[Joma] Hey guys welcome to another episode of Joma-- oh god damn
guys welcome [Andrew] Hey guys welcome to another episode of
[Cough COUGH] Andrew mo money
hi my name is Andrew Mo it's great to see you guys it's really really a pleasure to make your
acquaintance this is professor meatball he is a phd dog turret from the university of corgell and
today we're going to talk about how accurate is joma's video five tips for getting a data
science job back in march of 2018 to today the 2020 and 2021 especially post pandemic
currently in the pandemic landscape my goal of this video is so that you can answer questions
like are these skills still applicable in the year 2020 and 2021 can i trust joma for his joma class
which is still running i'm not affiliated with any way but you know he is trying to create resources
for you as a data scientist and how do i smash the like button and the subscribe button if i'm new so
that we can defeat the algorithm together if you hit the like button right now i'm gonna show you
a picture of a picture of meatballs so darn cute are you ready it's gonna it's gonna be right here
right there as soon as you click that button thank you oh you didn't do it yet okay there
you go without further ado let's jump into what i think about this video from jomatech
i had to remove my day in a life video uh you know the one that got me most of my views i had
to take it down because i violated some policies so joma also known as jonathan jamias tekkerson
he is a data scientist or was a data scientist working for facebook you can tell he is still
filming on technically on facebook's campus it is just directly outside his campus on the trail
and one of the things that he started working on recently is education so he's breaking into
the space of actually helping people become data scientists so that's admirable of him but today
we're going to be talking about something different we're going to be talking about
how to get a data science job at a you know a large company like friendster or myspace something
like that first of all what is data science see you can tell you can tell that this video is very
old because he's still talking about friendster and myspace those companies are like three years
old four or five really old for the dot-com era so that's still what it's called a lot of people have
you know misconceptions about data science so if you do i would suggest you to check
this video i made with um engineer truth that's funny engineered truth just recently sold his
channel last month he is now called well whoever bought his channel is now called uh honest careers
i think well whatever it's called uh that video might still be up i think it's still the best
performing video all right so let's get to it to the five tips on how to plan a data science job
number one tip do an internship and have a technical degree how i got my first hands-on
experience on data science was my internship and i did my internship in a large untitled company
he didn't have to censor himself with that first tip do an internship and have a technical degree
well i have to really agree with this latter half doing an internship is not easy to acquire
especially if you're coming from a place that you know you need experience to get more experience
john is going to talk about exactly how an internship provides you a springboard basically
a proof of work for how you can become a viable candidate for other companies because you have the
ability to say i i did this job correctly inside the landscape that essentially emulates what
i would do as a full-time employee so here are some examples of good degrees to have cs uh just
engineering in general like system engineering uh maybe even environmental engineering just anything
with the word engineering in it they would you know demon as technical and they will give you an
interview all right second tip it's not quite done with the first half of what he said yet he listed
a bunch of technical degrees now that's actually a very good idea being able to have a technical
degree puts you on a level playing field with other computer scientists and software engineering
majors one of the main ideas that he's putting out here is that if you have a software engineering or
equivalent major you are now someone who is able to put himself at the front of the line when it
comes to proof of work once again it's about proof of work now if you're curious about how to get
an internship it involves the same cycle of trying to become competitive through personal projects
especially if it's your first internship that you're trying to get you have to really try and
build out your portfolio of personal projects and i can send you down a couple of links for
playlists that kenji and data professor have created for their personal projects no matter
how fancy your you know python is and stuff like that in the end usually most companies set up
their infrastructure so that you query data using sql learning sql is really important learning sql
sql is a language that i believe you can learn in 24 hours and master in seven days that's the
initiative that i'm trying to build out from 60 seconds to data science it essentially means that
sql is the bread and butter of most data science interview cycles and if not probably one of the
only languages you'll use as an entry level data scientist for the couple first couple of months
usually most companies set up their infrastructure so that you query data using sql
why because it's an easy language it's not really because it's an easy language
it certainly is straightforward so that helps but the reason why sql is used all over the place
is because it's scales it's a structured query language you can use sql to query
a data set of just three or four columns in three or four rows but you can also use that same query
to query something that has terabytes of data this is usually something that older languages
even python have to consider when they're thinking about time complexity you can make
a quick and dirty hack that gets you something along the lines of what you're looking for just
as long as the input or the data doesn't scale and doesn't become humongous however sql isn't quite
the same it's the same language that is used in big data so you can imagine bigquery is actually
directly related to the simple sql functions that you're writing on a day-to-day basis as an
entry-level data scientist and then what you do is you have to write queries for that yeah i'm
not going to go through the answer but you should that would give you an idea of what kind of query
you should run tip number three learn about success metrics and tracking metrics oh that's
so important i actually really agree with joma in this sense in that uh the metrics that you follow
in terms of the entry level like product landscape is incredibly important if you don't have an idea
of what metrics your industry whatever job you're trying to get into you can just start breaking it
down as a core user the easiest way that you can think about it is take out your phone look
at which apps that you use most often and then think about what you do on that app from login to
scrolling to interacting to logging out from there you can start learning metrics like total session
time like how long do you actually scroll and then click out of the app how often do you have
impressions like photos from your friends or ads from an advertiser and you click through it the
impressions click-through rate and how often do you log in on a daily basis on a weekly basis
that is the daily active user rate and the weekly active user rate you see these are metrics that
you can immediately start thinking about through each application and e-commerce applications are
going to be different marketplace applications are going to be different learning about these metrics
is going to help you a ton in interviews it's important because when you become a data scientist
you will be the extrovert on your team on data this is really only true for
facebook this is that kombucha but you don't have to be an extrovert to be a data scientist i
think that's one of the most dangerous ideas that data scientists have to be this kind of go-getter
product manager type of person this is only one type of data scientist and that's the
main reason why data science is such a tricky term because it ultimately ends up being data science
is now a blanket term for four different kinds of jobs one that is closer to the analyst side
where you are making business inference for barely any statistics but mostly sql based visualization
based work and then the other two jobs which are actually plugging in the back end for the data
pipeline more of a data engineering job and then finally someone who is actually tuning the models
more of the machine learning engineer job so what are the tracking metrics so that would be for
example let's think about it um i want to know if they're actually watching a lot of you know videos
so tracking metrics can be watch time per active user and then or maybe just watch time in general
uh joma is right now going through a pretty lengthy rabbit hole of talking about exactly the
kind of success metrics and tracking metrics for this hypothetical i would say if you are curious
about this i will leave a link down below and you can also look into how different data scientists
have walk through interviews of entire metrics i think that's going to be a little more helpful
if you want to be in the technical sense but the way that i practically laid out for you
is a behavioral hack if you can go into apps that you've already used and then imagine yourself as
a core user and then just think from the point of view of the salesman of the app you know
the product manager is essentially the owner of the lemonade stand for a specific feature
if someone is specifically taking care of the youtube like button then all he really cares about
is how often you use it how uh at what rate do you use it for what videos do you use and how similar
are you to other users who also use that feature if you can keep those three things in mind i think
you have a good sense for most metrics and where they can come from and then the next question
after asking like how would you you know define your success metrics it would be how do you test
it okay so here's one thing you need to know you need to know about a b tests you need to
know about control groups and experimental groups and uh a b testing is going to be a big kicker
for most people in interviews and i'm going to give you like a 30 second rundown right
now the main idea behind a b test is that you have to understand that what you're trying to see is a
difference between one group and another that isn't due to random chance that's it if i take
two groups of chickens from the exact same chicken pen where are they from chicken land
if i take two groups of chickens from the exact same chicken land and i try to measure their
differences they're going to be different at least in a small bit because of random chance so if you
want to conduct an a b test and we hypothesize that there is a real difference between these
two groups because of something we're doing changing a feature flipping something on
flipping something off then we want to be able to make that rigorous test between these two groups
to pass interviews about a b test you have to ask three questions one you have to ask about sampling
how are you going to sample are you going to use stratified clustered sampling are you going to
be careful how you sample from demographics such as age and gender but also from rural
versus non-rural communities users of a specific political leaning users with a specific purchase
history you have to think about exactly how you are divvying up the sampling process two you have
to ask a question about the network effects inherent in this process if a facebook like
entity tries to flip on a feature for a specific group of isolated individuals people are going to
know so what is the risk of running an experiment too long for network effects to start interacting
with your isolated groups but run the experiment long enough so that the novelty effect isn't the
only thing that you're measuring and that in 10 seconds is just when people see a new feature
they are probably more likely to try to interact with it even though they might not want to
and three how significant are the results now this comes back to p values and confidence intervals
not including the null value but if that was all too complicated the main point is can we prove
that the results that we see are actually due to not random chance there's a lot of not knots
in there but remember that our chicken example if the two groups of chickens that we selected
from the same group of chickens have a small difference that should be due to random chance
and we would not be able to be confident that we have evidence that these two groups are actually
different and you need to visualize the data because you need to communicate them later
learning visualization skills is nice but it will almost never come up on an interview
so the only times i've ever had to prove my visualization skills was during a take home
and for that you can always just google so there you go all right number five you have to learn to
communicate really really well because the whole point of a data scientist is to communicate their
findings communication is incredibly important and once again i have to go back to what he said
about extroverts you do not have to seek joy from interacting with people to be a good communicator
to be a good communicator you have to empathize you have to come from a point where you are not
exactly sure what the other person is coming from yeah you have to be able to drop down
your technical knowledge to a baseline so that you can start making powerful analogies you can
start making clear concept bridging statements so that people can follow you and track wherever you
are going and make sure that your statements about statistical truth and rigor are not misconstrued
because that can be costly down the line for business stakeholders mainly talk good so people
don't make mistakes because your big brain all right so yeah so that's about it that's five tips
on how to land a data science shop at an untitled large company and yeah i hope
i hope that was useful it was great it was great it's amazing how little uh two years has actually
changed the data science landscape in terms of the skills necessary and that's because you know
data science has had a long and rigorous past and the most important three takeaways from this
video i hope you remember is one focus on skills that are going to help you pass the interview so
that you can get your very first internship or job so that you can get more experience because
you're going to learn 95 of what you need to know from your first job so don't learn extraneous
so don't learn extraneous skills like how to finely tune a hyper parameter for neural
networks or different models and don't learn extraneous skills like visualization software
you don't have to go buy a tableau license just so that you can learn it so you can say i know
visualization skills secondly make sure that you don't skimp the statistical stuff when it comes
to a b tests that's going to be asked it's going to be on the test and you're going to have to know
a little bit about how to communicate that to a five-year-old explain like on five statistical
concepts about a b test and then three make sure that you learn to communicate and if that
involves you starting a youtube channel talking to a camera you know explaining to rubber duck
right talking to a roommate or your best fluffy friend that can be really helpful because you
need to be able to learn how to open your mouth and start something sensible every single time i
hope that was helpful i hope that was great if you wanted me to interact with more youtubers online
or specifically just joma make sure to write a comment down below this is part of a 30-day
challenge i'm trying to upload every single day hector elizondo my roommate is helping out a ton
shout out to him he is really my biggest supporter i really want to thank professor meatball for
being patient with me and i want to thank you for supporting content just like this follow me
on instagram i really need a social validations check me out on tick tock i make short form
content there and for the sake of you go ahead and check out an audible book or get four free stocks
in the month of december for weeble as always but not for forever i'll see you guys next time peace
hey guys because your big brain