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What every programmer should know about Mathematics
Many programmers ask – “Does computer programming have any practical use of math that we learned as students?”
Computer science derives from "compute" and we cannot compute without numbers and mathematics. We, as programmers must realize this.
Programmers wear different hats. Besides writing programs, they have to troubleshoot problems or compare runtime complexities of algorithms that they design. They have to make estimates of error rates or make projections. Sometime data collection before making an informed decision is crucial. The author has also worn these hats at multiple times and draws from his own experiences of using math to make more informed decisions over the years.
Borrowing simple math concepts from trigonometry, calculus, limits, curve fitting, probability theory, linear algebra, distributions, etc. this paper highlights how they can be used in almost every aspect that a programmer has to take into account depending upon how experienced they are. A successful programmer always relies on back-of-the-envelope calculations and the paper highlights them too.
In some of the cases, data points had to be fabricated because author also lost track of some of them over the years. But that would not hamper our learning as we go through the paper.
We will not go deep into explaining each Math concept. Readers are also informed that his paper is not about machine learning. As mentioned this paper is about Math that we learned as students and its application to programming, estimating, designing and troubleshooting.
We will size a thread pool, use Fourier transform to help in troubleshooting tricky issue, try to assess chances of our request landing on some faulty node behind a load balancer, calculate chances of two requests landing exactly at the same instance, try to optimize our memory utilization, understand rate change in message arrival, understand how our different queues are loaded after state transitions from one to another, etc....
So, let us get read to wear a programmer hat that we always wear with an added feather of mathematical concepts!
Happy learning!
Computer science derives from "compute" and we cannot compute without numbers and mathematics. We, as programmers must realize this.
Programmers wear different hats. Besides writing programs, they have to troubleshoot problems or compare runtime complexities of algorithms that they design. They have to make estimates of error rates or make projections. Sometime data collection before making an informed decision is crucial. The author has also worn these hats at multiple times and draws from his own experiences of using math to make more informed decisions over the years.
Borrowing simple math concepts from trigonometry, calculus, limits, curve fitting, probability theory, linear algebra, distributions, etc. this paper highlights how they can be used in almost every aspect that a programmer has to take into account depending upon how experienced they are. A successful programmer always relies on back-of-the-envelope calculations and the paper highlights them too.
In some of the cases, data points had to be fabricated because author also lost track of some of them over the years. But that would not hamper our learning as we go through the paper.
We will not go deep into explaining each Math concept. Readers are also informed that his paper is not about machine learning. As mentioned this paper is about Math that we learned as students and its application to programming, estimating, designing and troubleshooting.
We will size a thread pool, use Fourier transform to help in troubleshooting tricky issue, try to assess chances of our request landing on some faulty node behind a load balancer, calculate chances of two requests landing exactly at the same instance, try to optimize our memory utilization, understand rate change in message arrival, understand how our different queues are loaded after state transitions from one to another, etc....
So, let us get read to wear a programmer hat that we always wear with an added feather of mathematical concepts!
Happy learning!
63 pages, Kindle Edition
Published May 13, 2016
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