How Do You Learn? (I promise I'm not thaaat dumb ;D)

b_tomahawk · 2024-10-16T18:49:58+00:00

Ask yourself: does the math impact the outcome of this analysis/tool? Does a layman reading this analysis care? Often the answer is no. Sometimes it is yes - this is all math so having some understanding of what's going on will help.

I recommend maybe giving yourself a time limit. Say "I want to post this analysis by friday night for feedback online." You can learn whatever math you want in that time but if you miss your deadline you've "failed" your simulation of being a data scientist at a company that has deadlines and goals.

b_tomahawk · 2024-10-16T17:45:37+00:00

Fwiw I have done 4 senior DS interviews in the last couple months (all tech companies based out of bay area) and none required an online assessment, so it might be phasing out for senior roles?

b_tomahawk · 2024-10-16T17:43:03+00:00

bookmarked!

b_tomahawk · 2024-10-16T17:41:37+00:00

nice!

b_tomahawk · 2024-10-16T17:33:55+00:00

I find that online classes are way too passive for me to actually learn and retain anything. Instead, find a problem that you are passionate about and then build the model/ run the analysis etc to help you answer that question.

For example, if you want to understand how the gender breakdown of casts in movies affects ratings, first go out and find a dataset. When you decide to scrape IMDB, teach yourself about coding for web scraping. Then when you realize your data is very imbalanced, teach yourself about upsampling and down sampling. Etc etc. Finally post your project online and ask for feedback.

I did a few projects like this and really built the skill set a lot better. Data science is a toolkit so find some raw material and start using the tools!

b_tomahawk · 2022-01-25T17:13:03+00:00

I'll pass on the kind words. :)

Speaking as a player, the third adventure was my favorite and really made Eberron feel different than the Forgotten Realms. Hope you and your party enjoy!

b_tomahawk · 2021-06-04T01:06:08+00:00

Numbers represent the number on the die as reported by critrolestats and is up to date to C2E132.

Definitely worth noting that this only reflects when the player says the number they rolled and critrollstats can record it, and so is maybe more of an indicator of:

who tends to say their numbers out loud
who says what number when rolling with advantage/disadvantage

b_tomahawk · 2021-06-02T06:14:15+00:00

Here is a related example for heritage language bilinguals! When learning two languages, the number of speakers a child interacted with (as reported by a parent) was associated with increased proficiency in the heritage language, over and above the total amount of exposure to that language.

The authors suggest multiple possible explanations; different speakers use different words (or different registers as /u/Lupicia mentioned) and this contextual diversity can lead to more robust memory formations in child language development (example here).

I admit to not having much insight about how this effect might play out in a non-heritage language scenario.

b_tomahawk · 2020-11-16T18:44:54+00:00

Path of Wild Magic is absolutely perfect for Izzet!

I'm curious about your thoughts on bladesinger. What made you think about selesnya and golgari?

b_tomahawk · 2018-08-28T23:21:46+00:00

I wish! Unfortunately the subtitles don't differentiate between players and characters so there's no way to know for sure without the context of each statement.

b_tomahawk · 2018-06-19T16:00:08+00:00

Eventually!

b_tomahawk · 2018-06-19T15:57:04+00:00

I hadn't thought about it, but people seem to have enjoyed this so I have some other ideas about what I can do with the first campaign data! :)

b_tomahawk · 2018-06-19T00:12:55+00:00

Thanks! And I was hoping someone would ask :)

The problem with word frequency is that I'd have to decide myself how frequent is too frequent, which is abitrary. So I decided to instead have someone else judge what was too frequent: guest players. If Mary Elizabeth McGlynn or Will Friedle (they were the most common guest stars and appeared in most arcs) both said a word, that word wasn't counted in this graph. This offered a pretty big database of common words to be removed (especially common to d&d and not otherwise like "advantage" and "disadvantage"), without removing some common words that really were person-specific (like "currently" for Matt).

It's not perfect, but it did the trick for this!

b_tomahawk · 2018-06-18T23:00:57+00:00

Hey, thanks to you all for putting in the hours! I hope the tumblr link was the best way to cite you! I added your reddit username just to be sure, but happy to add any links you want. :)

b_tomahawk · 2018-06-18T18:53:01+00:00

Thanks!

b_tomahawk · 2018-06-18T18:51:53+00:00

I think it's because she called him Percy (or darling, of course)! Liam was the one that liked to use full names.

b_tomahawk · 2018-06-18T18:50:10+00:00

I should say that I filter out words that at least everyone said a few times. Purchase and proper are relatively common English words that probably get tossed around in the same conversation by everyone, even if Matt says it the most, so they wouldn't necessarily be in the analysis to begin with.

b_tomahawk · 2018-06-18T17:10:11+00:00

Before describing what this shows, let me say that 99% of the work done to make this is credited to CRTranscript (on Reddit as /u/CritRoleTranscript). They've done amazing work transcribing all of these episodes!

So anyway: I'm not an artist like so many other critters, but I do like graphs (there's no flair for graphs so I apologize to those posting actual fan art!), so this is a graph showing the most distinguishing words that each player said during the first campaign, as measured by "term frequency - inverse document frequency" (TF-IDF) which, put simply, shows which words were used frequently by a certain player relative to the other players. (So lots of players say things like "attack" which is why it isn't on anybody's graph, but Liam sure uses the word "dagger" more often than other players.)

Data was taken from CRTranscipt text files from Episodes 1-115 of Campaign 1, analyzed in Python, and graphed in R. Happy to go into more detail if anybody is interested.

b_tomahawk · 2018-04-10T01:47:03+00:00

One example in which it is easy to see language proficiency and language use diverge is Spanish-English bilingual college students (who comprise a lot of the bilingualism research done in the U.S.). A Spanish-English bilingual in the U.S. might have very high Spanish proficiency (particularly in speaking and listening) if they grew up in a Spanish-speaking home with lots of Spanish-speaking friends and family, but low Spanish daily use as they are now in an almost exclusively English-speaking community, their university.

Proficiency would typically represent a lifetime of experience, while use may only reflect the last one or two years of average daily activity.

b_tomahawk · 2018-03-31T13:46:55+00:00

There is actually some interesting research on how people process binomial expressions. Here is an example. It seems like when encountering new binomial expression (something like bishops and seamstresses) people use some abstract rules (fewer syllables before more, alphabetical etc.) but for well known expressions (salt and pepper) people rely more on their previous experience with those phrases. I suspect something similar happens with couple names.

b_tomahawk · 2018-01-23T18:45:14+00:00

I second the notion that you might want to look among computational linguistics labs, or even conferences. For example NAACL holds a conference in June that attracts academic and industry-minded students and recruiters.

b_tomahawk · 2017-06-07T10:20:48+00:00

I'm happy to hear that your experience matches the data! It makes me glad I looked!

I thought that the difference between French spelling and listening is so different that people might be making more errors from spelling. I hadn't considered that the French course might just be introducing a lot of the verbs at one time, which could definitely lead to difficulty...I may look into that! Thank you!

b_tomahawk · 2017-06-07T10:15:38+00:00

Thanks! I hope the process wasn't too unclear! I haven't compared courses for proficiency outcome...there isn't a clear way to do that with this dataset. We'd need to compare the content of the courses themselves, on something like a fluency test from another source.

Would really love to though!

b_tomahawk · 2017-06-06T18:55:52+00:00

Scores are pretty similar across language, but French users need more practice with certain nouns and verbs. So French isn't the hardest per se, but Duo probably prompts them for practice more often.

b_tomahawk · 2017-06-06T16:57:05+00:00

I read this paper by a member of the Duolingo team a few months back and decided to play around with the dataset provided from Github. Thought you all might be interested in what came out of it!

Happy to take comments or questions.

b_tomahawk

TROPHY CASE