#bias spreadsheet math

[blizzardfluffykpop]

Bias Spreadsheet Math

Okay, so I have a spreadsheet for my biases because I have quite a few (currently on this sheet: 27) and I like to keep facts about them stored. Just to see what lines up with the other ones. (Side note: None of my biases seem to line up...)

Anyways, I always have to manually put in the age they've turned. And I've had this spreadsheet for like a few years now. And it's tiring...

Cause I'm lazy & okay with math- I made an equation? So, the =DATEDIF() equation only works w/numbers. While Google does have the function for "TODAY" it wouldn't work in said function.

So, if I wanted it to always solve from birthdate to today. I'd have to come up with a different way, so I'd never had to type their ages manually again. I configured a little 'today's date' cell in the sheet. Thus, dubbing "AB2" as the dedicated cell.

And here 'X' will stand for any birthdate/cell w/birthdate. And years will be represented by "Y." After all, that is what we are solving for.

Thus the equation looks like:

=DATEDIF(X, AB2, "Y")

I'll be the first to admit I love math, but as a person who doesn't like to do the same tedious work over and over... This is going to make a nice difference when I look at the sheet again in the future.

Also, disclaimer: I didn't get the idea for the bias spreadsheet, someone else made the original. I have just made it custom to me. It does have some functions from the original, most of them have been scrapped tbh. Especially because this is my third version of the spreadsheet. (I don't keep up w/a lot of groups anymore, so I made a new one to decrease clutter). I don't know how much of it remains? I do have the link to the official one tho.

Anyways- if you've read on this far- and are still intrigued! If you wish to start your own: I have the link to the original and can also link mine! And if you'd like I'd be more than happy to help you set it up!

Idk I'm very proud of myself for figuring it out. It took me a little bit, but algebra was my favorite in school, so it came with ease! :)

(Also, this post marks: 25,000 posts *reblogs & originals* 🥰)

[back-on-my-autistic-bullshit]

Parkciv Species Headcanons Results!

It's been a few days since I closed the google form to responses, and I've finally finished consolidating all the information into an easy to read format! The post gets a bit long and there a decent amount of images beyond this point so I've put in a read more, if this information interests you - look below :]

To anyone who hasn't done the form and wants to you can find it here. After posting this I reopened it to responses but unless it gets a lot more responses I probably won't post about this form again.

Oh and, before you read:

General Info

In total the form got 42 responses, including my own. Of these 42 responses, 35 answered every question in the first two sections. Six different characters were mentioned with a headacnon that I missed, but they were only mentioned once.

I made a pie chart for each question. If your answer was a joke answer (there was a few of those, they will get honourable mentions later on - because they were funny) or not able to be easily categorised/summarised they have been defined as "other".

Major Parkour Civilization Characters Charts

Note: the "+ 2 others on pkv's chart includes: species apathetic + other, I didn't realise the image cut them off whoopsies

Minor/Background Parkour Civilization Characters Charts

Missed Characters

The following characters and headcanons were manually entered:

Fire Pro - Alien

(Amethyst) Fighter Ally - Cat Hybrid

Purple Pro(/Sonic?) - Hedgehog (with two sets of ears)

The Old Woman from The Library - Humanoid but not neccesarily human (the exact phrasing used was: "same not-quite-human as the old man")

Ice Disc Challenge Gateway Fighter Guard - Golden Retriever

Ice Disc Challenge Fighter doing the Ice Disc Parkour - Black Cat

Trivia and Extra Info

Parkour Villian/Clownpierce is the character with the most different species, ranking in with 20 different options - excluding other - and this did make the spreadsheet hell to organise but I dug my own grave with that one

From a statistical perspective (because, yes, I did the math), Evbo's Noob Neighbour is the character with the most agreed upon species - that being Human. The opposite of Clownpierce and saviour of my only shred of sanity left/silly

Cookie God is the only character who doesn't have a single answer as human. (No it's not EMF or House Selling Master, they both had one answer as human sadly)

Seawatt is the only character with a two way draw between species as a final result. Those two species are Sphynx and Human - both having 10 votes or taking up ~23.8% of the pie chart. Sphynx is also my own personal headcanon that I've never actually seen anywhere else prior to this and was the only reason it's listed, so it's nice to see people agree :] even if it might be due to a bias as the option was listed and didn't have to be typed in shhhhhhh

Honourable Mentions to:

The person who said Seawatt is a vampire. You're the only one who is right, ever (/j)

The person who put The Evil Champion down as a rooster because they hate chickens. That got a laugh from me

Similarly, the person who said The Evil Champion's species is "white" and nothing else

Whoever said "cocklit chip" for Cookie God because haha cock. My humour is very mature

One answer for The Ice Legend being "son of frosty the snowman and elsa. no im not taking criticism at this time". Understandable, but how dare you not ship Elsa with Jack frost from Rise Of The Guardians (/j)

Whoever said Deepslate with no further explanation for The Neo Legend. I respect it but what does that MEAN/lh

The answer "gleep glorp :]" for House Selling Master. Gleep glorp :]

Whoever answered "Woman😱" for Ice Block Master. Your answer lives rent free in my head, Woman😱 indeed

[broadwaydivastournament]

Will She Get a Tony Nomination?

Tony nominations are out tomorrow morning, and how am I coping, you ask? Like Madeline Kahn's (Not) Getting Married Today.

This season, eleven of our beloved Divas have opened a show on Broadway. In a remarkably overcrowded season for musicals, the competition is stiffer than ever, especially for our women of a certain age. Statistically, women over fifty (or even forty-five) are less likely to be nominated and/or win a Tony. I have done the math, I’ve crunched the numbers, I have a stupidly detailed spreadsheet, and the mean age for both nominations and wins of Leading Actress in a Musical is under 40. This is largely because the parts for older women just aren’t there in the same quantity as younger women. Since the Tonys began in 1947, only twelve women ov 50 have won for Best Leading Actress in a Musical. And two of them were Chita Rivera, so like… eleven, technically.*

*I have thoughts on why Bette Midler (oldest winner) should not have won but that’s a separate post…

That being said, let’s talk Tony likelihood. But wait, you say, what makes me (@droughtofapathy) remotely qualified to predict anything? Well, nothing. I’m just some stranger on Tumblr who doesn’t do theatre professionally. However. As of today, I have seen 209 Broadway shows, and by the time the Tonys roll around in June, I will have seen every new show eligible.

Also. I read the Tony Award Rules & Regulations handbook (all 26 pages) because why not?

Unlikely:

Andrea Burns (Featured Actress in a Musical: The Notebook)

Andréa Burns has never been nominated for a Tony before, and is unlikely to do so in a fiendishly overcrowded musical season this year. I love her dearly, and she always steals the show, but she would be entering as Featured Actress in a Musical, and it is rough out there, let me tell you. There’s probably over fifty featured roles for women, if not more (though most would not be serious contenders), and only maybe fifteen even considerable. Andréa Burns alas, falls short. She does basically nothing.

Emily Skinner (Featured Actress in a Musical: Suffs)

Oh, I love me a broad, but Emily Skinner is kind of your definition of a working actress who’s generally pretty well-employed, but not your top-line star, or even your go-to featured. I adore her, she’s wildly talented. And she plays bit parts on Broadway. Her roles in Suffs, an ensemble show at its core, are small and offer just a few opportunities to ham it up, but are not going to nab her a nomination. She’ll be one of almost twenty women on stage, with at least half a dozen meatier parts. Still. I am deeply obsessed with her whole performance and I need her carnally.

Jennifer Simard (Featured Actress in a Musical: Once Upon a One More Time)

Darlings, if you asked me this in the fall, I’d say she had a great shot. Was the show good? Nope. But was it fun? Yeah, it was pretty entertaining. Was Jennifer Simard the singular best thing about it? Oh, fuck yeah. She had audiences screaming and crying with laughter as Cinderella’s Stepmother (one of three Stepmothers in three separate shows on Broadway in 2023, and probably the one with the best part). Her “Toxic” is on youtube right now, and you need to see it. The vocals will blow you out of your seat. But it’s crowded, and that show did not last at all. Recency bias is against her. I don’t see it happening, but it’s the one in this category I would be most ecstatic about.

Long-Shot:

Uh, disclaimer, I will be seeing Uncle Vanya one day after nominations come out, so I'm just guessing here and don't quote me on any of this...

Lea Salonga (Featured Actress and Producer: Here Lies Love)

For the record, while I do think Lea Salonga realistically has less of a shot than Jennifer Simard for Featured Actress, I still put her here instead because she’s a beloved Broadway Diva who starred in a creatively innovative and novel show (that I hated it is, again, a separate post). I also don’t think the role itself is nearly large enough to garner a nomination, nor is it the sort of one-scene showstopping wonder that Katie Finneran was in Promises, Promises. So, unlikely, but hey, I’d love a win for Asians. She's also one of the show's many producers, which is more likely to net her a nomination. (Note: Lea Salonga getting a producer nomination for Here Lies Love is a likely for me, so move that down.)

Laurie Metcalf (Lead Actress in a Play: Grey House):

There are, I believe, eight leading actresses eligible for the Tonys, making this probably a four-person category, maybe five if they don’t hate women like they always do. And it is with great devastation that I report the four leading contenders are all white, blonde, screen actresses and I hate it. I hate everything about it. But I digress. Laurie Metcalf deserves to be nominated. Her performance was harrowing, unsettling, and so fucking weird. Grey House was an experimental horror play that didn’t open at the right time (mid-summer instead of Halloween? What the fuck?) and didn’t find its audience. But she had me in tears. If the world is good, and I doubt it, she'll get that fourth nomination. She's a Tony darling, so maybe?

Beth Leavel (Featured Actress in a Musical: Lempicka)

Almost no shot given the small part she has. The Baroness doesn't get much more than one heartwrenching 11 o'clock number at the end of the show, and little other chance to sing or shine. The book also doesn't lend her any opportunities to do much with the awkward and inelegant dialogue. But she's also a beloved industry favorite, so perhaps some miracle will happen? I doubt it. Especially given how the show's been received...

Jayne Houdyshell (Featured Actress in a Play: Uncle Vanya)

Jayne Houdyshell was a solid maybe/likely for me. Career vet with a great performance. I thought she'd get in. She got in for Music Man in 2022 and who the fuck saw that one coming? Yeah, she was good, but like…seriously? I mean, I know it was our first year back and all, but-- Actually, strike all that. I’m looking at the season list here and yeah, no, she deserved it. That tracks. I mean, I’d have put Luba Mason (heartbreaking) or Samantha Williams (a fucking delight) over her, but it makes sense. It was not a time in the world for downer stories. We needed levity and light and hilarity and that was Jayne Houdyshell. But apparently her part has been reduced in this production, so I'm amending my statement. Less likely than I thought. Sad times.

Anika Noni Rose (Featured Actress in a Play: Uncle Vanya)

Anika Noni Rose was our last hope of saving the Leading Actress category from the fucking white ladies with their A-list Hollywood names. But per the final eligibility ruling, she's featured. So there goes everything. Featured is just so damn stacked this year (and every year) that I don't know. But I swear, if I see the nominations for Leading Actress and it’s just a four-person box of white blonde Hollywood women, I will burn the American Theatre Wing to the ground.

Likely:

LaChanze (Producer: Jaja’s African Hair Braiding, Here Lies Love, The Outsiders)

What a career transition this has been for LaChanze. From being nominated for Best Leading Actress in a Play in 2022 to winning TWO Tony Awards for producing in 2023 (Kimberly Akimbo for Best Musical and Topdog/Underdog for Play Revival), she’s on cloud nine. And now she’s back again with three more contenders. I think/hope Jaja gets a Best Play nomination. Out of the ten new plays, four are hard nos, but the other six are strong. Jaja's was fantastic. I loved this play. Strong and smart and well-received. It’s a decent, but not guaranteed, candidate. As for Here Lies Love, well… maybe. It was an ambition and creatively novel endeavor. I personally hated it for political reasons, but maybe it gets a nod? It’s a bloodthirsty year, but this show is shaping up to be one of the best-reviewed of the season. The most likely candidate would have to be The Outsiders, which may also be a moderate contender for Best Musical overall. However, having seen it, I hate it.

If She Doesn’t, I’ll Eat My Sondheim Hatbox:

Something something parallels, doomed relationships, exquisite performances, chemistry...

Kelli O’Hara (Leading Actress in a Musical: Days of Wine and Roses)

I know I’ve been saying it’s a crowded year for musicals, but this one was already locked in from day one. It is unfathomable to me that Kelli O’Hara would not be nominated for an eighth time. She has been nominated for every single role she’s had on Broadway since 2005. She is the darling of Broadway, beloved by all, genuinely kind, unproblematic, hardworking. If you’re going to love a (living) white blonde lady, this is the one. Aside from that, she’s going to be nominated because her performance was exquisite. Heartbreaking, ugly, excruciating, gorgeous. Her voice in this show was as close to an aural sexual experience as you will ever get. Statistically, she probably won’t win. Leading Actress in a musical has not gone to a closed show since Angela Lansbury in 1975. But she’ll be recognized.

Bebe Neuwirth (Featured Actress in a Musical: Cabaret)

Bebe Neuwirth back on Broadway in a beloved classic role of a beloved classic show. It is a match made in paradise. Fraulein Schneider is a wonderful, understated, deeply moving role. It’s been nominated four times for every Broadway iteration there ever has been. Thrice in featured, and was even considered a leading role in the original 1967 Broadway production (hello, Lotte Lenya). If the streak breaks with Bebe, I’ll riot. The circumstances, crowded or not, are favorable. Bebe is a veteran stage actress. Voters and audiences think of her fondly. It’s a Kander and Ebb. She got the biggest rave reviews I've ever seen, even as the show itself got panned. Right now, I would say there are three sure-fire nominees, and she’s one of them. I want her to win. I need her to win. I need her to be one of only three nominations this goddamn production gets. (Her, Skybell, and maybe scenic design, even though I'm mad about that too.)

[elizabethrobertajones]

#Obviously the answer is we have to do a controlled study#make two femroe max height one fem one masc#make two femroe min height one fem one masc#all four will be the same dps job; dmv portrait; basic adventurer plate with no bio; limit text chat to Hi o7 and tyfp.#get to level 50 then start keeping track of comms on a sticky note or something idk unless you wanna get to 50 outside group content—-#maybe potd with all four roe?#i just thought about it and all four roe should have the exact same appearance settings except hair and glam#one set for mascs and one for fems#names should be generated by the name generator. i think goin with sea wolf would be better to avoid adjective/noun bias#and THEN at level 50#the same arr dungeon should be run once daily for the same month#and then compare notes#my one prof is out there somewhere furious that this is what it takes for me to take a study seriously#ofc we could do a poll/questionnaire but i feel like there’s too many unknown variables and the data would be more skewed#shut up ube go to bed#ohhh i wanna make a spreadsheet#WE COULD MAKE A GRAPH!!!#i hate math but god am i a whore for data visualization

@ubejamjar I'm actually glad I didn't see these tags before I went and ran my own much less exacting study with 4 different femroes XD

it's incredibly funny but pre-DT i spent my whole time as a max height femroe and i got a LOT of comms. when portraits were introduced, comms went up. i do all my group content as dps so the amount of comms i got was truly disproportionate to what dps is "supposed" to get.

when DT launched i paid real american money to namechange to estelle properly, and while i was at it i also made her a min height femroe to reflect her better (because she is mixed race hyur and thus Short For A Roegadyn)

since doing that comms have tanked. i get half the comms i used to. girlies, they were literally only here for our height and if you don't have that to offer they don't give a shit about you. punish them

[dontrollthedicesideblog]

ok you know what im incredibly mad about the methodology used for that paper and the conclusion they came to what the fuck

[aroaceacacia]

"scar is a textbook sexyman!" "I think techno fits it better!" you all dont understand. sexypedia has worked extensively to define and understand sexymen and similar phenomena.

here is my spreadsheet comparing sexypedia's list of sexyman tropes vs goodtimeswithscar and technoblade.

declaring my bias: i am a techno voter who has watched plenty of techno and scar content but is not an expert in either. I may have forgotten key aspects of each character. I did no research & went in blind uncertain of which of the two might pull ahead.

After doing some math, Techno's sexyman trope score is 38 and Scar's is 34.5, suggesting that both are sexymen, but that Techno may have a slight advantage. (My reasonings for many of the choices I made are included on the sheet, as is the math.)

Is this scientific? No! But its more scientific than just claiming one candidate is more of a sexyman without any explanation or examination of what being a sexyman actually entails.

[wild-aloof-rebel]

I'm curious when/how/why you started compiling information through such detailed spreadsheets. I'd like to be more like that because I'm sure it'd help me keep track of things a lot easier but I just CANNOT get myself to think in terms of columns and rows and formulas. I'm assuming your brain is just naturally very analytical/systematic, so was this something that just happened organically or did you sort of have to teach yourself? Was it something fostered through school/work?

let me start with the why, which i think is really a three-fold thing...

1) i like making lists. i’ve always been the kind of person who makes to-do lists to keep me on track when i really need to buckle down and work. i make lists of things like my favorite movies for the purpose of being able to compare and rank them. (those bias sorters that were going around for a hot minute and the tiermaker thing that’s been going around more recently? very much my jam.) and the kinds of spreadsheets i make are basically just complicated lists. my fic spreadsheet, for instance, is essentially just a list of every fic, but because of how i decided to track things, it’s also a list of every fic where david and patrick are married, a list of every fic that includes twyla, a list of every fic with fake dating, etc etc etc. it’s like list-ception lolol. lists upon lists, all combined into one easier to use format than having all those things be their own separate lists.

2) my brain always wants to Know All the Things. i am the ringer for my local trivia team for this precise reason. if i decide i want to learn about something, i want to learn everything possible about it. like for sc, i didn’t start out knowing alllllll this stuff about the fashion on the show. someone asked me a question about it once and i went, huh, idk but i’ll go find out. and then that just tumbles over into, okay well now that i know that particular thing, what else can i learn? and eventually i just... learn all of it lolol. i mean obviously not all because where does that even end, but basically my brain is just like, we will not be satisfied until we are an expert in this in some way. and while i do truly remember lots of what i learn (e.g., i can def rattle off who makes like 95% of david’s clothes from memory), a) it takes a while to get there and b) there are some things i don’t care about remembering quite as much (e.g., i’m never gonna truly memorize who makes every pair of shoes alexis has ever worn because i’m just not as invested in that). that’s where the spreadsheets come in. they’re a good reference for things i don’t care enough to remember (or things that are just too impossibly large to memorize, like every fic in the fandom) but still want to be able to easily access, and they’re essentially a study guide for the things i just don’t remember yet.

3) if we’re using a sensory modalities learning model, i’m very much a visual and physical learner, and spreadsheets satisfy both of those modalities for me. they’re basically like a visual map of data, but one that i can also physically manipulate when needed. in a non-spreadsheet example, i decided several years ago that i was gonna learn all the world capitals. to do that i made flash cards and laid them out in an approximation of their place on the world map. then i set about memorizing them through repetition, the same as you would with any flash cards, except that now there was an added visual component to it which becomes part of the learning process. so now when i think something like what is the capital of burkina faso, picturing where burkina faso is on the world map gets me to the capital is ouagadougou. that mental image of where that flash card was helps me recall the information that was on it. and my spreadsheets work largely the same way. i certainly don’t remember every fic on my spreadsheet, but often when i can’t recall an exact title, i do remember things like which sheet it’s on and the general area it’s in on that sheet, which at least helps me find it faster. formatting can help with that too--on some spreadsheets i can picture what color the cell is that’s associated with the data i need, etc.

so that’s the long why of it all. the when and how is basically just i slowly started doing it as an easy way to keep track of simple lists and over time it morphed into the somewhat more complicated lists i now keep. but that’s really still all they are--lists.

yes, there’s sometimes math and formulas involved now, but really those kinds of things only come into it when i want to play with the data that i’ve collected. like okay i’ve got this spreadsheet full of fics; if i then want to look at each author and see how many fics they’ve written of each rating, i can use a pivot table to do that. if i want to know what the average length of a fic in this fandom is, i can use formulas to do that. if i want to color code new additions by word count, i can use conditional formatting to do that. 

i’ve taught myself about those kinds of features over time just because they give me new ways of looking at the data i already have (and pretty much everything i know about using any of that is stuff i’ve just learned by googling it and then the trial and error of applying it). but all of that is really just window dressing laid over a whole lot of lists. 

[argumate]

let's see if I can figure this out starting with a simple example grabbed off Google:

If an insurance company sells a fire insurance policy with a premium of $500, and the probability of a fire is 0.001, and the payout for a fire claim is $100,000, the expected value for the company would be positive even though a few claims could result in large losses, as the majority of policyholders will not make a claim.

so the insurance company charges each client $500 and expects to lose approximately $100 per client on average, thus making a profit of $400 per client, so it's positive expected value for them, so why would the client take this bet?

often people say "loss aversion" or "risk aversion" but that's a complicated term as it can suggest an irrational bias; would the client be better off without insurance? is there an objective way to measure this?

yes there is: using the Kelly Criterion! this states that your willingness to engage in a risky bet depends upon the risk of the bet but also the amount wagered relative to your overall wealth: the less money you have, the more a loss will hurt you.

now I haven't worked out the maths for this before so I'm going to crib from this blog post and see if I get it right; let's say that your original wealth is W so if you buy insurance it's going to be W-500 (regardless of whether or not there's a fire) and if you don't buy insurance it's going to stay as W with 0.999 probability and change to W-100000 with 0.001 probability.

the Kelly Criterion maximises your long-term expected geometric growth rate by maximising the logarithm of wealth, so we have:

with insurance: log(W-500)

no insurance: log(W)*0.999 + log(W-100000)*0.001

what value of W makes the no insurance option better, time to remember those pesky logarithm rules:

log(W)*0.999 + log(W-100000)*0.001 > log(W-500)

log(W^0.999) + log((W-100000)^0.001) > log(W-500)

log(W^0.999 * (W-100000)^0.001) > log(W-500)

W^0.999 * (W-100000)^0.001 > W-500

and if we solve that for W by a series of numeric machinations too tawdry to detail (my algebra failed me and I resorted to a spreadsheet I'm so sorry) we find that if W = 100150 then the log wealth of not buying insurance is greater than the log wealth of buying insurance.

if I'm interpreting that correctly it suggests that if you were betting that your house won't burn down then you should ensure you have enough cash to rebuild it in case it does burn down, otherwise you'll get wiped out, and if you keep rolling those dice long enough then eventually you will get wiped out.

and we can see that there are bets with positive expected value for both sides because the total wealth of the insurance company is much higher than any individual client, so they can risk more in absolute terms and still expect to grow the logarithm of their wealth faster by doing so (as long as they're accurately assessing the true risk).

in summary it's rational to insure expensive things that would wipe you out if you lost them! and rational not to insure cheaper things where the replacement cost is a small fraction of your wealth.

for more on this look at ergodicity economics, which simplifies a ton of apparent paradoxes by assuming that humans (and possibly every other living organism and institutions in general) generally seek to maximise their geometric rate of growth and make decisions on that basis (modulo errors and misunderstanding) and simplistic expected value calculations that don't take this into account lead to confusing and unrealistic results (and can't explain insurance).

The California insurance thing is adding on to my established belief that insurance is inherently a bad mitigation for any risk that affects large swaths of the population who will very likely need it at some point.

The point of insurance is for the collective to pay an affordable regular fee, and the few who are affected by a given risk are compensated. Ideally, the EV of insurance would be 0, but in practice the EV is negative, but since we are broadly risk averse* we want it anyway.

But that only works if the fee is affordable and the risk is low. Fire insurance in California was a bad idea because everyone was at a major risk, the full cost of premiums would have been immense. California made it worse by making it illegal to charge for the real cost, but even if they hadn't, the premiums would have simply ballooned and many people would have been priced out of insuring their homes, and would still be in this situation.

Similar thing happens with healthcare. Everyone wants their insurance to cover everything, including very common, every day medications for long term medical problems. Needing my ADHD medication isn't a risk, it's not at the tail end of the probability distribution. My insurance shouldn't cover it, because covering it means that the company has to raise everyone's premiums, and raising the premiums prices people (myself included) out of being able to afford insurance for actual tail risks that could ruin their lives.

"But I need my ADHD medication to live a good life, it would ruin me to not get it" yeah, I hear you, same here, but insurance is just a bad system for dealing with that. It just isn't a risk, it doesn't fit well with the "small affordable fee to avoid unlikely disaster" model. If insurance pays for everyone's lifetime medications, then it has to raise premiums by the average of the cost of everyone's lifetime medications, and everyone breaks even, except now insurance is just more expensive and some people are priced out.

I think anytime insurance starts to sail out of the price range of the target demographic, it's a sign that something is deeply wrong and some intervention, likely government intervention, is needed. If people can't afford the fire insurance, it's because the fire risk is way too high and your forest management strategy should be overhauled. If people are struggling to pay their health care insurance, their insurance probably covers way too much because too much of every day health care is also out of reach of people.

Insurance companies aren't the good guys, you have an adversarial relationship with them. They will try to screw you over and lie to you and scam you and all the rest. But you can't get blood from stone, and you can't get a policy of "pay me money if my home built on top of kindling burns down" without paying for it somewhere, and instead of getting mad at the insurance company you should probably start asking why your home is built on kindling.

*Or, well, more complicatedly the full cost of some disasters is much worse than than strict accounting cost. Losing your home is far more destructive than losing the market price of the home's value, because it means spending weeks to months homeless and opening you up to other risks.

[joanofarchetype]

had a video appointment with a neurologist today because i’m looking to get assessed for potential dyscalculia? like it was always something my mom floated as an idea when i was in middle/high school for why i had SO much trouble grasping and retaining basic math concepts when i didn’t have trouble in anything else and was in honors classes but kept out of remedial math by the skin of my teeth, and i shrugged it off because “dyslexia but math” doesn’t accurately describe the semiotic nuance of seeing integers but not actually having them mean anything to you. and when i was in undergrad, i took a class on fractals that changed the way i looked at math and it made sense to me in a way it never had, and there was meaning at the higher levels. and now that i’m getting my master’s in a STEM degree, i’m doing discrete maths, but anything where we’re talking digits and integers and equations and fractions just means nothing to me. and then i realized i struggle with finances, counting change, spreadsheet cells are physical agony and i make mistakes no matter how many times i check, i have trouble with choreography, i can only measure time in songs and tv episodes, and i’ve always learned to sing everything from memory because despite having taken about five separate music theory classes i cannot for the life of me read sheet music and just like my ADHD, maybe this was something to look into. it just felt like if these things were connected then there could be a way to better understand why i don’t grasp something no matter how many times i run through it, when i’m so interdisciplinary and otherwise able to grasp complex theory/concepts in nearly every other field at speed.

i did relatively fine in the mini assessment he led me in (though counting backwards from a hundred in sevens literally raised my heart rate and made be turn red) and he admitted he was a stroke specialist and didn’t know as much about cognitive disorders but this was obviously important to me and he referred me for a neuropsychological exam to get some clarity and find out if there is a wiring thing at play. and i’m determined to figure it out but also worried, because just as with ADHD there are a lot of misconceptions and because most information is for how it is exhibited in children and also at its most severe, it means we don’t tend to catch people who are at a different spot on the spectrum with maybe not all of the classic discalculic markers? and i definitely don’t have all of them. but that’s how so many people with ADHD (especially adult women) go through their lives finding ways to compensate for the invisible struggle instead of getting the proper tools they need. it’s why i only got medicated for ADHD at 27, and my mom just went up through her 60s not realizing that her ADHD wiring was responsible for the things she attributed to laziness and mess, but also for the parts that made her a brilliant scientist. 

i’m not discouraged by this if i do have it? it only motivates me even more to get my degree in computational linguistics, which i went into to translate this absolutely perplexing language i’ve never understood but have begun to understand through myth and metaphor, and because my ADHD brain learns interdisciplinarily, and i can use those leaps in storytelling to help others who might have different ideas and perspectives who aren’t getting the tools they need and are being gatekept from success in our current system.

so idk, on the one hand i’m worried about bias, and not getting the right specialists who understand it’s a spectrum disorder and know what to look for in adults and especially those who overcompensate in other areas and don’t exhibit traditionally, and how much of that spectrum do we even have data for?? and on the other hand i’m worried that i don’t have it and i’m “just” bad at math, and “just” incapable of doing what neurotypical people consider basic tasks, and if i’m not neurodivergent enough for dyscalculia to be the reason and i won’t be able to crack my own code. 

anyway. 

[cleverblog361]

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[scienceknowledge]

What University Courses have the Most Real-World Applications?

      I haven't done any posts in a while, and I was thinking tonight about which classes are applicable to the real world. Since I have been graduated from university for over a year now I want to share my experience with what courses I believe have skills and knowledge that are transferable to the real world.  

      First off I'd like to start with recommending which courses I believe every student should take that are very beneficial in the real world. I will make a list and give a short description as to why I think it is important to take said course(s).

 At least 1 psychology course, preferably two

People are everywhere. We are physical things with a mind as a command center. We are our thoughts. To know how the mind functions is to better control it, and thus better applying oneself in practice. Also, knowing how other people think benefits relationships, a crucial part of professional and personal life.

 1 anthropology course

The study of human life through space and time at a social level. I think knowing about human life in general, instead of sticking to only knowing about our own proximal (near) cultural societies, creates tolerance and can reduce discrimination

1 chemistry of physics course. (maybe both). A course with a strong focus on equations

The study of the physical and chemical environment. We live in a world that is physical. I'm typing on a solid keyboard. As we move we're applying physical principles. All things abide by the laws of physics (except God of course who created these laws). We live in a chemical world. Do you know what the ingredients in your toothpaste are? That sliced processed cheese is basically one molecule away from being plastic? How does the ingredients interact in your cooked meals? Should you open your windows when it's warm out as well as inside, or leave them closed? By learning the principle of physics and behavior of molecules, you understand this so much more. Knowing how to manipulate equations will make it much easier to apply simple formulas to work out different things you need to calculate in life (e.g. If I have two 500ml drinks at 4.9% alcohol, and one 100ml drink at 19% alcohol, will my blood alcohol be at a safe level to drive after 1 hour without penalty?)

1 stats course

Stats are in advertisements, polls, news reports, you name it... To understand what these stats means makes you a better and more knowledgeable consumer. You will learn how It's easy to bias and stretch stats to look better or worse. And you can also understand better things like percentages of chance based on choices you make.

1 computer science course

We're living in a more and more technology-driven world. Basically, everything is like a little computer now to at least some extent. I see everyday ways that I can implement coding or algorithms to make my life easier (although I feel inadequate at programming). Programming can be intimidating and a challenge, but it’s invaluable. And the skills are transferable. Do you know what a bitcoin is? Why it's important? How it functions? Computer science can help understand things like this, and also Economics, which brings us to.....

1 economics course

The world is driven by currency. "Money makes the world go 'round" as they say" We use it every day as a means of trading goods. It's a store of value. Banking, sticks, investments, bitcoin... It all falls under economy. Do you know how much opportunity cost your schooling incurs?  

1 biology course

The world is a large ecosystem consisting of many living biological organisms. We’re a biological organism and also an animal. Bio "life" logy "study" - The study of life. Di you know there are more microbes living on/in you than cells of your body? That you breathe in hundreds of microbes with every breath? Why you wouldn't get sick when you do breathe these in? Biology teaches you all this. I rarely get sick because I understand the behavior of microbes, so that I can manipulate my environment to be more sanitary.  

1 or 2 philosophy courses; 1 logic mandatory

Philosophy is the love or study of knowledge. How do things work at a conceptual level? Are we a body or a mind? What is a mind? (as a tidbit, there are probably hundreds of theories of what a "mind" really is). Why does anything exist at all?  What is our basic human nature? All these things are studied by philosophy, and philosophy offers a much expanded and diversified way of looking at the world.

      Logic is the most mandatory course I will add to this list. Logic is the study of knowledge and its applications. It studies arguments and fallacies. The relationships between evidence and conclusions. It makes it much easier to see people that make errors in logic, even when on the surface they appear to be saying the truth. It will increase critical thinking.  Sometimes a logic course will include a symbolic logic component, but please don't be scared away from logic because of this. It can be the most difficult part for some, but the takeaway from the course will be invaluable. Symbolic logic is basically arguments which transcend language and words and overlaps into math and computer science territory. Here is a YouTube video of intro symbolic logic.

      As a real life professional example, in my current job as a QA Tester I need to sometimes evaluate formulas and I use the scientific method to perform experiment tests to see if the game stats I get are correct. I use excel to perform calculations to see if numbers add up correctly. Knowing about stats, how to use excel spreadsheets, manipulate formulas, and evaluate data is essential to performing efficiently in this role. (Chemistry, stats, physics might help for this role). It is also helpful to be able to coordinate and work effectively with team members (psychology/philosophy helps here). Knowing about fallacies and how some testers (myself included) might think some things are true or evident when they aren’t can be seen through using logic. I use logic to determine sets of conditions to test and how to use all possible combinations of possibilities to test all possible conditions.

     I don’t think I’ve ever really used calculus, symbolic logic, or computing things like the energies or molecules and I label these things as academic or job-specific knowledge. For example, a nuclear physicist might use all these things. An average Joe Schmoe won’t. A basic math proficiency is plentiful, there are things like numeric calculators and Google to find equations and different specific calculators when you need them.

      This is a list of courses I urge everyone to look into taking while they are in university if they can fit it into their schedule, and why I think they are important. Use it as distribution credits or as requirements towards a major/minor. These courses in my experience and opinion are invaluable and highly applicable to the real world. The course material of all these courses might not apply directly to life – a person might never need to calculate parameters of electrical circuits in their life – but the transferable knowledge of knowing how to work with complex formulas, will be invaluable.  

[impala-pies-and-cas]

Does Supernatural Have a Problem with Representation and Diversity: A Mathematical Study

At the end of season 12, another fan favorite minority character, Eileen, was killed. This has come in a long line of favorite SPN characters who were people of color, women, lgbt+, and/or disabled being killed seemingly before their time. This, like other instances with such characters like Kevin and Charlie, sparked outrage from many fans. Some called the move sexist and ableist. Many said it was not inherently bad that Eileen died, but the way it was done was disgraceful and unworthy of such a beloved character. Other fans fought back against these claims, citing that everyone dies in supernatural and that no one should be immune. Besides, others said, with more representation, shouldn’t that mean more death?

But is there actually more representation? And is the death count equal? Are we being persuaded by biases and personal agendas?

After the season 12 finale, I’ve set out to see if there is a quantifiable difference in representation, huge differences that can be backed up by numbers and not just perception. Much of this is going to cover gender and race, as those are the easiest diversity angles to notice, but I will touch upon other areas. This information was not compiled to confirm any set of biases, but instead answer these questions at the heart of the debate and anger. Some of the information complied is quite obvious, but having set numbers is vital in these debates.

The rest, which is a lot, is under the cut:

A few notes/disclaimers before we begin:

All information is taken from seasons 1-12. When season 13 starts the numbers on here will, no doubt, have to change.

I have not counted every single character ever put on Supernatural ever, but instead elected to take my sample size from supernaturalwiki.com. I was originally going to pull a list from imdb, but I didn’t just want to record who was in what episode, but also who lives and who dies. To do this I would have had to closely watch the entire series over again. I have school and a job, so I have no time to actually do that. Instead, I collected my sample size from a site that has that information already on it. [I may end up redoing this using imdb and rewatching the show, but that will take months - if not years].

Jumping off of the first point, I have collected about 850 characters for this experiment that uses a lot of math and percentages. Obviously, these numbers are not entirely accurate since I couldn’t find everyone, but I highly doubt the percentages would greatly tip the scale in any minorities’ favor by a recount. This is just an example, but it’s a large sample size example that still reveals a lot.

Although many characters may not inherently be the same gender/race as their actor counterparts [see angels and demons] I am using the actor’s race and gender as the character’s. This is about on screen representation, so what you see is the most important. And, before anyone asks, no, i did not go up to every actor and asked them if they were a person of color or what gender the identify as; i guessed, but they were educated guesses. I followed this up until the show directly contradicted the casting. For instance: the actress playing the angel Benjamin is a black woman, but the character was presented as using he/him pronouns, so I listed Benjamin as a man of color.

In the cases of characters with multiple actors who fall into the same category (ie Meg’s two actresses are both white women) the character is counted once under that group (Meg is a white woman). In cases were the actors are in different categories (ie Raphael was portrayed by a black man and black woman), I count the character twice under each group (Raphael is counted once as a man of color and then again as a woman of color). I didn’t feel comfortable choosing one, and saying a character is 50% one thing and 50% another seemed more harmful that just counting them twice.

There are a few characters I couldn’t pin down as either gender, as either the wiki only used neutral pronouns with them or they were a straight up genderless creature, so I have a few characters in a gender neutral category. I have listed them, but they are by and large excluded from the majority of the analysis.

This is only about numbers and percentages, not how characters are portrayed on screen. The latter is more subjective and hard to discuss without bias getting involved. After all, I am one queer woman who can’t speak for everyone. 

If you’d like to see my very annoying spreadsheets documenting all of this, click here: [x]. For larger versions of the graphs, you can find them here [1] [2] [3] [4] [5] [6] [7] [8]

And now onto the graphs:

[Graph 1]

When you look at a strict gender breakdown of the characters, it comes out to be about 60% men and 40% women, which is, proportionally, a bit too uneven. The show should be hovering closer to the 50/50 mark. However, when race is added, the proportions get depressing. White men (blue )make up almost 55% of the total population of the show. People of color combined make up just over 10%, with each (moc are green and woc are orange) at about 5%.

[Graph 2]

The numbers get more interesting as we break it up into percent alive (the light colors) and precent dead or status unknown (the darker). While all groups have more people dead than alive, both groups of women have 43% of their population alive. White men have about 30% percent. Men of color are the most killed off group, at 20% still alive.

[Graph 3]

I further broke up the categories into how many episodes these characters have been in [this is not separated into alive or dead]. The brackets are 1 episode (top left), 2-3 episodes (top right), 4-6 episodes (middle left), 7-9 episodes (middle right), 10-12 episodes (bottom left), and 13+ episodes (bottom right). The columns go from left to right with white men, men of color, white women, and women of color.

NOTE: there are always far more characters in only one episode than any other grouping, and numbers of characters in each group constantly get lower on a bell curve. However, it is important to look at the percentages and comparable representation in each grouping.

As you can see, white people always have higher columns than people of color. You may also notice that at the 7-9 episode mark the graph loses columns. As of the season 12 finale, no woman of color has been in 7 episodes. While there is one man of color who has been in over 13+ episodes (Kevin), there are currently no men of color in 10-12.

I decided to break this up further into individual graphs for the different race/gender categories: first there are pie charts showing the group percentages in each number of episodes bracket . The second is a bar graph looking at the alive/dead status of characters in each number of episodes bracket.

[Graph 4]

There are so many men in 1 episode I had to log the graph. They currently have more characters in every category, save for 7-9 episodes where white women lead. Also, as of the end of season 12 there is at least one white man alive in each number of episodes bracket.

[Graph 5]

While they have far less characters in total than white men, white women do have a similar percentage breakdown.They do beat white men in the 7-9 episodes bracket, and like white men have at least one person who is alive in each group.

[Graph 6]

The graphs for men of color look very different than the other two. The do have a lower percentage of characters in only one episode, but that is mainly due to the lack of characters overall. Further, there are far more dead men of color than alive; the number of dead men of color in one episode is at a 78% death rate, far higher than any other group. They also don’t have any currently living characters past 2-3 episodes.

[Graph 7]

Women of color have, quantifiably, the worst record for representation in these categories. As I said earlier, no woman of color has been in at least 7 episodes. While they are tied for the group least killed off, they have so few characters introduced that it hardly makes a difference.

[Graph 8]

I also wanted to look at the number of people of color I could find in each season [once again i’d like to reiterate that this is based on the characters I could find from supernaturalwiki.com]. This bar graph has men of color in green and women of color in orange, tracking the number of each and both of them combined per season. While I give credit to season 12 for having the highest number of characters of color, that’s still at a lousy 16 characters, and when you can easily find over 100 characters per season, 16 is nothing to applaud. Further, if you count up all the characters listed in the graph, you reach 98. Not even 100 characters. That’s less than one new character of color being introduced every two episodes, which should be ridiculously easy to do. Supernatural still cannot do that.

I did also want to look at lgbt+ and disabled representation in the show.

NOTE: while characters like Hannah and Raphael have been in different gendered vessels, the show has never confirmed them as trans or non-binary, and I couldn’t find any human characters that were definitely not cis. I have decided that because of this lack of clear information not to collect stats on trans/non-binary characters on Supernatural, but that might speak for itself.

I found 17 characters who were confirmed in show to experience some form of same gender attraction. That makes up 2% of the show’s character population. Of these characters, 5 of them have been in multiple episodes. As of the end of season 12, 5 are dead (3 of the five in multiple episodes are dead). 5 of the 17 are people of color (only 1 of them has been in multiple episodes so far).

Tracking disability is harder, as many disabilities are invisible, so I stuck with characters with physical disabilities in multiple episodes, of which are only 3: Pamela Barnes, Bobby Singer (in season 5 he was in a wheelchair), and Eileen Leahy. They make up 0.35% of the show’s population. 2 of these characters were cured of there disability before their last episode on the show. All three are white. All three are dead.

So does Supernatural have a problem with representation and diversity?

Yes. It most certainly does. But not in the way people expect or often perceive.

Women aren’t being killed off at higher rates than man. Actually it’s quite the opposite. And white, straight, able-bodied women are pretty good in terms of representation.

The real problem is with the representation of people of color, disabled people, and the lgbt+ community.

And really, it’s not in the rates of them being killed off (well, men of color need to be killed off less). The problem lies in that these characters aren’t being introduced in the first place. It really doesn’t matter if 30% of white men are alive verse 43% of women of color when that comes out to 141 total living white men and 19 total living women of color. It’s not fair playing field.

Supernatural is a show set all across The United States of America and lives in it’s culture and lore. Nearly 40% of the United States is made up of people of color: black, asian, native, latinx, arab, etc. The show should reflect that. While the numbers on lgbt+ representation is still being disputed, the perception is that 4-10% of the population has same gender attraction and 0.6% are transgender. The show should reflect that. According to the US census, about 19% of the population has some sort of disability. The show should reflect that. It’s more than just adding in a few new characters of color and lgbt+ characters in season 12; tptb need to purposefully write in more diverse characters, cast diverse actors, and keep these characters around longer.

When people complain about Kevin, Charlie, Eileen, or others’ deaths in the show, this isn’t a matter of being sad a character is dead and not understanding how the supernatural death toll works. It’s being frustrated at a show which has so little representation and having one of the few characters in that category being ripped away from us, often in ways that are easily avoidable and/or disrespectful to the character. It’s characters being killed early on so we don’t have characters of color, lgbt+ characters, or disabled characters to go through the seasons with. It’s getting the bare minimum of representation and being told that’s enough and we shouldn’t complain any more.

There are people that aren’t bothered by the lack of diversity, and that’s fine. You’re in the full right not to care. But telling those who are frustrated and upset that they are overreacting, being childish, and are not true fans is beyond rude. It’s a silencing tactic, and it needs to stop.

No matter what side of the aisle you’re on, I hope people will read this and gain a better understanding of where Supernatural’s diversity is and why people may be mad. And, no matter what, the proportions tell us a change needs to happen.

[sciforce]

A Layman’s Guide to Data Science: How to Become a (Good) Data Scientist

How simple is Data Science?

Sometimes when you hear data scientists shoot a dozen of algorithms while discussing their experiments or go into details of Tensorflow usage you might think that there is no way a layman can master Data Science. Big Data looks like another mystery of the Universe that will be shut up in an ivory tower with a handful of present-day alchemists and magicians. At the same time, you hear about the urgent necessity to become data-driven from everywhere.

The trick is, we used to have only limited and well-structured data. Now, with the global Internet, we are swimming in the never-ending flows of structured, unstructured and semi-structured data. It gives us more power to understand industrial, commercial or social processes, but at the same time, it requires new tools and technologies.

Data Science is merely a 21st century extension of mathematics that people have been doing for centuries. In its essence, it is the same skill of using information available to gain insight and improve processes. Whether it’s a small Excel spreadsheet or a 100 million records in a database, the goal is always the same: to find value. What makes Data Science different from traditional statistics is that it tries not only to explain values, but to predict future trends.

In other words, we use Data Science for:

Data Science is a newly developed blend of machine learning algorithms, statistics, business intelligence, and programming. This blend helps us reveal hidden patterns from the raw data which in turn provides insights in business and manufacturing processes.

What should a data scientist know?

To go into Data Science, you need the skills of a business analyst, a statistician, a programmer, and a Machine Learning developer. Luckily, for the first dive into the world of data, you do not need to be an expert in any of these fields. Let’s see what you need and how you can teach yourself the necessary minimum.

Business Intelligence

When we first look at Data Science and Business Intelligence we see the similarity: they both focus on “data” to provide favorable outcomes and they both offer reliable decision-support systems. The difference is that while BI works with static and structured data, Data Science can handle high-speed and complex, multi-structured data from a wide variety of data sources. From the practical perspective, BI helps interpret past data for reporting or Descriptive Analytics and Data Science analyzes the past data to make future predictions in Predictive Analytics or Prescriptive Analytics.

Theories aside, to start a simple Data Science project, you do not need to be an expert Business Analyst. What you need is to have clear ideas of the following points:

have a question or something you’re curious about;

find and collect relevant data that exists for your area of interest and might answer your question;

analyze your data with selected tools;

look at your analysis and try to interpret findings.

As you can see, at the very beginning of your journey your curiosity and common sense might be sufficient from the BI point of view. In a more complex production environment, there will probably be separate Business Analysts to do insightful interpreting. However, it is important to have at least dim vision of BI tasks and strategies.

Resources

We recommend you to have a look at the following introductory books to feel more confident in analytics:

Introduction To The Basic Business Intelligence Concepts — an insightful article giving an overview of the basic concepts in BI;

Business Intelligence for Dummies — a step-by-step guidance through the BI technologies;

Big Data & Business Intelligence — an online course for beginners;

Business Analytics Fundamentals — another introductory course teaching the basic concepts of BI.

Statistics and probability

Probability and statistics are the basis of Data Science. Statistics is, in simple terms, the use of mathematics to perform technical analysis of data. With the help of statistical methods, we make estimates for the further analysis. Statistical methods themselves are dependent on the theory of probability which allow us to make predictions. Both statistics and probability are separate and complicated fields of mathematics, however, as a beginner data scientist, you can start with 5 basic statistics concepts:

Statistical features. Things like bias, variance, mean, median, percentiles, and many others are the first stats technique you would apply when exploring a dataset. It’s all fairly easy to understand and implement them in code even at the novice level.

Probability Distributions represent the probabilities of all possible values in the experiment. The most common in Data Science are a Uniform Distribution that has is concerned with events that are equally likely to occur, a Gaussian, or Normal Distribution where most observations cluster around the central peak (mean) and the probabilities for values further away taper off equally in both directions in a bell curve, and a Poisson Distribution similar to the Gaussian but with an added factor of skewness.

Over and Under Sampling that help to balance datasets. If the majority class is overrepresented, undersampling helps select some of the data from it to balance it with the minority class has. When data is insufficient, oversampling duplicates the minority class values to have the same number of examples as the majority class has.

Dimensionality Reduction. The most common technique used for dimensionality reduction is PCA which essentially creates vector representations of features showing how important they are to the output i.e. their correlation.

Bayesian Statistics. Finally, Bayesian statistics is an approach applying probability to statistical problems. It provides us with mathematical tools to update our beliefs about random events in light of seeing new data or evidence about those events.

Image credit: unsplash.com

Resources

We have selected just a few books and courses that are practice-oriented and can help you feel the taste of statistical concepts from the beginning:

Practical Statistics for Data Scientists: 50 Essential Concepts — a solid practical book that introduces essential tools specifically for data science;

Naked Statistics: Stripping the Dread from the Data — an introduction to statistics in simple words;

Statistics and probability — an introductory online course;

Statistics for data science — a special course on statistics developed for data scientists.

Programming

Data Science is an exciting field to work in, as it combines advanced statistical and quantitative skills with real-world programming ability. Depending on your background, you are free to choose a programming language to your liking. The most popular in the Data Science community are, however, R, Python and SQL.

R is a powerful language specifically designed for Data Science needs. It excels at a huge variety of statistical and data visualization applications, and being open source has an active community of contributors. In fact, 43 percent of data scientists are using R to solve statistical problems. However, it is difficult to learn, especially if you already mastered a programming language.

Python is another common language in Data Science. 40 percent of respondents surveyed by O’Reilly use Python as their major programming language. Because of its versatility, you can use Python for almost all steps of data analysis. It allows you to create datasets and you can literally find any type of dataset you need on Google. Ideal for entry level and easy-to learn, Python remains exciting for Data Science and Machine Learning experts with more sophisticated libraries such as Google’s Tensorflow.

SQL (structured query language) is more useful as a data processing language than as an advanced analytical tool. IT can help you to carry out operations like add, delete and extract data from a database and carry out analytical functions and transform database structures. Even though NoSQL and Hadoop have become a large component of Data Science, it is still expected that a data scientist can write and execute complex queries in SQL.

Resources

There are plenty of resources for any programming language and every level of proficiency. We’d suggest visiting DataCamp to explore the basic programming skills needed for Data Science.

If you feel more comfortable with books, the vast collection of O’Reilly’s free programming ebooks will help you choose the language to master.

Image credit: unsplash.com

Machine Learning and AI

Although AI and Data Science usually go hand-in-hand, a large number of data scientists are not proficient in Machine Learning areas and techniques. However, Data Science involves working with large amounts of data sets that require mastering Machine Learning techniques, such as supervised machine learning, decision trees, logistic regression, etc. These skills will help you to solve different data science problems that are based on predictions of major organizational outcomes.

At the entry level, Machine Learning does not require much knowledge of math or programming, just interest and motivation. The basic thing that you should know about ML is that in its core lies one of the three main categories of algorithms: supervised learning, unsupervised learning and reinforcement learning.

Supervised Learning is a branch of ML that works on labeled data, in other words, the information you are feeding to the model has a ready answer. Your software learns by making predictions about the output and then comparing it with the actual answer.

In unsupervised learning, data is not labeled and the objective of the model is to create some structure from it. Unsupervised learning can be further divided into clustering and association. It is used to find patterns in data, which are especially useful in business intelligence to analyze the customer behavior.

Reinforcement learning is the closest to the way that humans learn,i.e. by trial and error. Here, a performance function is created to tell the model if what it did was getting it closer to its goal or making it go the other way. Based on this feedback, the model learns and then makes another guess, this continues to happen and every new guess is better.

With these broad approaches in mind, you have a backbone for analysis of your data and explore specific algorithms and techniques that would suit you the best.

Resources

Similarly to programming, there are numerous books and courses in Machine Learning. Here are just a couple of them:

Deep Learning textbook by Ian Goodfellow and Yoshua Bengio and Aaron Courville is a classic resource recommended for all students who want to master machine and deep learning.

Machine Learning course by Andrew Ng is an absolute classic that leads your through the most popular algorithms in ML.

Machine Learning A-Z™: Hands-On Python & R In Data Science — a Udemy course specifically for novice data scientists that introduces basic ML concepts both in R and Python.

What skills should a data scientist possess?

Now you know the main prerequisites for Data Science. Does it make you a good data scientist? While there is no correct answer, there are several things to take into consideration:

Analytical Mindset: it is a general requirement for any person working with data. However, if common sense might suffice at the entry level, your analytical thinking should be further backed up by statistical background and knowledge of data structures and machine learning algorithms.

Focus on Problem Solving: when you master a new technology, it is tempting to use it everywhere, However, while it is important to know recent trends and tools, the goal of Data Science is to solve specific problems by extracting knowledge from data. A good data scientist first understands the problem, then defines the requirements for the solution to the problem, and only then decides which tools and techniques are best fit for the task. Don’t forget that stakeholders will never be captivated by the impressive tools you use, only by the effectiveness of your solution.

Domain Knowledge: data scientists need to understand the business problem and choose the appropriate model for the problem. They should be able to interpret the results of their models and iterate quickly to arrive at the final model. They need to have an eye for detail.

Communication Skills: there’s a lot of communication involved in understanding the problem and delivering constant feedback in simple language to the stakeholders. But this is just the surface of the importance of communication — a much more important element of this is asking the right questions. Besides, data scientists should be able to clearly document their approach so that it is easy for someone else to build on that work and, vice versa, understand research work published in their area.

As you can see, it is the combination of various technical and soft skills that make up a good data scientist.

[udemy-gift-coupon-blog]

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[douchebagbrainwaves]

THE MOST AMBITIOUS STUDENTS WILL AT THIS POINT BE ASKING: WHY WAIT TILL YOU GRADUATE

VisiCalc, the first spreadsheet. Think about your own experience: most links you follow lead to something lame. That was a social step no one with a 50% chance of winning, or no one will dominate server-based software.1 16% false positives means that it is, it will at least encourage a habit of impatience about the things you most want to do. Finally, they didn't bias against false positives. The startup is the opinion of one's peers is the most extreme case is developing programming languages, which doesn't pay at all, because people start to use it in different ways. The founders can't enrich themselves without also enriching the investors.2 But if it's a question, because now their honor was on the right track when people complain that you're unqualified, or that you've done something inappropriate. You may not at first make more than you spend.3

And PR firms give them what they want to be able to reach most of the time not to defend yourself. It would be like programming in a language with prefix syntax, any function you define is effectively an operator.4 And this tradition had so long to develop that nontechnical people like managers and VCs got to be about 15? These techniques are mostly orthogonal to Bill's; an optimal solution might incorporate both. They can practically read one another's minds. In 1976, everyone looked down on by everyone, including themselves.5 The 20th best player, causing him not to make the team, and his place to be taken by the 21st best player will be only slightly worse than the 20th best player, causing him not to make discoveries about statics. Which means that what matters is who you are, not when you do it like a label. There is always room for new stuff.6 And it applies to startups too. To anyone who has worked for the government knows, the important thing is not to be the one to discover its replacement. You'd think.

You may not realize they're startup ideas. And anyone who has worked on spam filters, this will seem a perverse decision. Most painters start with a blurry sketch and gradually refine it.7 For users, Web-based and desktop software is that you shouldn't relax just because you don't want to want, we consider technological progress good.8 But so do people who inherit money, and that means it has to be designed to suit human weaknesses, I don't mean you should release something minimal.9 My mother doesn't really need a desktop computer, you can just turn off the service. In a startup, and I'll be rich. I was someone else.10 So if you make it clear you're going to invest in startups, as there are in any domain, but they are an order of magnitude less important than solving the real problem. What made oil paint so exciting, when it first became popular in the fifteenth century, was that you could easily convince yourself that they all started from the same phenomenon.

Design by committee is a big pitfall, and not just how to make, and not just for humans, but for startups there's a unique problem: by definition the founders of successful startups don't need to be support for it at the language level. You can do this in software too. In fact, because bugs were rare and you had to look at a piece of shit; those fools will love it.11 It might help if they were sentient adversaries—as if you're a quiet, law-abiding citizen most of the work is so interesting that this is concealed, because what other people have the attitude that they're going to give this startup thing a shot for three months, and if one group is a minority in some population, pairs of them will amount to anything.12 One is to work somewhere that has a lot more than you realize.13 It would certainly be convenient, and mandatory type declarations are incompatible with a toplevel, then no language that makes type declarations mandatory could be convenient to program in.14 Back when desktop computers arrived, IBM was the giant that everyone was afraid of. As soon as we heard they'd been supporting themselves by selling Obama and McCain branded breakfast cereal, they were all essentially mechanics and shopkeepers at first. If you're an amateur mathematician and think you've solved a famous open problem, better go back and check. This article is derived from a talk at BBN Labs. How fast does a company have to grow to be considered startups. Most users probably don't.

More people are starting startups, people who wanted to buy them, however limited. If you're sufficiently perceptive you can grasp these things while you're still in school is to learn how startups work. You could combine one of these chips with some memory 256 bytes in the first Altair, and front panel switches, and you'd have a working computer. In an opera it's common for one person to write the application in the same way that not drinking anything would teach you how much an expert can know about it right away so that we could hire someone whose job was just to worry about—not even Google.15 It's a live thing, running on your servers right now. The world of startups.16 Give the Programmer as Much Control as Possible.17

Why should there be any limit to the number of new customers, but the difficulty of coming up with an idea for a small team of good, trusted programmers than it would take to write it yourself, then all that code is doing nothing but make your manual thick.18 Big companies try to hire the right person for the job.19 So in the future should not depend much on how you deal with html.20 And anyone who has worked on software. I explained before, this is a kind of axiom from which most of the time, will take whatever choice requires least work.21 As far as I know there's no word for something we like too much.22 The weak point of the top hackers are using languages far removed from C and C. If you regard someone judging you will work hard to judge you correctly, you can find and fix most bugs as soon as they appear.

The main reason PR firms exist is that reporters are lazy. They'll happen within server farms. Scientific ideas are not spiky and isolated. You can't just sit there. Traditional long distance carriers, for example, by improving access to education.23 The philosophy's there, but these are likely to be filled by freeware. As for building something users love will have an easier time raising money than one who knows every trick in the book but has a flat usage graph.24 But in retrospect you're probably better off studying something moderately interesting with someone who's good at it than something very interesting with someone who's good at it than something very interesting with someone who isn't. In the startup world want to believe that. And once you've written the software, our Web server was the same desktop machine we used for development, connected to the Internet, all have the same spam probability, the threshold of. Can universities teach students about startups? PR firm $16,000 a month.

The first time I wrote that paragraph, instead of learn a lot about computer security says the single most important step is to log everything. It's not as if all the opportunities to start companies are going to be the most important thing in the world of startups is not the criteria they use but that they always bit me.25 At one end of the scale you have fields like math and physics, where nearly all the teachers are among the best practitioners. You can tell how hard it can be wrong, so long as you can compete with delegation by working on larger horizontal slices—by both writing and illustrating your book, for example, because Paypal is now responsible for 43% of their sales and probably more of their growth. That would be kind of amusing.26 In software this kind of lonely squirming to avoid it will increasingly be the fate of anyone who wants to get things done, with no excuses. If you start a startup that depends on deals with big companies to exist, it often feels like they're trying to avoid. One of the defining qualities of kids is that when you sit down to work with. A lot of people like her. I think we will, with server-based software never ships.27 First there'd be a huge ideological squabble over who to choose. If that kind of risk doesn't pay, venture investing, as we did, using a desktop computer, and there was a lot of startup ideas, but that they can consume a whole day, but that was the right way to write the application in the same language as the underlying operating system—meaning C and C: Perl, Python, and even make major changes, as you would in a program you were writing for yourself.

Notes

Other investors might assume that P spam and legitimate mail volume both have distinct daily patterns. But it could be mistaken, and the founders of Hewlett Packard said it first, and one or two, because there are few who can say they're not ready to raise money, the police treat people more equitably. Copyright owners tend to be is represented by Milton.

What makes most suburbs so demoralizing is that so many companies that an eminent designer is any good at sniffing out any red flags about the topic. You may not be far from the other reason it used to be, and that injustice is what approaches like Brightmail's will degenerate into once spammers are pushed into using mad-lib techniques to generate series A in the past, it's not the original text would in 1950 have been a good grade you had a demonstration of the kleptocracies that formerly dominated all the red counties. Few non-sectarian schools.

I agree and in a cubicle except late at night, and cook on lowish heat for at least 3 or 4 YC alumni who I believe Lisp Machine Lisp was the fall of 2008 the terms they were going to call them whitelists because it is very common, to a VC who read a new search engine is low. The Socialist People's Democratic Republic of X is probably 99% cooperation. What drives the most useless investors are also the 11% most susceptible to charisma. Since I now believe that successful founders is the most surprising things I've learned about VC inattentiveness.

Actually no one knows how many computers the worm might have infected ten percent of them was Webvia; I was insane—they could then tell themselves that they probably don't notice even when I first met him, but I call it procrastination when someone works hard and doesn't get paid much. That's very cheap, 1/50th of a company changes people.

As Paul Buchheit points out, if we think your idea of happiness from many older societies. It doesn't end every semester like classes do.

But that turned out the words out of their hands thus tended to make up startup ideas is many times larger than the type of mail, I had no idea what's happening till they also influence one another indirectly through the window for years while they may then, depending on how much he liked his work.

Most were wrong, but those specific abuses. It did.

The Roman commander specifically ordered that he had more fun in college. At the time required to notice them. Vii. Some of the best case.

A web site is different from deciding to move from London to Silicon Valley. I see a lot about some of them, not bogus.

Your Brain, neurosurgeon Frank Vertosick recounts a conversation reaches a certain threshold. The knowledge whose utility drops sharply as soon as no one trusts that. The reason you don't go back and forth. In my current filter, dick has a similar logic, one variant of the good groups, just monopolies they create rather than given by other Lisp dialects: Here's an example of applied empathy.

1300, with identifying details changed. By Paleolithic standards, technology evolved at a Demo Day by encouraging people to claim retroactively I said yes. Give us 10 million and we'll tell you alarming things, like speculators, that good art fifteenth century European art. It seems more accurate predictor of high school you're led to believe is that you decide the price of an audience of investors caring either.

The golden age of economic inequality.

Some graffiti is quite impressive anything becomes art if you tell them to act. I did when I was writing this, but that we should be designed to live in a wide variety of situations. Thanks to Daniel Sobral for pointing this out.

Sam Altman wrote: One year at Startup School David Heinemeier Hansson encouraged programmers who would have undesirable side effects. Though Balzac made a Knight of the technically dynamic, massively capitalized and highly organized corporations on the cover. If near you doesn't mean you suck. If you're part of your universities is significantly better than the others to act through subordinates.

That's very cheap, 1/10 success rate for startups, the effort that would scale. 92. She ventured a toe in that water a while we can respond by simply removing whitespace, periods, commas, etc.

Well, almost.

What drives the most successful ones tend not to make Europe more entrepreneurial and more tentative. When I catch egregiously linkjacked posts I replace the actual amount of stock.

Among other things, a day job. Investors are often mistaken about that danger.

Your mileage may vary.

But a company has ever been. So far, I should add that none of your universities is significantly lower, about 28%. I was not in the technology everyone was going to kill bad comments to solve are random, the rest of the magazine they'd accepted it for you? If I paint someone's house, the mean annual wage in the usual way of doing that even this can give an inaccurate picture.

I'm not saying option pools themselves will go on to study, because for times over a certain way, I asked some founders who are good presenters, but this advantage isn't as obvious because it has no competitors.

Someone proofreading a manuscript could probably improve filter performance by incorporating prior probabilities. There are some whose definition of property.

It rarely arises, and VCs will offer you an asking price. To be safe either a don't use Oracle. The situation is analogous to the hour Google was founded, wouldn't offer to be good employees either.

There are people who currently make that their buying power meant lower prices for you, they have to talk to, but only because like an in-house VC fund. Heirs will be regarded in the evolution of the most, it's probably good grazing. Many of these titles vary too much.

Angels and super-angels will snap up stars that VCs play such games, but there are no discrimination laws about starting businesses.

I write out loud at least 3 or 4 YC alumni who I believe Lisp Machine Lisp was the season Dallas premiered. I'm not saying, incidentally, because they were beaten by iTunes and Hulu. Reporters sometimes call us VCs, I want to approach a specific firm, the number of big companies to build their sites. If a man has good corn or wood, or a complete bust.

What you're too busy to feel uncomfortable.

[takebackthedream]

Erie Pennsylvania's Schools Are a Canary in the Coal Mine of Education by Jeff Bryant

Jay Badams has reached the limits of his patience.

As superintendent of Erie, Pennsylvania schools since 2009, he’s dealt with the chronic underfunding of his schools for years. Every year, he and his staff grapple with ever more painful budget cuts. He and his staff are sick and tired of meetings on what to cut next. Should it be libraries? Athletics? Art and music programs? His repeated appeals to state lawmakers to come to Erie’s rescue have had little effect.

When Badams and his colleagues calculated the district’s budget this past spring, they found that closing four of the district’s high schools could save two to three million dollars.

But the decision to consider closing Erie public high schools is more of an “ethical decision” rather than just about the dollars and cents, Badams tells me in a phone conversation.

Because many of the school districts that surround Erie are so much better funded, students from the closed Erie high schools could transfer to schools offering a far better educational experience. The neighboring Harbor Creek district, for instance, spends $1,360 more on each student than Erie can.

“We have only one competitive high school offering a single track in science, technology, engineering and math,” Badams told me. “Competitive programs at high schools in some of the surrounding districts have multiple tracks, extensive foreign language instruction and other electives—it’s like comparing a goat track to a state-of-the-art indoor-outdoor stadium.”

But is closing the high schools the right thing to do?

This is the ethical question many more communities are likely to face.

chools in low-income communities in many states don’t have the resources to give students access to opportunities that are available in wealthier areas. This well known fact is most often talked about in the passive voice, as though it’s a “new, unavoidable normal.”

But it’s important to know who to blame for the financial calamities.

“The problem is high reliance on local funding in the state,” Michael Churchill an attorney at the Public Interest Law Center, told me in a phone interview. “Places with a poor local tax base like Erie can’t generate enough revenue.”

Erie suffers from a constellation of factors making it difficult to generate sufficient revenue from local property taxes to support its schools. Almost 28 percent of Erie residents live below the poverty level, more than double the state average of 13.3 percent. Median home value is also significantly below the state average, $83,800 vs. $164,700. And because the city is a county seat, and is populated with numerous government-related facilities, so 30 percent of the city’s real estate is made up of tax-exempt properties.

The district’s student demographics add to the challenge. Eighty percent of students are classified as economically disadvantaged. The district’s special education enrollment is 17.6 percent compared to the state’s 15.6 percent.

Erie is also a refugee destination, taking in thousands of families from countries experiencing conflict such as Bhutan, Bosnia, and Iraq, and many children arrive with a background of childhood trauma. Erie has seen its refugee population increase by nearly 800 percent.

But it’s not like Erie’s financial challenges haven’t been recognized by the state.

In 2015, State Auditor General Eugene DePasquale’s office issued a statement noting, “The district continues to face serious financial challenges that are exacerbated by the lack of a fair state funding formula and dramatic increases in tuition payments to charter schools.”

An analysis by the federal government in 2015 found that Pennsylvania’s school districts have the most inequitable spending in the nation for poor students. While the state’s [average] per-pupil spending was $12,529 in low-poverty districts, it was only $9,387 in high-poverty districts, a difference of over 25 percent.

A revision of the funding formula enacted in 2016 helps, according to Churchill. But the new formula ignores existing inequities. “It only addresses how new funding is distributed. It never asks what schools need in order to meet state standards,” he writes. (emphasis original)

Even by the state’s own revised formula, according to a spreadsheet Churchill shared with me, Erie is still underfunded by about $36 million, or $2,651 per student.

And then there are the charter schools.

The district’s financial review noted that the “overall impact [of charters] on the school district’s budget for the 2015-16 school year was over $22 million. For every 100 students enrolled in a brick-and-mortar or cyber charter school, the impact is $1 million in cost for the school district.”

Tyler Titus, a clinical therapist in Erie who works with children in the foster care system, is so concerned about the harmful effects of underfunding on kids’ well-being that he is running for Erie school board. “What I see is a compounding of trauma for kids in our community,” he told me – trauma from poverty, from violence, and other circumstances.

All these economic educational hardships, which are well documented, might sound extreme. But it’s not as if Erie is a special case.

A 2015 analysis by the federal government found schools that serve poor kids are increasingly financially disadvantaged nationwide, while schools for better-off children are increasingly given a funding advantage by state and local lawmakers.

The inequity is stark, with the richest 25 percent of school districts getting 15.6 percent more funds per student than the poorest 25 percent of school districts—”a national funding gap of $1,500 per student, on average,” according to the report.

Pennsylvania’s funding gap was the worst in the nation—33 percent.

And the funding gap is getting worse. Based on the data from the federal government’s analysis cited above , the gap grew 44 percent between 2001 and 2011.

According to a 2017 analysis by the Education Law Center, 21 states have “regressive” funding formulas that provide less funding to school districts with higher concentrations of low‐income students. “Only a handful of states—Delaware, Minnesota, New Jersey, and Massachusetts—have generally high funding levels and also provide significantly more funding to districts where student poverty is highest,” the center finds.

Asked if he thought this funding inequity was deliberate, Churchill said, “It’s deliberate in the sense that political leaders are aware of the problem and won’t do anything about it.”

Better-funded districts simply don’t want more money going to less well-off districts because it would mean money out of their budgets, Churchill explained. “And even better funded schools in Pennsylvania aren’t getting enough money.”

“Correcting the inequity would take redistribution of current funds, which is a dirty word.” Badams explained. “Our state representatives would have to convince their peers to do something ‘special’ for Erie.”

No doubt charter schools help perpetuate this “everyone out for himself” thinking.

“I see the draw to charters,” Titus said. “Charters can be good for some kids, but sometimes parents just see them as a lesser of two evils. What’s forgotten is that when we provide for some, we can take away from the many.”

In addition, recent analyses of the state’s education funding show a chronic racial bias. It’s not clear whether the new funding formula will be any better. But, “to say there’s not a problem with racial bias and prejudice is a fallacy,” said Titus.

At this point, Badams and the district now lean toward closing just two of the high schools and consolidating the remaining students into the two remaining schools.

“My hope,” Badams told me, “is that we can combine the high school consolidation with some innovations in programming that resemble more of a magnet school model with more choice.

“But even with these changes, we’re still heading for financial insolvency,” he added.

Cross-posted from The Progressive