site banner
PaperclipPerfector  1yr ago (text post)   2696 thread views

Small-Scale Question Sunday for October 8, 2023

Do you have a dumb question that you're kind of embarrassed to ask in the main thread? Is there something you're just not sure about?

This is your opportunity to ask questions. No question too simple or too silly.

Culture war topics are accepted, and proposals for a better intro post are appreciated.

2
Top Bottom Old Controversial Comments
Jump in the discussion.

No email address required.

Why are there so many women in data science? I don't mean fake boot camp data scientists who know how to use pandas or matplotlib. I mean women with a math degree then a master's in data science. I won't pretend to be a data science expert by any means, but when I was getting my CS degree, I took a couple data science electives and they were really hard. I'd put them up there with my operating systems (dinosaur book) and computer systems classes in terms of difficulty. There was a ton of calculus and probability on top of coding in there. If you look at low level programming in C or assembly there's almost no women in any of those classes unless its a requirement. It's the same in industry. Yet there are a ton in data science, which I found to be just as difficult to be good at as programming in C.

Is this just some kind of networking effect where women gravitate to a field with other women? This is something that seems super obvious to me and I've never heard anyone give a good explanation.

Data Science is a weird profession. You have people doing everything from adding 2+2 in excel sheets all the way upto creating SOTA models being labelled as data scientists. I'm sure there are other fields too where the variation in the difficulty and objects level of what is done is the same if not more.

As to why there are so many women? I think the reasons are;

  • Not all Data Scientists took the CS (with ML) route. Some just got there because they had quantitative undergraduate degrees or masters and failed upwards into the job because "thet know math". A lot of women would have been stupid to cash in on the trend, especially given most employers don't even know what the fuck they want when they want a data scientist, other than the fact that they want one.
  • The fact you would need to code isn't in your face as much. This is partly true if you find yourself in the "right" kind of data science job. I mean I'll admit, we have good PR.
  • Tends to be more credentialist and academic heavy than traditionally male dominated fields. Most DS openings ask for you have a masters. Base rate of female college grads is higher.

I think a lot of this just boils down to the title being ill defined and the field having really good PR.

I'm a DS for a startup sized company and I have to write a lot of backend/server side code (not Python unfortunately for me). I think a lot of the data science PR leaves out the fact that unless you are in a mega corp, no one is really going to take time out of their day to deploy the models you make. I am the default numbers and SQL guy for the company (the backend engineers only use ORMs and don't feel the need to help the hordes of analysts with their menial tasks).

I studied applied maths in biology/medicine and my year was literally 4 men and about 20 women. I already knew that it was much more gender balanced than pure/theoretic math but was still surprised, so I talked with some of them about why they chose to study this. The answer was fairly uniform: They had always been very good at math, but didn't particularly like it. Some originally wanted to study medicine, but were put off for some reason (and there's more than enough good reasons!). This allowed them to take advantage of something they're good at, while still ultimately working on a topic they like.

I'd wager data science is in a similar boat, albeit to a slightly lesser degree.

I think this is the most likely explanation. The most significant difference between men and women when it comes to careers is preferences, not abilities.

What is this place?

This website is a place for people who want to move past shady thinking and test their ideas in a court of people who don't all share the same biases. Our goal is to optimize for light, not heat; this is a group effort, and all commentators are asked to do their part.

The weekly Culture War threads host the most controversial topics and are the most visible aspect of The Motte. However, many other topics are appropriate here. We encourage people to post anything related to science, politics, or philosophy; if in doubt, post!

Check out The Vault for an archive of old quality posts. You are encouraged to crosspost these elsewhere.


Why are you called The Motte?

A motte is a stone keep on a raised earthwork common in early medieval fortifications. More pertinently, it's an element in a rhetorical move called a "Motte-and-Bailey", originally identified by philosopher Nicholas Shackel. It describes the tendency in discourse for people to move from a controversial but high value claim to a defensible but less exciting one upon any resistance to the former. He likens this to the medieval fortification, where a desirable land (the bailey) is abandoned when in danger for the more easily defended motte. In Shackel's words, "The Motte represents the defensible but undesired propositions to which one retreats when hard pressed."

On The Motte, always attempt to remain inside your defensible territory, even if you are not being pressed.


New post guidelines

If you're posting something that isn't related to the culture war, we encourage you to post a thread for it. A submission statement is highly appreciated, but isn't necessary for text posts or links to largely-text posts such as blogs or news articles; if we're unsure of the value of your post, we might remove it until you add a submission statement. A submission statement is required for non-text sources (videos, podcasts, images).

Culture war posts go in the culture war thread; all links must either include a submission statement or significant commentary. Bare links without those will be removed.

If in doubt, please post it!


Rules


Recommended Posts And Communities

Recommended Realtime Chats