[Up Next] Often times you feel like you want to go home
you want to snuggle up with a beer and netflix. You want to have
the world roll off your shoulders and you are a professional making somewhere
between $50 to 80,000 a year you will feel well
enough adjusted.
If you're not a professional
then you're a student who has worked hours and hours dozens of hours this
week cramming for midterms maybe working on your side hustle
maybe working on your internship or part-time job you just don't feel like
you have the time it takes. Wrong-o, wrong-o, wrong-o.
How's it going everyone my name is Andrew welcome to DataLeap. This is a
channel where i am talking to students academics and young professionals tired
of their industry and wanting to move to a
place that pays well has good job security and is on the
cutting edge of technology data science my job here is to make sure
that you are on the right track to get a
six-figure salary within the next six months that means within this year
you could be riding pretty with a data science job and today we are
going to talk about with no time to lose the rookie mistakes that a data
scientist in the making makes when they first start out training
to become a data scientist now this comes deep from the heart
because just this week i have started talking to
four yes four mentees that are coming from all different walks of life
coming from canada come from america and coming from the bottom of their
hearts of wanting to become a data scientist and that's one of the
things that i've started to do mentorship coaching if you are curious
about how i can implement an eight-week plan for you and coach you along the way
to get you a data science career set up a calendly invite with me down in
the description let's talk it through maybe we'll get
some free interview questions out of the way
just for you now the couple of things that i want to talk
to you guys about first is why do you think people want to switch
from data analytics from business consulting from
another young professional role that handles a lot of excel and powerpoint
and non-stop meetings into the exciting world of data science
well data science involves solving big problems just like
a software engineer does but it involves you not having to sit
down with code all day it involves you sitting down on a
whiteboard with a colleague sitting down with product teams
sitting down with marketers sitting down with data engineering and
of course software engineering teams it involves you covering a wide swath
of a technical company and understanding the product from inside and out a lot of
fang companies in silicon valley treat their data scientists as a very business
facing role and that's something that you can easily pivot to
if you are a management consultant if you're an investment banker if you're
someone who is in the professional industry but is
tired of spending their life and blood in a nine to five
sitting in a cubicle and just praying that this next meeting won't take the
full hour and a half let me tell you how you
are making grave mistakes of starting your data
science journey number one the first big mistake that
people have when they start their data science journey
is settling for anything less than their goal
a lot of people are telling me hey if i can just get a data analyst position
out of graduation or maybe if i pivot from this business consultant roll into
a data analyst position then i'll be that much closer and i'll
just make it next year right no wrong stop don't do that if you
are settling for a data analyst role and that's not your final destination you
are going to have a really hard time pivoting and transitioning that's
because data analysts spend a lot of their time in meetings
constantly working on an ad hoc basis an analyst is not
usually writing scalable infrastructure or dashboards that report to someone's
direct email therefore someone has to ask you hey where's that
dashboard of x y z or hey can you investigate this dip with your own
manual sql query you see none of these actionable items are something that you
can 80 20 your time into you're probably
spending a lot of your time working directly with the data itself
cleaning it visualizing it aggregating it combining it and then
finding out where the next piece of combining and aggregating and
visualizing it comes from you see that's a lot of
data investigation data momming let's say and that means
not only are you going to be caught between the anvil and the hammer where
the anvil is the dozens of meetings you'll have per
week and the hammer is the non-stop dirty data that is crushing you into the
nine-to-five workplace you'll be just back to where
you started with maybe a considerable rise in your salary
and a management consultant is never going to make anywhere close to
the six-figure salary a data analyst can a base starting salary for certain data
analysts in silicon valley is well above a hundred thousand in u.s dollars
but of course a data scientist is going to
see even another 20 to 40 percent increase
on their base salary and don't even get me started on total compensation
so if you are gunning for that six figure beautiful
plump luscious data science salary and job security and cutting edge research
opportunities don't stop short of your goal that's
number one now i'm only human so even Andrew from DataLeap needs to tell you
his advice from his set of experiences so
if you have a differing sense of what you think you should do
to start your data science journey i want you to go ahead into the comments
and type it out now i'll give it a second actually
not too long because i want you to go ahead and while you're down there click
the like button click the subscribe button make sure that you are not
missing out on any of this midi detail on how you can
make six figures this year as a data scientist well now
back to the list number two the second biggest
rookie mistake that i notice is that people are spending a ton of
their time wrapping their minds around these complex machine learning
algorithms why they want to become a machine learning
engineer or a research scientist ease up because those positions easily
need you to have a master's degree a phd or
something more advanced maybe six years in industry of learning research now if
you're already in that position great and i'm gonna set up a
entirely different video series for you but if you are pivoting
which is the point of this video if you are pivoting from a specifically
professional industry then you're going to have a rough time
trying to map your current experiences and accomplishments to that very
academic theoretical space so if you want to
use your time wisely i recommend you pick up a pen
cross out the machine learning on your to-do list and then focus your efforts
on something else i often times have people approach me
when it comes to mentorships when it comes to coaching they come to me and
they say hey i have the best understanding of a
support vector machine i know exactly how a neural network works if you ask me
anything about resnets and i say great can you solve this sql question do
you know the difference between a group by
and having do you know why these two tables need to be joined
by a left join these are actually more likely the questions you're going to be
solving on a technical interview which is why if you
have a machine learning background already
great but it should not be your top priority when trying to make the best
use of your time number three the third biggest rookie mistake i see
is everyone stressing over what language to pick
or which ide to get familiar with hey it turns out that there are only
so many choices that you can make and there really aren't
that many wrong choices for ids i suggest
something free and easy like adam or pycharm or if you want to pay a couple
bucks there are great premium products like intellij from jetbrains
now i will never ever doubt a data scientist
for only working exclusively with anaconda
if you work with jupiter notebooks only or jupiter lab like i prefer
that is a perfectly valid data scientist background now there is a trend
moving from scripting in notebooks and towards
scripts themselves it's more modular it's more scalable and it
keeps your code way easier to maintain hey i will make an entire video about
that in the future but for now if you are
curious about how to get started with your first 21 days of data please go
check out the 21 days of data zero dollar data science bible
that i made with collaboration with data professor
that just came out last week i'm still giving away
200 resumes i've given about 150 away you can have the last 50
if you follow the instructions on that video and i'll wait for a sec
while you get back but make sure to come back here
you hear boy this entire world is like sepia tone right now
if you're curious about what programming language to use like python or
r or c plus or javascript it's python learn python python is an industry
accepted language and almost every company will be asking you
interview questions in python it is an easy question to code with but
also to answer interview questions with because of its readability and
simplicity but also it is widely industry accepted
number four the fourth biggest way that people have made rookie mistakes
is not starting sql training today sql is a big part of any data
scientist's journey just ask john singer sql is invaluable
not only for the technical interview but also on your day-to-day being able to
explain to data engineering your partners in
data crime that sounds criminal your partners in fighting
data crime they will be very happy that you are extremely
well-versed in sql and of course if you are perhaps one of the only data
scientists on board on a smaller company you will probably need to handle all the
sql yourself so get started with lead code get
started on youtube with a bunch of different youtubers
covering solutions i actually have three solutions that i'll link down below
it is an easy way to get your five to 30 minutes of
python sql and data science learning out of the way
number five the fifth biggest rookie mistake i see people make
is not knowing what project to work on hey
if you are trying to build an interesting project as a student
because you want to pad your lack of experience perhaps you have a couple of
great internships perhaps you're coming out with
no internships and you want to make sure that this
next big project personal projects becomes your
major selling point when you are either switching majors halfway through college
hey i understand i get you if you are in
that position make sure that you are scraping your own
data that differentiates you a ton there are a ton of different
tutorials with beautiful soup and selenium
helping you go from zero to hero helping you scrape
any website for any kind of data you're looking for you can scrape the yc
hack news for an aggregation of technical news
you could scrape ticker websites for the nasdaq you could scrape glass store
for data scientist salaries for example these are all
great ways to show off your custom skills and then you won't be playing
around with data sets that recruiters have seen
over and over again the iris data the titanic data set the
taxicab data set and get a professor the penguin data set
although i do love mia penguin
sixth i don't have enough fingers
the sixth and final rookie mistake that is preventing you from becoming a
six-figure data scientist this year within six months is
not starting today oftentimes you feel like you want to go home
you want to snuggle up with a beer and netflix you want to have
the world roll off your shoulders and you are a professional making somewhere
between 50 to 80 000 a year you will feel well
enough adjusted if you are not a professional
then you're a student who has worked hours and hours dozens of hours this
week cramming for midterms maybe working on your side hustle
maybe working on your internship or part-time job you just don't feel like
you have the time it takes rongo rongo rongo
you do have the time it takes if you only have five minutes if you only have
five hours split among an entire week you
have the ability to start today you need to push yourself
to make sure you are on the right track and
having a community helps with that that's why the 66 days of challenge that
ken g has created is a really good way to
keep yourself accountable but even if you don't have someone poking
you in the back let me do it comment down below and say hey andrew i
need you to keep track of what i'm doing and then
i'm going to post my videos pretty regularly keep checking in then i will
be your alarm clock to make sure that you
are learning and if you don't know what to learn once
again my zero dollar data science bible is the first 21 days that you
should be working on i'll link to that video down below
if i haven't already linked it up right here finally
if this video has been helpful to you at all i would really appreciate a like
i would appreciate if you just gently press
that like button make sure to tell your friends
that Andrew from DataLeap sent you and you are on your way to becoming a six
figure data scientist this year and keep on the lookout for
more amazing content especially a particular collab
with a particularly amazing data scientist that i
adore later on this week thank you for staying all the way and i think we have
some time to read some comments that made my heart
smile so if you have time for that stick around and if not
i will see you guys next time peace kenji said
kenji of 66 days of data he commented on one of my videos
data professor replied andrew thanks for having me i really had a
fun i'm why can't i talk andrew data professor andrew data professor
said andrew thanks for having me i really had fun
filming this collab video thank you so much for making this
clapton come true it was honestly him pulling the heavyweights
here rc conception rc concepcion says love the energy humor
and how you explained meatball you want to be a part of the
video noel rubio said we stan a data security king a that's the person
that you're talking to here call me your royal dada
calvin pinto says your voice and accent sound so professional as if you have a
million subs hope to see that quickly become a
reality calvin we are putting the wagon in front of the
horse but honestly let's make rocket power horses
you and me together thank you calvin for leaving that comment
two bros said perfectly timed corgi break
let's have more corgi breaks don't you agree let me know in the comments below
if you enjoyed the corgi break in this video now i'm gonna
have to go back and put a corgi break but that's for future andrew to figure
out
i'm out of time i gotta go goodbye
what are you talking about
you