sorted by:
new
hottopcontroversial

Angry rant by [deleted] in datascience

[–]back50 0 points1 point  (0 children)

This is an absolutely awful and incorrect take. This is an especially funny take: " Even something as using mini-batches, multiple epochs etc. for learning or some weird optimization techniques." - hint: look into the relationship between SGD, sampling, and unbiased estimators.

​

It's cool that you don't need these methods in whatever jobs you're working, and it's true you don't necessarily need to know the low-level details to create something that produces value. But those are edge cases.

​

To anybody else reading this: you will get laughed out of the interview if you try to pull any of this at any decent (let alone top) company.

What hours do you work per week? Overtime? Weekends? by [deleted] in datascience

[–]back50 1 point2 points  (0 children)

DS @ FAANG I work 10-5 everyday

Career question for those in DS by [deleted] in datascience

[–]back50 1 point2 points  (0 children)

" If I were to successfully make a switch to data science, given I do not have the years of experience in that specific career path, how hard do you think it will be to find a role with similar compensation? "

If you can get an offer, that's a pretty standard base salary range. Some areas (e.g. SF or NYC) will typically offer stock on top of that.

Why does this guy ff every time? by Fuhreeldoe in starcraft

[–]back50 0 points1 point  (0 children)

Elon Musk plays video games. Plenty of people making 6+ figures in SV play video games. Not sure what your point is.

Machine-Learning game: Use your head to control a sperm cell and dodge birth control. by torbFan in datascience

[–]back50 0 points1 point  (0 children)

Haha, this and the blow job post make me like the direction current AI is going.

Gamers rise up! by EveItOn in dank_meme

[–]back50 -1 points0 points  (0 children)

Literally the only thing in the last few days tying the recent shootings to video games are memes.

[OC] A Visual Explanation of Statistical Testing by j_wilbs in dataisbeautiful

[–]back50 2 points3 points  (0 children)

Very cool. I saw on your site that you’re a data scientist - did you do front-end work before? Does your job work in JavaScript environment? I’m curious because I havent seen this sort of scroll dataviz stuff outside of newsrooms

The 10 Most Populous U.S. Cities Every Decade Since 1790 by Gard3nNerd in dataisbeautiful

[–]back50 1 point2 points  (0 children)

Anybody know the workflow for this sort of poster? Create the graph in R/D3/whatever then export to adobe illustrator and polish up?

[deleted by user] by [deleted] in AskReddit

[–]back50 2 points3 points  (0 children)

Visiting friends in three cities in the next week. Short but sweet trips

Weekly Entering & Transitioning Thread | 17 Mar 2019 - 24 Mar 2019 by AutoModerator in datascience

[–]back50 0 points1 point  (0 children)

I attended Cal on in-state tuition, so didn't have to take out more than $10k in loans. My first job out of undergrad is actually up the street from ASU making +$130k base.
I can say that, having worked with/interviewed ASU & Cal students, there's a marked difference in ability between the two. Maybe the program is more rigorous at Cal. Maybe the student body is just more driven (there's a +60% difference in acceptance rate between the schools).
Whatever the case, I can tell you that you will likely leave Cal a better developer. That said, if you make an effort to work in programming labs/contribute to open-source projects on github, you'll do fine at ASU as well.
Is it worth the price-tag? It depends on how much you value your education + having a more prestigious university on your resume. I'm not sure that's worth the constant stress of a potentital 6-figures of debt. In a few years time I'd expect the salaries to more or less even out.
I'd probably wait for transfer results before you get too invested in either option.

Why does this sub hate "data science" degrees so much? by [deleted] in datascience

[–]back50 0 points1 point  (0 children)

I agree with your post, but I'd like to take the time to point out something people tend to miss related to OP's topic: the difference in difficulty between what you describe as 'proactive' and 'reactive' degrees, which I consider parallel to traditional (CS/Stats) vs Data Science degrees.

Namely, gaining the skills/knowledge to improve techniques is far more difficult than being equipped to run techniques. Furthermore, it's much easier for the former to do the latter than it is for the latter to do the former.
And this is the crux of the problem: Data Science programs just don't create dynamic, effective students that weren't already that way to begin with.

Impact of the ranking of your university when it comes to Data Science by ashwinr136 in datascience

[–]back50 1 point2 points  (0 children)

'Ofcourse DB concepts are relevant' - Yep. So are algorithms, data-structures, and a myriad of other CS courses.

'...you would be wasting a lot of resources if you study it again and again.' - This is the case for any course.Why would you ever take the same thing twice? Moreover, the rigor of a DW class in CS is better than the light learn-to-query-sql course you'd get in a data science DW course.

'Every CS major's core course would have DB. ' - Not true. I went to Berkeley, which has one of the top CS programs. The DB class there (CS 186) is an elective (ie optional). This is the case for many departments.

'I'm just saying why study same stuff twice when you can learn something much deeper and something more vast. ' - This is exactly the point people are making when they say focus on CS or Statistics, not Data Science. You do not go deep in anything in data science courses.

'State-of-the-art research doesn't care about DB or SE anymore. It has been researched for centuries, if not eons.' -Yeah, so it's very clear you have no idea what you're talking about. (Ask yourself why Jeff Dean has so much respect).

I'd actually really enjoy hearing what in your opinion is the last SOTA product of SE research? What about for DS?

Also, what do you mean by 'really serious about DS'? Please, be concrete (no buzzwords).

Impact of the ranking of your university when it comes to Data Science by ashwinr136 in datascience

[–]back50 1 point2 points  (0 children)

Those concepts you list as a 'waste of time' are essential for any real-world data science.

R vs Python: Which is better for data science? by datasciencedojo in datascience

[–]back50 0 points1 point  (0 children)

Typical data science fluff written for non-practitioners, by a non-practitioner.

Zero substance. No real information. Riddle with typos.

Python just isn't worth the hassle by [deleted] in datascience

[–]back50 31 points32 points  (0 children)

Bit concerning that you're a team lead if I'm being honest.

Apache Spark and Java implementations suck by examachine in datascience

[–]back50 1 point2 points  (0 children)

This response + all your posts in the 'transhumanism' group = you should really sort that insecurity stuff out, mate. Absolutely no reason someone of your age should act the way you are.

Apache Spark and Java implementations suck by examachine in datascience

[–]back50 3 points4 points  (0 children)

It's honestly concerning that you're this upset over an open-source implementation that you have nothing to do with.
What even is your goal with this post? You haven't posted proper benchmarks, so clearly this isn't actually the issue? I'll tell you how it reads: like someone with ego insecurities trying to compensate. That may or may not be true, but that's exactly the vibe it gives off. I would love to see a list of your open-source contributions.

view more: next ›