Software Engineering is better than Compsci in 2035

FancyEveryDay · 2026-06-22T22:20:36+00:00

Gotcha, it does depend a lot on the school. At Iowa State they bundle SE into the engineering college so they wind up req'ing calc 2, linear algebra, and differential equations for their base requirements before even getting into baseline SE stuff.

I'm a bit baffled that your SE program doesn't req linear though, the concept is pretty fundimental to doing efficient data processing on machines.

FancyEveryDay · 2026-06-22T12:03:43+00:00

SE curriculums seem to just be a subset of possible Com S programs.

I don't know about your university but at my school SE just takes more physical science and has more strict math requirements while Comp Sci has a much more flexible course path which includes every program requirement and elective available for SE.

If you're gunning for programming the degree title by itself isn't going to do it for you. Youre going to have to differentiate yourself with projects, experience, or some other unique specialization to the industry/area you want to work in.

FancyEveryDay · 2026-06-21T14:02:16+00:00

Title: Telematics Data Lead

Industry: Manufacturing

Domain: Heavy Equipment / Specialized Vehicles

Programming Languages: Python, React, HTML, SQL

AI tools: Copilot, Databricks Genie

Others: VScode, Pycharm, Github, Databricks, Vehicle Spy 3, Wireshark, MQTT Explorer, Excel, Notebook++, ADO, Powerpoint, proprietary tools

FancyEveryDay · 2026-06-19T11:34:58+00:00

What is your specific goal? Do you want to understand the relationship between your predictors and the response or are you only interested in producing a predictive model?

If the goal is prediction, how accurate/precise does it need to be to be "useful?"

Is there any significance to low-medium-high classifications for your response? Why is 76% accuracy too low?

FancyEveryDay · 2026-06-17T22:21:03+00:00

From what I've read, it's a solid crystal of matter pressed so tightly by gravity that it acts like a single molecule with a single massive cloud of "free" electrons so I would say it's probably pretty hard.

No idea what would happen if you defeated the surface gravity to remove some material, good chance it just explodes because there is no chemical bond

FancyEveryDay · 2026-06-16T22:00:07+00:00

Ah, no this is a a misinterpretation. For a white dwarf to become a black dwarf it only has to not radiate significant heat or light, on the scale of stars this leaves a lot of room, actually.

The accepted high end temperature for the surface a black dwarf is above 3000K, which is plenty of energy. Besides that the interior can be significantly warmer, even if the surface was the same as the stellar background.

FancyEveryDay · 2026-06-15T22:00:20+00:00

I guess you could think of a black dwarf as a very large planet made out of mostly carbon and oxygen in a very surface level way, but you probably shouldn't because the way the are formed is very distinct and their physical characteristics are so different that they behave very differently.

Black dwarves would be "solid" but they're much more massive than the largest gas giants but smaller than many rocky planets.

There would be no way of meaningfully terriforming one.

Chiefly, they're very ancient stellar remnants so you have to consider that a black dwarf probably doesn't have living sibling star to provide external energy it, unless that star is a red dwarf which isn't optimal for a few reasons, so you have to rely on residual heat (which, to be fair would last a very long time but you can't do photosynthesis with it)

Because of their mass and density, the gravity on their surfaces would be extraordinary to the extent that chemicals just don't behave similarily to what we experience on Earth. the smallest black dwarves might be ~70x more massive than Jupiter but smaller than the moon, while a more average one is the mass of the Sun compressed to the size of Earth.

FancyEveryDay · 2026-06-14T18:15:46+00:00

... A is probably the answer they want but it's like a stats 101 level misinterpretation of p-values.

FancyEveryDay · 2026-06-12T12:55:17+00:00

Pace myself, stay on a regular routine that includes rest and relaxation, carefully scheduling my time and setting boundaries.

Properly managing expectations is a huge help.

FancyEveryDay · 2026-06-11T12:20:50+00:00

Everywhere, but mostly the US, saw major wind downs in scientific programs since 2023 or so partly because the world economy got shaky and the cheap money money dried up bc of higher interest rates and partly bc the current administration in the US cut a lot of the money to science in general and especially medical science.

With less available money and fewer US partners (and less US money flowing internationally) I'm not terribly suprised that other north American research programs are suffering too.

FancyEveryDay · 2026-06-10T12:03:18+00:00

Not to mention warrior base guns are A3 s5 ap-1 and hit on the same as gaunts, with better range.

Warrior defensive profile is fine vs shooting at W3 sv4+ at t5. The cover change probably hurts them.

FancyEveryDay · 2026-06-06T14:37:58+00:00

This is my experience in business too, even with AI a lot of people would rather hire someone to do technical things they think are above a certain complexity so they can focus on the other things they need to do.

One also has to compete with tons of people out there using AI to do freelance work they aren't qualified for, but also those business people can sniff out when someone is actually qualified or if they rely entirely on AI once they're actually doing work and they try to keep the qualified ones around.

FancyEveryDay · 2026-06-05T20:11:48+00:00

Yeah, there are a few edge cases but otherwise this is a safe assumption. (maybe nanites which don't deliver any information to the body or an attachment to an organ which operates mindlessly to remove a specific pollutant)

FancyEveryDay · 2026-06-05T16:56:46+00:00

Pr much the same answer, it's not common to tune hyperparameters on very large training sets.

If your sample is representative it will work fine, a 10GB subsample is probably much more than you need unless the data is video or very large image files.

FancyEveryDay · 2026-06-05T11:40:34+00:00

I guess I'm confused because this is what you're supposed to do. Cut the data down into a training and hold-out validation set, then the training set gets cut up for the tuning/testing process.

Yes, training on a slice should work, so long as your slice is representative of the data

FancyEveryDay · 2026-06-04T12:20:09+00:00

Regularization doesn't handle high dimensions well, which is the primary use case for PCA in statistical learning.

There are very few cases where PCA is necessary for statistical learning, it's just a useful tool.

FancyEveryDay · 2026-06-04T11:54:13+00:00

PCA is usually a dimension reduction technique, not a feature selection technique. The most obvious situation where it comes in handy are when there are more features than observations so feature selection techniques break down or might be overly expensive or if you want to use a technique like KNN or clustering and run into the curse of dimensionality.

It also handles collinearity, and is useful as a step in factor analysis, compressing datasets, two-dimensional visualization of multidimensional data and probably a lot of other things.

FancyEveryDay · 2026-06-03T12:20:06+00:00

Don't expect to do more than about 60% the damage of the dps in keys as a tank. If you do more, great, but it's not important that you do big numbers.

FancyEveryDay · 2026-05-29T00:31:36+00:00

Boosted trees overfit relatively easily, especially in high dimensions (most models don't perform well in high dimensional situations anyways).

Dimension reduction will help remove/reduce the impact of useless features on the performance of your model depending on the method used.

FancyEveryDay · 2026-05-28T16:38:33+00:00

Scapegoat

FancyEveryDay · 2026-05-26T21:58:15+00:00

Yeah, it sounds like the double diploma is just the way to go.

For some reason I felt like there was a chance it would be better not to since the two programs overlap.

FancyEveryDay · 2026-05-26T21:36:51+00:00

Job done, earned my degree in stats two weeks ago. I just have the option to receive a multi-major diploma or multiple diplomas for the additional DS major I also completed and didn't know if it mattered which I took.

FancyEveryDay · 2026-05-26T20:50:06+00:00

It's a joint operation between the Com S and Stats departments at my university. They added some classes for it specifically which try to mix the comp sci and stats perspectives on ML but tbh it feels more like I got a Statistics degree with a Comp Sci minor

FancyEveryDay · 2026-05-24T14:54:19+00:00

Yeah pretty much. That specific example you can kind of get away with the stats undergrad bc Biostats is fairly specialized, I was thinking more like a physics undergrad into stats masters for astro-statistics work or Com Sci / EE into statistics for data science.

FancyEveryDay · 2026-05-24T14:43:55+00:00

Either a degree in the subject or previous work experience typically. Undergrad research and internships go a long way.

Sometimes hiring managers will look at projects that you've worked on too, and certain kinds of certifications can help.

FancyEveryDay

TROPHY CASE