How does sci feel about statistics?

Job Titles Related to this Major
Some fields require a graduate degree.

Actuary
Algorithm Designer
Computer Scientist
Consultant
Data Miner
Database Administrator
Economist
Financial Analyst or Advisor
Insurance Underwriter
Inventory Analyst
Market Researcher
Mathematician
Research Analyst
Social Scientist
Statistical Engineer
Statistician
Survey Researcher
Teacher
Skills You Acquire With this Major
Strong quantitative reasoning abilities
Strong problem-solving skills
Analytical and logical reasoning skills
Statistical analysis abilities
Independent, self-motivated work ethic
Informational management

is being an actuary some kind of a religion? the 'field of actuarial work' has an impressive website.

beanactuary.org/what/

sounds cool though, i'm willing to manage risk.

im an actuary ama

No, that's why science funding exists. Science exists to explain how the world works.

do you enjoy what you do? whats your day like? i'm just starting out at a community college, what would be a conservative path for becoming an actuary, starting out fresh such as i am? what misc advice would be worth hearing?

3 years too late. Job market for statistics is oversaturated.

As someone studying to become a computer scienctist, I find statistics incredibly important, and I think this should be the case in every science.

If you don't love it, that's fine. That means you shouldn't major in it. But you should take at least a class or two. It's good stuff, that will help you understand scientific literature better and help you understand when people are lying to you with statistics.

You will undoubtedly have to use statistics in your career, so you should understand the fundamentals behind the tools you're using. Additionally, you'll probably learn some sick graphs to use to make rad posters.

I enjoy what I do because I like math and it pays well. I also got into (lucked into maybe) a more interesting role than some actuaries are in (pensions, ew)

I spend 80% of my day in excel/Access/SQL, the rest talking to management or clients

Get good at excel. Pass your exams. Actually study for the exams... some people go into them without actually preparing thinking their university courses are enough, this is not true. The questions aren't necessarily hard if you're good at math but you need a lot of exposure to the types of questions you'll be seeing.

Get a good GPA. They actually do care when hiring actuaries. Mine was ~3.7 and I didn't find it too hard to get a job but it definitely is easier if you're a 4.0.

Oh and do internships if you can. Super valuable.

You're retarded and needlessly elitist. Just because someone posts their hopes and dreams doesn't make them an autist.

His post wasn't even cringey jesus

yeah this actuary website is telling me 300 hours of studying to pass the exams. must be some interesting stuff if it's that complex.

thanks for the advice. i'll look further into actuary stuff. until then i suppose just pursue a bachelors or masters in statistics/ data science or informatics or whatever its called.

it's like there's a subculture of actuaries. what the hell? there are videos of hysterical people talking about actuary stuff and videos of 'top ten most legendary actuaries' and stuff. this is just blowing my mind.

>the specifics of what a statistician can do.
Take data of different types, and be able to classify it, predict outcomes, or tell us the extent of the data's usefulness in understanding a given phenonmenon.

Yeah so actuaries are 90% awkward as fuck so like any good nerds we have our own subculture and memes and shit. Especially because we all have to suffer through the same exams, it's a comradery thing I guess.

It's exasperated by the fact that when you try to relate to people who are not actuaries you get told shit like "i hated math in high school i would kill myself if I did what you do" or "insurance is such a scam". Of course if you try to defend your profession their eyes glaze over because it's too technical and people are stuck in their opinions.

This is quite outdated advice, imo. Excel and SQL are extremely limited, and you're better off learning to use 'data science' tools. Also in my experience, GPA is not especially any more important than with any other discipline.

youtube.com/watch?v=L1WOFxIjfUQ

i'm pumping myself the fuck up to figure out how to calculate the cost of life insurance for given populations considering their mortality rates right now

that's kinda what i was thinking, i already have basic knowledge of C++ and C, it wouldn't be too much to learn to set up with R and python and use neural nets to do fun stuff and crunch numbers. or something to that effect. i want a math heavy degree, and i want to do data wizardry shit with the math, so it just seemed like statistics + data science would be a good combination to get an interesting job.

For most actuarial roles I respectfully disagree. I've probably met ~ 150 actuaries and i'd say maybe a dozen of them know or use anything more intensive on a daily basis.

I've personally used R and Python a few times at work but honestly when you need to present things to non actuaries they don't give a flying fuck about your portfolios kurtosis or skewness or blah blah blah

If you're doing something on the PC side related to, say, hurricanes, wildfires and such I will definitely agree with you there that more rigorous statistics is needed.

As a caveat to what I've said if you're willing to put the time into learning more advanced statistical modelling then definitely do it - if you can combine that with being at least minimally socialable you'll be an all star actuary.

Statistics provides a half-semester's worth of coursework for a math major. It's not really a whole degree though.

Ah yeah, good point. If you're working within a business environment, most people are only interested in pie charts, bar graphs and summary statistics.
Even businesses looking to hire 'data scientists'.
Most businesses seem to want data analysts...

im not good at parties but when i go to interact with people i become personable and friendly with them very quickly, i have a knack for guiding and teaching people.

maybe that's my destiny, data-wizard actuary.

i can make some FIRE interactive graphs and displays and models and deeply responsive charts n visualizations n stuff. i'll have to think of places that this can impress people.

i've heard if you put two actuaries into a fish tank, they fight to the death immediately.

I agree with you there - the great thing about hiring a "Data Scientist" to do the job traditionally done by an Actuary is that you can usually pay them less (at least entry level and 1-3 year experience or so) and you don't have to pay for their exams.

However, there is still a lot of insurance regulation knowledge and business practice knowledge that comes with being an actuary over "just" being a data person.

What is the different between subject 'Statistic' and subject 'Probability and Statistics'? Is it a same shit with extra thing added or what?

>it wouldn't be too much to learn to set up with R and python and use neural nets to do fun stuff and crunch numbers
Sure, but I meant in terms of handling large datasets. Excel is quite awkward for that, and SQL is poor for analysis. If you're going to create a data flow task, why not do the whole thing in Python? If you're going to do statistical analysis on structured data, why not do it all in R?
>i want a math heavy degree
If you're talking about a first degree, then just do maths. Statistics isn't all that complicated, so you can have it as a minor, or a masters, or something. Any programming you can do on the side, since it's not too hard and employers will be able to assess your ability in the interview.

Made me kek

I can think of at least two companies I've dealt with in the past who would hire you JUST for the ability to make pretty visualizations. Even if there isn't a clear purpose for it, or it doesn't help them understand their business any better.

that's a good idea. straight mathematics major. it feels right. 300k starting.

that makes me glad.

It would probably be contract work, though.
Have you considered working for some media company? Many of them do impressive vizualisations. NYT have done some good interactive stuff, but I'd avoid applying there since they're probably gonna go bust.

i've thought about that kind of stuff, but it seems like it has a low ceiling and i really don't want to work in an 'artistic' field. i simply enjoy creating nuanced animations and effects where the qualia reflect actual information, creating a more fascinating piece of media that feels rich.

i think i'll just keep that as my secret hobby and break it out once in a while to impress people. who knows though.

i can't think anymore today. my head is full after having come into awareness of the existence of actuaries, and i have a lot of content to mush through now.

this has been productive and thank you mr actuary and various counselling anons.

stats undergrad here. Veeky Forums doesn't discuss it much at all and you should probably find somewhere else to take your stats-specific questions. there is more out there than actuarial science, which from my experience is not as secure or stomach-able as Veeky Forums claims.

>photoshop, aftereffects, indesign, illustrator, autodesk maya, autodesk inventor, autodesk revit, zbrush, unreal engine 4, etc
I doubt you'd use any of those in your work. R is honestly great for visualizations, there's a wide variety of packages out there to suit your needs and tastes

If you're looking to busy yourself with something before january, I recommend learning python 3. I get the impression that you haven't got much programming background, and starting with python will shore up those fundamentals much better than R will. Also, I think there's a handful of cs50 videos out there on data science in python.

>lots and lots of math
Depending on your school's program, you may have to actually go out of your way to get a proper math education. I got lucky and snuck into a school known for its math and CS programs, so even though the stats program here is mediocre, I can make up for it with math and cs classes. I keep running into people who majored in stats at state schools and became business analyst drones - all they do is compute summary statistics and write reports, they're glorified spreadsheet slaves. This can be avoided as long as you git gud at math and cs, but it's stimulating material and you seem eager for it.

I probably sound cynical. Just get yourself a pet project and you'll probably have a grand time. Teasing out some underlying structure in an otherwise opaque system, and leveraging that, is fucking amazing when you do it in the wild. It's much more rewarding than polishing a visualization because you're actually [math]discovering[/math] something, rather than summarizing it for some business major.

But Statistics try to explain the real world.

Also, if you're looking for inspiration to begin a personal project, shop around on /r/datasets for something to play around with. If you've already got a problem/topic in mind, they can also help you track down data.

I've been using Python 2.7 since forever for computations and such. Is it good to take the plunge to Python 3?

It's alright and mostly boring to me personally. I'd rather do pure mathematics and be poor.

oh, neat. alright.

well, i'm probably going to do 2 years at my community college to get the filler courses out of the way and then go to texas A&M, they're known for high quality, rigorous technical and scientific education. i do want to do something more interesting than just spreadsheet grinding.

The only reason not to switch up to 3.x is if you're maintaining a large code base that for some reason you don't have permission or time to translate up.

basically this:. If you've only been using it for basic stuff, the plunge isn't tough at all.

what do you think about astrostatistic and biostatistic?

Thanks for asking

biostats seems fishy desu. this is 100% hearsay but it seems like pharma companies employ them to just squeeze a nice p-value out of their clinical trials. they do get paid well though

there's a computational biology department at my school that does lots of cool shit in machine learning, but biostat folks don't hang out there much at all. So it seems like biostats isn't concerned with the creative side of research, and rather the validating side, if that makes sense.

astrostats is nice because you frequently have a lot of very nice physical models and data that adheres to it quite well. Unfortunately if you want to do astrostatistics for the rest of your life, you're confined to academia. i snuck into an astrostats conference at my school one time to steal their food - bunch of phds from big-name institutions talking about their work. Wished i could keep up because it was clearly exciting in material and methodology

What statistics courses should an undergrad take? I have an elective option of "intro to nonparametric stats" or "Introduction to Resampling Inference"

which 1 should i do

i'm and I haven't much experience with either tbqh. i already replied in your last thread but i'll add this

iirc "resampling inference" doesn't have strong programming prereqs. Is it geared towards people in sciences that are any softer than physics? If so it might be below your level. Universities make them take these classes in order to get them into research ASAP, so they'll probably gloss over the math underlying it and seriously dumb it down

Graduate students in statistics here. Both are useful, but I'd take resampling because bootstrapping is really useful.

thanks!

How accurate is ?

No disagreement. Depending on the professor and the course, you won't go into too much detail on the underlying theory. Resampling isn't really about math, it's a set of techniques used for extracting more information out of the data you've collected. They are practically useful techniques, and will probably be taught as such.

That being said, I think the same is true for non-parametric statistics, or nearly every undergraduate statistics course - unless you're at Stanford or Berkeley or somesuch.

There are a couple of annoying libraries that seem to still only work for Python 2 (e.g. libsvm, although scikit-learn includes libsvm for Python 3), but otherwise Python 3 should be the best choice.

Read some of the books by Nassim Taleb to avoid common pitfalls when applying statistical methods to real world data

1/2
I learn nothing new from Taleb statistically, but, boy is he great at marketing concepts. Lots to learn from him there -- how to become a household name by explaining what everyone in your field already knows!

In this 2012 article by Taleb Beware the Big Errors of 'Big Data,

And speaking of genetics, why haven’t we found much of significance in the dozen or so years since we’ve decoded the human genome?

Ans: (Implied) multiple testing is the problem and researchers aren't correcting for it. The real reason rather is that scientists over-estimated how important genes were and their role in causing diseases. Taleb is unnecessarily offensive and insulting. I get there maybe a handful of scientists that still don't correct for multiple testing correctly in genomics or neuroimaging. But *all* the statisticians who work in these areas and most of the scientists do. But if you are a layman, you wouldn't walk away knowing this.

And, I'm not the only one who thinks so. Larry Wasserman sums up Taleb pretty well in his Review of ``Antifragile'' by Nassim Taleb (a.k.a. Doc Savage)

Taleb is well-known for his previous books such as Fooled By Randomness and The Black Swan.

2/2I read Fooled By Randomness a long time ago and, as best as I can recall I liked it. I think the message was: “all traders except for me are idiots.” The message of The Black Swan was that outliers matter. It may sound trite but it is an important point; he is right that many people use models and then forget that they are just approximations. The Black Swan made a lot of people mad. He gave the impression that all statisticians were idiots and didn’t know about outliers and model violations. Nevertheless, I think he did have interesting things to say.

Antifragile continues the Black Swan theme but the arrogant tone has been taken up a notch. As with Taleb’s other books, there are interesting ideas here. His main point is this: there is no word in the English language to mean the opposite of fragile. You might think that “resilient” or “robust” is the opposite of fragile but that’s not right. A system is fragile if it is sensitive to errors. A system is resilient if it is insensitive to errors. A system is antifragile if it improves with errors.

To understand antifragility, think of things that lead to improvement by trial-and-error. Evolution is an example. Entrepreneurship is another.
Generally, top-down, bureaucratic things tend to be fragile. Bottom-up, decentralized things tend to be anti-fragile. He refers to meddlers who want to impose centralized — and hence fragile — decision making on people as “fragilistas.” I love that word.

I like his ideas about antifragility. I share his dislike for centralized decision-making, bureaucrats, (as well as his dislike of Paul Krugman and Thomas Friedman). So I really wanted to like this book.
is Doc Savage (the Man of Bronze).

The problem is the tone. The somewhat arrogant tone of his previous books has evolved into a kind of belligerent yelling. The “I am smart and everyone else is an idiot” shtick gets tiresome. Having dinner with your know-it-all uncle is tolerable. But spend too much time with him and you’ll go mad.

The book is full bragging; there are continuous references to his amazing wonderful travels, all the cafe’s he has been in around the world, zillions of references to historical and philosophical texts and a steady stream of his likes and dislikes. He particularly dislikes academics, business schools, and especially Harvard. He often talks about the Harvard-Soviet empire. He got an MBA for Wharton where he credits an un-named professor for teaching him about options. But, of course, the professor did not really understand what he was teaching.

We find out that Taleb hates TV, air-conditioning, sissies, and most economists. He has taken up weightlifting and, using a training technique he learned from “Lenny Cake” he can deadlift 350 pounds. He is now so strong that people mistake him for a bodyguard. You can’t make this stuff up.
I think that Taleb (last sentence of goes here but it got cut off)

Oh lord, I apologise for recommending him. I recently bought Antifragile, but have not yet read it. His earlier books are ok though.