This is my fear of working remote. Seems to come up relatively often in threads that discuss remote work -- needing to force oneself to leave the house (which is also the workplace)
Here's the way I look at it: learning loss is almost inevitable, but I think it's less of a problem than it appears.
What I think we should instead pay attention to is the rate of re-learning. Something may take significant time and effort to learn the first time, and after say a year or two, one may forget it. But when re-introduced to the concept (when you may actually need it), I think one can re-learn it quite rapidly.
Information loss is inevitable when that information isn't used enough to become inately retained. When we focus on a particular area of study, what we learn is self-reinforcing. If I study calculus and discrete math, my understanding and retention increases in each subject, because the overlap between them connects mental pathways. If I study calculus and eastern Asian religions, retention suffers because my brain cannot make meaningful connections between the two. This lack of coherence in what I'm learning means that I'm wasting my time with one of those subjects ;)
Politely disagree. I'm going to take engineering math to mean ~4 semesters of calculus (differentiation, integration, multivariable, and differential equations) as well as maybe a Linear Algebra class and a discrete mathematics class (probability, set theory, combinatorics). I would argue only the discrete mathematics course is practically useful
Anecdotally, as an engineer in industry, I use very little of the engineering math. Number sense and sort of general quantitative reasoning are used.
Let's suppose for sake of argument that I actually used engineering math though. Analytic solutions to derivatives and integrals (2 semesters of calculus) are largely useless because of applications like Wolfram Alpha that will solve these problems for you. The small class of ODE/PDE problems that are handled by analytic undergrad math classes will most often be solved using numerical methods such as Runge Kutta. Linear algebra is actually super useful in practice because you can use matrix solvers to solve systems of linear equations -- but a first course in undergraduate linear algebra is often just basic by-hand computations on matrix systems, and don't discuss higher order concepts at more than a shallow level (spans, invertibility, spectral decomposition, canonical forms, etc.)
I'm old. There were no PCs then, so the analytic stuff was useful. What I remember most about the pure math classes were proofs about whether or not something was solvable. But never anything about how one might actually solve anything. And far too much number theory. All too abstract for me.
Thanks for taking the time to read my post. :) Like I mentioned, this article only lays the groundwork. The next article I intend to write on is DP+Strings (for example, finding the longest substring of a string which is a subsequence of another, etc)
I'm starting with classical problems and I'll soon diverge into non-classical problems, as I've mentioned in my article too.
In any case, hope my other articles on my blog added more value to you in comparison!
Anyone have advice for junior SWEs? I will have a year of co-op experience by the end of 2019, and was thinking of applying from the US to work in Germany or the Netherlands for full-time junior positions.
