u/GayTwink-69

One of the most inconsiderate terms I've come across.

You can say that mental health sufferers should seek appropriate healthcare, which I agree with. But sometimes that is not immediately accessible and/or you just need someone to listen to you with empathy.

The term is usually used by selfish and unempathetic people who believe that anything that doesn't affect them is not their problem and they should be shielded from the realities of other people's suffering.

The word "dump" itself is extremely inconsiderate. In most cases, they are not "dumping" for the sake of dumping, they are trying to get out of the emotional pain they are feeling and often times it is a cry for help.

If you frequently use this term, I will assume you are a selfish and possibly narcissistic person and I would make a conscious attempt to not form a meaningful relationship with you. If I ever go through an unfortunate life event (which is kind of inevitable), you would probably break away when I bring it up anyway.

Does going to a top 20 vs top 50 vs top 75 program make a tangible difference to your likelihood of publishing better papers and, ultimately, getting a tenure-track faculty position?

I know in Economics they are super elitist about this kind of thing, but I heard it's less so in more math-y fields.

To be theoretically valid, basically. Cause the size of the test increases with more than 1 hypothesis test if I understood correctly.

They have seemingly disappeared off the face of the earth. All their albums are deleted from spotify and youtube and there is little trace of them left...

Beyond just checking that it's a real citation (AI hallucinations and such), do lecturers actually read every citation you list and see if it actually supports the point(s) it was referencing?

Transformers, NLP, agentic AI, explainable AI, generative AI and LLMs?

I think we all agree that data science is in large part statistics, but what about AI?

Data science gets a lot of shit on this sub, but what about AI?

Is this saturated too? AI is literally the hottest topic right now.

And if it is more difficult does that mean that it is harder to establish an academic career as a theoretician?

Alternatively, as a frequentist, how much should you know about Bayesian methods?

Is there utility in having deep knowledge of both?

Since journals in those categories are a lot less impactful and may even have questionable standards, at what point would publishing in Q3 or Q4 journals actually hurt your career?

Does NK have a better relationship with Russia than China?

Is it true that industry jobs, especially entry-level ones, are full of menial tasks that don't bring you much inherent satisfaction?

Alternatively, academia feels like the place where you are expanding human knowledge with cutting-edge tools, making the world a better place, and educating the next generation. This might be a bit of a romanticized perception I have, though.

Field: statistics and data science

My curious mind wants me to.

Would you guys mind??

Topics that are more on the inference side of things than algorithmic

I've seen Master of AI programs at Go8 universities that accept people with any bachelor's degree, and not even with that high of a WAM (usually around 60%). They will offer an extra semester (sometimes free of cost!) for students without a relevant bachelors to "get them up to speed"

But can you really learn all the fundamentals of a completely new field in 1 measly semester? For AI and data science, especially, you need a strong grasp of mathematics, programming, computer architectures, algorithms, etc. There is no way you can get a firm grasp on all of that in 1 semester.

But I heard it is common in Australian universities to accept someone into a master's program without a relevant bachelors, even in technical fields.

Conversely, in Europe, you usually need to have a bachelors degree in the same field as the masters you're intending to pursue.

And only has training in methods (econometrics, statistics, data science, mathematics) and little formal training in economics