemed endless and adding new features to my programs was exciting and gratifying, and I brimmed with pride at every detail, and I boasted to my friends of the amazing things I did, and I felt powerful. The world was literally at my fingertips. I could give substance to any idea I cared to entertain and any facet of life I wanted to explore. I had developed an insatiable thirst for programming that has lasted to this very day.

My younger self, if programming were more noodley.

The ironic thing is…

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Overview of GE’s Change Acceleration Process (CAP)

Bob Von Der Linn's HPT Blog

In 1989-90, under the direction of Jack Welch, GE launched “Work-Out” – a team based problem-solving and employee empowerment program modeled after the Japanese quality circles model that was in vogue at the time.  Work-Out was a huge success and Welch was frustrated by the rate of adoption through the business.  Welch, the visionary, realized that GE (and everyone else!) was entering an era of constant change, and that those who adapted to change the fasted would be the survivors.  He commissioned a team of consultants (including Steve Kerr, who was to become GE’s first Chief Learning Officer) to scour industry and academia to study the best practices in change management and come back to GE with a tool kit that Welch’s managers could easily implement.  The result was the Change Acceleration Process, commonly referred to within GE simply as “CAP.”[1]

The Change Effectiveness Equation

The team studied hundreds…

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Logarithms Explained, and the Associative Property of Multiplication

The Trickle-Down

So much advanced math relies on a firm grasp of basic Algebra and Algebra II.

Today, lets take a look at logarithms!

So what are logarithms? Well, first let’s look at exponential equations, such as $latex 2^x = y$ where the 2 is a base. We all know that for example, $latex 2^3 = 8$. A general form is $latex b^x = y$ where b is the base. Well, with logarithms, the format is $latex log_b y = x$. So for $latex 2^3 = 8$, we would express that with logarithms as $latex log_2 8=3$. Fun, isn’t it! The logarithm is the number that the base is raised to a power by to equal a given number; in the example above, the base 2 is raised by the power 3 to equal the number 8.

So the tricky part is that you get rules like $latex log_b y + log_b…

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Jeff Bezos on Leading for the Long-Term at Amazon

Wish Bezos did more of these…

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The Fast Fourier Transform

Math ∩ Programming

John Tukey, one of the developers of the Cooley-Tukey FFT algorithm.

It’s often said that the Age of Information began on August 17, 1964 with the publication of Cooley and Tukey’s paper, “An Algorithm for the Machine Calculation of Complex Fourier Series.” They published a landmark algorithm which has since been called the Fast Fourier Transform algorithm, and has spawned countless variations. Specifically, it improved the best known computational bound on the discrete Fourier transform from $latex O(n^2)$ to $latex O(n log n)$, which is the difference between uselessness and panacea.

Indeed, their work was revolutionary because so much of our current daily lives depends on efficient signal processing. Digital audio and video, graphics, mobile phones, radar and sonar, satellite transmissions, weather forecasting, economics and medicine all use the Fast Fourier Transform algorithm in a crucial way. (Not to mention that electronic circuits wouldn’t exist without Fourier analysis in general.)…

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Predictive Analytics and Spurious Correlations


The spurious correlations site has a lot of interesting charts showing various arbitrary combinations of trends that show strong correlations and yet have no rational basis that suggest causation.  Also, the site has a nice feature to explore other correlations by using the hyperlinks on the chart titles to find other trends that correlate with that topic.  Hidden at the bottom of the main page is a link to an entertaining video that nicely discusses how correlations are different from causation.   Some of my discussion concerns the points he makes in the video.   His video expresses an optimism that humans will always be in the loop to insert sanity after just a brief moment of belief that there could be a causal relationship behind such compelling correlations both in graphic form and in statistical values.   The evidence I see is that optimism is misplaced as illustrated by…

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