Hello for Vancouver welcome everybody to
our master of data science webinar today. We're going to be talking about student
experience. My name is Tiffany Jeffers I'm an instructor here in the Department
of Statistics at UBC and I work primarily with the master data science
program. I teach in the program that I develop particular for it so this
webinar is going to run from 9:30 a.m. until 11 a.m. this morning and I'm going
to start off with some housekeeping just to get us oriented with how this its
network so this is our your opportunity to ask questions from two MDS alumni and
a current student and I will introduce our panel shortly the questions as I
mentioned earlier are going to be focused about the student experience so
we're going to talk about things from their experience in the program to tips
they might have for new incoming master students and what they did after
graduation in the case of our alumni we will have another webinar coming up to
talk about application procedures prerequisites and more technical aspects
of the master data science program and that is going to happen on February 27th
so keep paying attention to our Facebook page and we'll send a notification or
announcement there are details for that so for today's webinar many of you
registered before we started this morning and have already submitted
questions we're going to start with those questions but if you have
additional questions you would like to ask please feel free to put them in the
comment box and if we have time we will get to those after the questions that
were posted earlier okay so the next thing I'm going to do is introduce a
little bit about the master-maid science program at UBC then we'll talk about the
capstone project and then I will introduce you to our panel so the
professional masters of data science program here at UBC is a ten-month
program and you spend the first eight months working in intensive coursework
during 24 one credit courses these courses each have a lecture and hands-on
lab component and the content of the course is a thermal art from statistics
a third of them are from computer science and then about a third of them
are data science specific at the end of these eight months then you take what
you learned in those courses and apply it to a real world problem in a capstone
project this program is very intensive and fast-paced it will keep you busy and
so engaging in this program is really a full-time commitment
and the prerequisites again we'll talk about this later on February 27th but
just in brief we are that you meet an undergraduate course in statistics an
undergraduate course in computer programming and an undergraduate course
course in either calculus or linear algebra but we recommend a new outlook
so a little bit more about the capstone project first is that students are not
doing these individually we're going to set you up in teams to do this so that
you can build on strengths about each other and you're going to work on a
real-world problem from one of our partners and our partners come from
various statements so we have industry partners government partners
not-for-profit partners as well as academic partners and some examples of
current and past partners include Microsoft Unbounce
VC statistics the UBC Sauder School of Business and fin which is a financial
tech startup company ok so now we're going to move on to our panel
so I'm going to start on the far side over here while we're coming back to me
so listen it's Tristan and Oliver so I'm going to get each of you just one moment
for technical difficulties
oh okay I'm going to continue looks like technical difficulties have been solved
excellent I'm sorry gonna start at the far end and
I'm gonna ask person Oliver jits tell us a little bit your background before you
enter the program and then a little bit about what you're doing now sure right
so my name is Chris I'm currently working as a data scientist for a
company called T for either consulting company Canadian consulting company my
background before the data science program yeah I was sort of in the middle
of a career change going from classical pianist and piano teacher I think it's
pretty rare but I had spent a couple years doing
coursework looking at possibly statistics or actuarial science and so
in two years I gained some experience in computer science and statistics and
calculus and so when this program came up it looked really interesting and I
applied and really glad I did it yeah sure so I'm Olly before doing the
course I was a mechanical engineer so I did a master's in mechanical engineering
back in the UK and spent a year working in the automotive industry yeah and the
best decision I've I made really was to it was to do the course and make them
move sideways in today's science so and yeah following the course I'm now
working for fin AI which is a financial tech startup so I'm working on the data
science team building supervised learning systems for natural language
processing great thank you and then we'll move over to Jordan one of our
current students and Jordan can you tell us a little bit about your background
and where you are in the program now okay so I did my undergrad sorry I'm
Jordan I did my undergrad in biology but I actually worked as preschool teacher
and just in a coffee shop and did this stuff kind of on the side but at this
point in the program we've covered a lot actually the basics of supervised
learning right now we're in unsupervised learning we're looking at model and
feature selection and we're at doing some basic regression and
statistics it's all really interesting stuff that's really overlapping and very
cohesive great thank you very much okay so now we're going to jump to some of
the questions that were submitted to us before we started the webinar this
morning and I'm going to invite the panel to just jump in and answer the
ones that you can if somebody has answered a question but you feel like
you can add something or you would like to add something please go ahead and
again some of these my apply for the current student apply to the current
students more or the alumni so the first question that was asked was why you were
interested in data science and I think this is something you all can answer so
why don't we all take a turn that's very nice sure like I said I was in the
middle of a career change so I was actually looking at actuarial science I
really liked statistics and math solving puzzles and so I decided I was gonna go
into something related to this and it was kind of by fluke my neighbor told me
that this program was starting up and I had I think a week and a half to apply
for it and and so I looked into it for about a week and then I applied and and
I'm really really glad I did it's been fantastic great oh yeah I think very
very similar answer through throughout University and through engineering
mathematics was my strong point but I think the impact you can have as a
single engineer is is a lot less than you can have as a single data scientist
so that's what excited me about data science was to go in and build software
that that drives a big impact in the company so almost on your own yeah
excellent Georgia yeah so my undergrad in biology I like statistics but I
didn't know what part of biology actually really enjoyed and then in my
last term I took a computer science course and I didn't realize you could
put the two together and actually do something with it and then like you a
friend of mine sent me the link to this and because I did my undergrad at UBC so
I was like it's like coming home
awesome thank you okay this is actually another question I think that you all
could answer so the next question is are there any subjects content
specifically from the math computer science that you would have liked to had
more experience with before you started MVS personally like I said I was I was
actually looking at possibly taking my first doctoral exam so I was reviewing
pretty hard statistics the summer I applied and even before I knew that I
got in I was still looking at that so I think that helped me a lot I feel like
the statistics and probability would be something that lots of people lots of my
colleagues fellow students would probably say that be useful but I I felt
pretty good good yeah I think having a solid foundation in them things like
matrix multiplication and linear algebra it's not what a prerequisite like it was
definitely possible to pick it up as we went through the course but if you came
in with that you would some things would be a lot easier throughout the process
yeah great thank you yes and also I found two people who at least had some
of a computer program language they could translate it to learning Python
and art whereas if they didn't have any of that they thought that they couldn't
do it it just made it a bit more of a challenge hmm so definitely having some
sort of programming language on top of definitely the linear algebra definitely
okay so the messages algebra and a kind of programming language you know before
you start in September it was definitely possible I mean my programming
experience coming in was next to nothing so you know it's it is possible to pick
it up Oh totally totally I'm not saying but to set yourself up for success yeah
great okay okay what if an applicant does not have any prior experience in
data science maybe Chris could answer this yes I'm pretty sure coming in I
didn't even realize like I don't think I knew what supervised and unsupervised
learning even meant when I showed when I saw them on my my timesheet my schedule
so I think that's fine I mean if you've got the background and you're you're
interested in the field I I don't at all great do either of you have
anything to add or nothing all hungry okay great and then what if an applicant
has a lot of prior data science experience or education do you think
what do you think they will find or feel with the program any advice I found it's
it's incredibly broad so I'm constantly because I work at a start-up I can
constantly see different places where I can apply it so if you've already got a
lot of experience in one specific area you're still gonna broaden your your
scope but for me it was really useful to start broad and then find the parts of
data science that I find really interesting and specialize in those so
that seemed like the logical sort of process to do it in but if you're
already in data science and you you already are doing what you enjoy I think
there's a lot of self teaching you can do anyway to to pick up on what you're
doing and certainly on the job you need to be you know the field is progressing
so fast that you need to be keeping up with it anyway so yeah it'll broaden
your scope along great thank you okay so maybe I'll save this one for the
Alumni so working as data scientists now what skills do you think are most
important to have and what subject areas of study are an asset when you are
looking for a job so which of the courses maybe helped you have the most
in your job search that's kind of a hard one to say I think in general I think it
was just a lot of skills that we learned in the program so being able to put on
my resume that I've got R and Python and sequel and machine learning algorithms
right there's I think it's a combination of things that may be successfully
getting a job I don't know if it was anyone in particular personally yeah I'd
agree with that you come out with a very broad range of skills so certainly when
I was looking for work I think that is appealing to small companies that you
know you interviews data scientists they're not even sure what they're gonna
have you doing yet and then you know sort of six months down the line you end
up in a very specialized area but it's you know I the company that I work for
now I came in and just sort of identified things like we need a
database we need to be doing deep learning for these problems and sort of
learning that on the job myself but yeah I think the broad range of skills again
it's like what is really attractive to companies great okay you probably all
can weigh in on this one although maybe yours hasn't happened yet but you can
weigh on what it husband so far so what was your favorite or best experience in
the program I was thinking about that question I mean I like I liked a lot
about the program we develop at the same time it was tough but I guess one thing
I really appreciate about the program is the fact that I was changing careers but
I also feel like not only do I have this new career path but I've got a network
now of 20 other data scientists that you know we just went out for ten of us went
out for drinks the other night together and we're all catching up them you know
we kind of stay in contact so yeah I appreciate that part of the program for
sure favorite material I guess probably supervised machine learning was what
that was what sparked my interest and and what I've gone into since then
that's what I really enjoyed okay should be a bit of a tangent but education as
far as the education goes having the support of because we have a very
dedicated team of instructors and having that support along with the environment
of your fellow students who have all such different backgrounds I think it's
my favorite thing at the program because you're learning so much not only from
your instructors and your TAS but also the people around you because they have
different specialized skills and that to me is huge
great it's also just incredibly applied which I loved there was no like
everything you're doing there is a reason and you can use it mm-hmm that
was really nice coming from a sort of traditional master's program and into
something that was just like very punchy in terms of the delivery
information great okay the next question is more about while
you're in the program so maybe you can comment on how you interact with others
in the program so we can talk about perhaps students TAS the lab instructors
and then the the lecture instructors I think in general though the Elector
instructors were available we have some great TAS and great Teaching Fellows I
can't really complain about any of that actually there was a really great
experience from that in that respect and the instructors always wanted to hear
our feedback as well so they were very receptive to how are we handling a
material is there anything that they thought that is unclear that needs to be
covered again even like teaching styles like I think they were very receptive to
just about any feedback we had and then like we touched on before within the
class our dynamic I think in our class was really great and we're able to learn
from each other I think all that was really positive right now and even as an
example like last night I was having a hard time with something about missing
values and a data set and I we have slack so I contacted one of our
instructors and he got by it was like 10:00 p.m. and he got back to me it was
really nice and the time because you're not sitting there frustrated for ages I
mean yeah I shouldn't say that like it happens all the time but it was a big
deal at the time yeah look fantastic
okay so maybe I'll direct this one at you Oliver and maybe you've touched on
this a little bit but you can go into a bit more deep detail but what is the
most useful knowledge that you learn in the MDS and how are you taking that to
the job sure so the lots of different I mean that the area would be supervised
learning and there was sort of lots of different pieces that you pick up in the
course and you maybe I couldn't recite them on the spot but I knew they existed
and that was really important going into where I work now
so I'm currently building a classifier for natural language processing so we
have sentences come in from users and we need to classify those into 50 different
categories and I immediately had a good I'd like an idea of how you might do
that from from the course and
implemented that at work and we've been entering it iterating over that since so
that's evolved from sort of a very simple bag-of-words approach to
something that is now a deep learning model that uses recurrent and
convolutional layers and things like that and that's that's all information
I've learned on the job but things that have been really valuable there have
been like understanding the metrics for a classifier things like precision and
recall as opposed to accuracy and all of these small decisions that actually make
a big difference in a in a company like ours where we we measure our performance
and report that to our customers so we need to report and sensible metrics
about our models to them we need our models to be as accurate as they can
possibly be and things like that so we're we now read research papers and
implement the things that are state-of-the-art which I could not have
done coming out of the course but you've you've got all of the knowledge to be
able to open those doors and sort of proceed in that direction so yeah for me
it's it's almost entirely supervised learning it's been what I've what I've
used from this course in my current work but I think that's because our company
specialized in in one thing great I'm Chris do you have a different experience
not really I mean I'm in a consulting field so I'm just been working on one
project right now and I'm not running deep neural nets at the moment but you
know creating a dashboard and running classification models and
and I just feel like I've had such a good grounding though that I'm ready for
like the next project whether that's gonna be breathing with our or Python or
you know whatever technology I need I feel like I just have a good solid
foundation now so I'm ready to keep learning on the job and pick up new
things great okay so again maybe this can more speak to
the alumni but if there's anything that you wanted to add cuz curriculum we do
try and keep it updated and updated from year to year so what tools and
technologies did you learn in the program so so many well have a go get is
yeah so get github Python are those those are kind of the big ones and then
I guess everything and then AWS as well and then everything kind of sat
underneath that so that you know all of the like all of the libraries that you
would imagine they're things like ggplot which i think is great for visualization
yeah maybe just Randall so many people yet we don't know sequel as well
anything else you want to add Jordan not yet all right great
okay this will be towards the Alumni what kind of enterprise level or no this
would be for that everybody sorry not we don't ask about the enterprise data
you're working with now what kind of data did you work with in the program
some of the capstone projects working with that data that was that was great
maybe you could remaining so who your partner was and just a little tiny bit
about the data sure so for my capstone project I worked with Rio Tinto it's a
mining company and so we were given drill results basically and so we were
taking a look at trying to identify early-stage projects that most resembled
later stage projects that were successful basically and so that was
really helpful to get our hands on data like that not just because the data set
was complex but because it's a real-world application where you have to
really start to understand what the data means and you can't rely on your
assumptions about what you think that column is is but and I'm realizing that
as well with in my job right like you have to look at the data set but
sometimes you have to get used to having to really gain knowledge of that data
set so there was that and then we worked with the Hemnes and missed handwritten
numbers data set it was they definitely switched the data sets up through the
course in the content as well so that was something I realized you're not just
constantly doing you know this different theory on the same side which is
interesting
yeah then for my capstone I work with Coast Mountain bus company so we had as
much data as we wanted to ask for if we end up with a hundred gigabytes I think
they had well over a terabyte of data that would have had from them and we had
nine weeks to produce something so we we scaled that back but that was a this is
a bus company and that was trip times for buses between two points
spanning back five years for the whole of the Greater Vancouver area and we
were trying to predict the runtimes of those buses so that we could help them
schedule more accurately in the future yeah I think right now we're just
starting to get into like stepping away from like Titanic and the IRS data set
and we're right now doing further the answer provides Learning Lab this week
it's a eight gigabyte sparse matrix for Amazon ratings which is really cool
because it's you're really starting to get into actual features and user stuff
and so that's been really interesting so we're getting your feet wet obviously we
even hit the capstone point yet but I'm excited for that all right so I guess
the next question that I'm gonna ask is maybe if we can talk a little bit less
about the technical aspects may be a bit more about experience so maybe you could
all cuz you'll have different experiences even though you're in the
same year comment on the atmosphere of the program atmosphere of the program
yeah and how you found that and how that worked with you as an individual I guess
I touched on it before I felt like you know there's a lot of feedback there's a
lot of interaction but then the class it was great
so there's a lot of support I felt but it is intense so the atmosphere is
definitely definitely intense it's kind of like a we're all in this together
this fear like it's very supportive in that respect yeah yeah but it was it was
tough it was a tough program at the same time yep but it's it's so it like that
kind of challenge it's just so fun you know ya know it was it was a great
challenge and you know it's like I would do it again if I could go back in time
but don't know if I necessarily want to do that I don't read that at the Bowman
but uh but but I came away with a lot of skills so yeah it was a great experience
yeah and a load a lot of maturity in the cohort I found and people are there
because they've spent the money and they want to be there making a decision then
it's not like you know often I I found in undergrad they're people who they're
because they just thought they should do a degree where some yeah everyone was
there because they thought about it and made their decision
some people have quit jobs to be there etc so that was really good and then
there was a I think we all found it really interesting so we you know we
would often talk about it outside of outside of class which was great as well
excellent yeah and then after you talk about it you go down a rabbit hole of
something they're interested and they've never heard the term rabbit hole so much
as being in this program just go off on a tangent okay everyone's super
supportive like I've said most six times now that's
great thank you for sharing that okay this I think goes along a similar theme
I guess a less technical ass about the technical aspects of program more about
yeah atmosphere and how you felt about it
so let's talk a now about the length of the program so Chris you mentioned that
it's intense it's ten months what did you like about it being ten months and
what did you dislike about it being ten months yeah I think that was a bit of a
conversation that we were having because we were the first cohort and we're
deciding you know we want to try to provide feedback you know and so I liked
that it was ten months personally it's like I said it was it was a tough ten
months especially the eight months of coursework
but I yeah I I like the look of it personally yeah especially cuz the field
is changing so much right now like stepping away from work for more
than ten months you don't know what's gonna have to when you finished yeah
yeah any negative thoughts about the ten months or or things that you found
challenging it's not the kind of program where it's like you can have a part-time
job outside like you're definitely intense and focused on it mhm and you're
gonna spend a lot of time with your cohort but that to me is worth it but
just depends if you're a big person he likes to travel a lot or something you
might not have time for that yeah it's yeah it's it's definitely possible to
have a weekend but you have to be ruthless with the or time management and
I personally wasn't like I didn't want to go back to university I didn't like
the idea of a two-year course either I was trying to get it done and get into
industry so hear me spot-on but if that's not what you're looking for then
yeah you've got to know you want it before you go into things yeah fair
enough and then was ten months enough I think so yeah than ten months at that
pace but yeah it's funny I think when I graduated I felt like okay I just
changed careers I barely had any data science on my resume and I thought okay
this might be kind of tough to get a job and I sort of wondered did I really have
the skillset from this program to do it but now that I've got a job and I've
been working at it and like everything is going well I'm like okay no this this
did give me the solid foundation that I need and like Oliver was saying there's
still so much to learn because it's such a such a developing field that even if
it was two years you know you still wouldn't know everything and you'd still
be learning constantly so I feel like it's enough to get a job and to feel
comfortable in that job and then you'll just keep
on the job yeah yeah that too and the piece that you're learning at I'm kind
of anticipating that that'll be a benefit when we're actually working
because you're gonna be picking up stuff quick and we'll have a lot of experience
of that eight months worth for sure right
that's true yeah great maybe I'll start with all ollie on this
one what would you tell your 10 month younger self to watch it for him to
prepare for this program that's a tough one so what would I tell myself before
starting the course yeah yeah if you could give some advice I think I just
kind of told myself those things before I started it anyway yeah
but it was more like you know it's for me it wasn't about grades it was what do
you find interesting what do you want to focus on this is an incredibly broad
field and there were their areas of it naturally that interested me more than
others and that's what I put the time into and but for me it was a focus on
understanding like do I understand this okay move on and to try and get through
the content so yeah I think that that was really helpful in carrying me
through great any advice that you two would give yourself make time for
balance like exercise that kind of stuff cuz it's so easy to sit there for 12
hours and go oh I need to do this and I'm gonna do that and then you go
getting down that rabbit hole that I told you about and you're like oh wait
I've been sitting in the same position
balance yes yeah I don't know I was expecting it to be intense you know sort
of like preparing myself for it and it was so I don't know I don't think so
okay all right so we've reached the end of our pre-registered questions so I'm
gonna lean over to to the web now and take a look at some of the questions
that are rolling in off the Facebook page live so the first one is how good
is the data science field explored in Canada
so perhaps maybe we can just comment on how are you finding the data science
field in Canada that's an interesting question I'm not sure if I've been in
industry long enough to really get a sense of that in Canada but I mean
everybody in our I think everybody in our class has a job now right I've heard
of I know my company is getting more interested in and expanding their data
science program I was talking to another one of our alumni yesterday who's
working at a company and they're looking extending so I think it's growing I
think it's still definitely growing it's my sense any perspective Olly
no I just don't agree with that really I do get the sense that the demand for
data scientists is growing faster than the course can partner matter so I guess
that's that's a great thing Google's opening office in Vancouver I
think soon and there's this obviously Microsoft here as well yeah so they're
they'll hire a large number of dates scientists there and then there's a bit
of a tech hub as well particularly in Vancouver I think because it's cheaper
to start a company here than in the States so and yes a lot of small
companies yeah great okay so maybe I'll jump back to Olly how did you find your
job yeah your current job after you left the program I yeah i loved it i was in
came into a team of a very small team of data scientists in a company that was 17
people and we've since tripled in size in the last six months okay it's just
been completely nuts but been so fun to watch it watch it grow at that speed so
did you apply for that position were they how did you find how did you find
it yeah how do you find no no no how do you mind yours I was on AngelList I
think looking at startups on there and I saw saw this company and shot a message
over LinkedIn to the CEO so I'd love to grab a coffee and chat about what what
you do and he bumped me on to the VP of data science and I was
interviewed a day later and then how did they have to there such as the speed of
a company like that yeah yeah great Chris what was your experience like yeah
I was applying for jobs in a few different places actually not just
focusing on Vancouver so and then I I made it pretty far in a job interview
process with Microsoft but then I didn't get that job but then they recommended
me to one of their partners t4g okay and yeah I still have actually haven't even
met my boss face to face he's in Toronto so he hired me over the after a couple
phone interviews okay and yeah great I think the the capstone is also really
good for that I I did get a job offer from the company we did the capstone
project with and those relationships are only gonna grow as the course growth so
maybe you could each comment a little bit more about the capstone what
specifically would be interesting to hear would be how did your team work
together and how did you split up the work I think that would be an
interesting to comment on okay yeah so like I said I worked with Rio Tinto
mining company there was four of us on the team and so we were looking at a few
different things so I said I part of the modeling that we did was we were looking
at identifying early-stage projects there was a couple other modeling things
that we were doing and then we were really trying to create a dashboard to
visualize some of their data that they hadn't really visualized that way before
and so it in our project it kind of naturally split up so we started with
the visualization and there was just so many different things to visualize that
we each took apart and then there was also different things that we needed to
model so we took a bit of a role than different different components of that
but we met four days a week four like 6 hours+ every day and so we had a
lot of contact with each other that were that worked pretty well okay our team I
think was really successful and just yeah it's great awesome
and how about your team Olli as it was similar yeah it sort of naturally split
into a supervised learning problem and a visualization problem so we split the
team down the middle and it was good gizelle four of us and yeah two people
actually wanted to work honey to them so we weren't fighting over projects which
was great and then we also made the decision to meet for four days a week in
in the morning which I think I don't think that that was critical to the
success of our project there were some groups that did it more of a sort of
remote working style and and got a great deal done as well so yeah that's that's
how our team worked great next question is okay I think all three of you could
probably comment on this so what will be the depth of understanding of the
machine learning algorithms that you learn anyone who would like to address
that question well the depth of understanding of the
machine learning algorithms yeah so in the program how deep into how deeply
your learning go into the out in the machine learning algorithms I think that
they try to go pretty deep actually so it's not just a surface level like use
this and they get this outcome no they they do go pretty rigorously into the
the algorithms I think yeah like you know we did the first iteration of the
name algorithm two weeks ago like working through that you definitely
learn to understudy you get the back the depth of it you know what I mean you
understand what's going on behind scenes right exactly
yeah it's not just okay run this and pretend to interpret it like you
actually you know what's going on no which is really good cuz
and you know and it's important it's important to know that as a data
scientist going forward right so I just got back from a presentation yesterday
and you know I'm ready to like talk about my models and defend my choices of
why I did certain things right and that's important you need that so and
I'll be I'll be having to explain the models to other people next week right
like and and we just it's important and I think the program realizes that and so
we're they're making sure we realize that you yeah I mean we went as far as
things like you know the the depth was on the level of you know what is the
equation for the entropy to split in the decision tree and things like that so
they we we definitely went deep enough to have a full understanding of these
things and I found that particularly useful in my work where we're we're
building neural networks where we need to be able to block the gradient in
certain places and so understanding things like back propagation is like I
wouldn't have understand why you block the gradient in a neural network if we
hadn't gone that deep in the course so yeah super important great thank you
okay this will be for the alumni and please don't feel like you have to
release any personal information you can maybe give a rough buffer on what data
science salaries are like in the Vancouver area maybe from like y'all
just call me on your cell I'm sure you've talked to your colleague sense
that you probably have some rough idea if you want to be that could be perhaps
a to personal question okay let's go back to the student experience let's
talk about assessments in the program so maybe if somebody wanted to comment on
and describe how students are assessed at the different levels and then maybe
how that helps or hinders learning from your experiences well each week you do a
lab for each course where you go quite in-depth like you're learning a lot in
that week so the labs are a bit long but in the sense you're really tackling
everything whereas every two weeks or so we have a quiz on what we've covered but
that's more low level conceptual making sure you actually understand and not
just the nitty gritty little details so it's a nice balance in that sense
because you're getting kind of a well-rounded set some what's going on
yep definitely agree I think I think that approach is really good because you
know you spend a lot of time working on a solution we're just you know really
testing all your capabilities and it might take you like ten hours to do that
lab right but then but then you do have these quizzes for your you need to know
just like little specifics about things right but you don't get to in depth a
minute well it's nice to with the labs because if you're doing something a
certain way your own way when you talk with someone else they might implement
it a different way and then the actual solution is a different way and having
spent so much time trying to figure it out on your own you're definitely gonna
learn from that and get a very like and once again a well-rounded experience out
of it great thank you
okay so this will go back to Olli and Chris what does a day and your job look
like every day I guess that's a yeah that's a that's the thing I love about
it and most recently it's been building models so there's a lot of engineering
work that's had to be done around that so there was a time when I was doing a
lot of work on the company database and recently been doing a lot of engineering
work in Python to set up our infrastructure around the models but now
that we've got to the place where we can kind of just go wild with the research
and it's reading research papers for the specific problem we're trying to solve
which is either sentence classification or entity tagging seeing which models
apply most to our problem and then trying to implement them at work which
is yeah it's a lot of fun Chris how are you thought yeah so I'm at a consulting
company and like I said I just I'm just working on this this first project that
I've got so it's probably gonna look much different once I'm off this project
next weekend on something new but we're working on a proof of concept in
partnership with Microsoft they're they're pre sales team so my job right
now is is building models investigating the data communicating with our the
client making sure that I'm on the right path with this I've got quite a few
calls between me and we've got somebody else in the company who's been helping
on the visualization side I meet with Microsoft probably once a week I just
got back from Calgary yesterday where I gave a presentation on you know our
almost final solution so it's pretty varied but lots of it is kind of sitting
trying to figure out what models do how to make the models better getting
them to run but it's it's been good diverse great thank you
okay so along that theme how many I don't know if you have to get as
specific as hours but maybe what proportion of your day are you sitting
in a chair banging away working on code on the computer in the coolest sort of
work I work all of it oh pretty close to all of it I guess except for when I've
got meetings I leave sometimes for meetings but mmhmm yeah I've got a giant
whiteboard that I use sometimes to like problem-solve stuff so I try to walk
around yep great
I'm just looking through some more questions maybe we can talk about we can
split it into two we can maybe you two can give us summary a little bit more
about the diversity of your cohort and then maybe you can report on the
diversity of your cohort yeah ours was fairly doing quite diverse actually so I
think we had about a third of the students that were just coming out of
university into like their first masters program we had another third that were
that had some experience working experience and then other third of us
were like over 30 and like changing careers camera and then and then we had
a diversity of backgrounds as well I was the only professional musician but but
all he's an engineer there's some other engineers pharmacists arm assists other
people coming from like biology background science background one
liberal art or one arts student yeah and not everyone had a technical
mathematical background in our cohort um which was nice yeah yes really varied
alright then how about your cohort yeah ours is very diverse we have probably
about a quarter so where our cohort is about twice as big as theirs was and so
we have about a quarter of them are just came out of university from last year
there's probably half of us have been out for a few years working in various
fields we have a lot of people with engineering backgrounds math backgrounds
but there's also a lot of us like me bio and I mean half of us I maybe not even
half of us were born in Canada too like we have people from everywhere I think
it's ten different countries that we cover oh wow which is yeah really cool
and various that we have a few people who have worked in data science before
but not a lot like it's the majority is switching careers or getting new skills
to take back to their careers and great yeah it's really interesting great
thank you okay maybe you could one of you could comment on what percentage of
the program is stats versus programming or is there a clear distinction like
5050 like it's such an overlap it's all programming I guess what do we mean like
stats versus machine learning or dad the question is what percentage of the
course is stats versus programming but if it's not a clear distinction I think
it's fair to comment on that too yeah we get started pretty quickly with R and
Python and then pretty much everything you do is to the heart right so your
stats work is gonna be programming to you yeah and then machine learning is
the other part of it and you're doing lots of stats in every block though so
that's overlapping and your stats are commenting on your machine learning and
justifying it's all very intertwined I don't know if you can really say it's a
clear boundary great thank you this again would bounce back to the
alumni have any of your classmates covered their own compass re how many of
your classmates started their own company there any anyone who's done that
I don't think so no not that I know of some of you are working in startups but
haven't but none of the the startups have been driven by the graduates yet
yeah yeah not what I know yeah I think it'd be possible though okay yeah
Whitley when we first graduated I don't I don't think I realized what I knew
versus went was what other people knew about an industry right so I think maybe
I underestimated the skill set that I had gained until I got a job and I'm
like oh no that's yeah you know personality think to like there's a
couple in our cohort who I'm not sure in the next five years are gonna be running
their own startup yeah I'm sure so it's funny I felt exactly the same came
out the course and you're you're intimidated by what you think you don't
know and then you you walk through the door somewhere and you suddenly realize
that you're in a really good place that's great to hear new questions
rolling in does not having a technical background affect your success in the
program any examples from yourself or your data science friends I don't have a
technical background and I'm learning a lot like it's definitely a steep
learning curve but if you're willing to put in the time to understand it and I
mean I I don't think I've struggled in that sense and like I said learned a lot
I think there are so many different parts of data science that you can
choose to specialize in or kind of take a step back and be on a more broad level
if you want to specialize in certain parts I think understanding statistics
and mathematics really well is is important but I don't think it's
actually a requirement for the course I think some people made that decision
there's a guy running a a team for a consulting company that consults with
Microsoft now who was in the course with us last year and he kind of made the
decision to step back from the sort of like very technical machine learning
side and that's that suited him perfectly like he understands all these
things exists and he manages a team really well so it's kind of it's up to
the student great and this program also seems very much suited for people who
don't have the Toronto strong technical background there's that bonus too great
thank you more questions rolling in would you
recommend that students coming into the program prepare by learning a bit of
Python and R before they get here it would make your life easier yeah in that
sense I think it's worth it although I didn't have any R I had a little bit of
Python coming into it and now I feel much more comfortable in our than Python
oh yeah there's a big switch but yeah that's helpful but also yeah sorry it's
not chin drop but also in the first block you're just getting used to the
pace so if you already have a few of those key skills already in there you
can kind of take more time to figure out how you're gonna balance everything
because you do really hit the ground running I think that's not a joke yeah
maybe Jordan you can comment on what were the courses you took in the first
block and what did you learn you know our vs. Python and then how does our and
Python get incorporated in the programs afterwards okay so in the first block we
cover it ironpython in we have just a basics in programming and that
definitely we expanded on that actually more throughout the next few blocks and
learn more key skills like really got into tidy person r and various packages
in your library sorry in Python we also started off really with like git and
github and they really walked us well no yeah walked us through which was really
nice because it can be totally intimidating github if you aren't
experienced in it so the first block was more or less getting you set up and it
was doing also the key statistic like the foundations we went from what does
it mean to multivariate derivatives in four weeks which sounds wild and it is
but you learn a lot and it's really a good starting point for the rest of the
school year and every block really does build on each other too which is a key
thing to highlight so I don't know I just I'm totally biased I really enjoyed
it great and then maybe again back to you
Jordan can you comment on blocks afterwards are you taking Python and are
every courses in Python and are every block which courses tend to have which
depends so with supervised learning we definitely did Python just because we
have there's more libraries suited for it whereas a lot of the I found the data
wrangling we were taught an art and we've gone back and forth like right now
and unsupervised learning we've jumped back between R and Python depending on
what the application was and what we were so everything was a bit more
tailored in that respect but it's been almost an equal divide and I mean coming
in I was much more comfortable in Python than I wasn't art and now they're
probably about equal but stats is mostly done are
like I said it's really dependent on what you're actually doing what language
you're gonna use yeah it's a good balance great and then maybe you can
comment about in your jobs to have you used both languages or did you find that
your job primarily has been requiring one of them and then maybe comment on
the diversity when you met up with your cohort the other ten last week is
everyone working in Python or that is there some diversity so our product is
built in Python okay Python a node so the engineers there in does in node so
almost no effort but we do actually use it for us some reporting stuff so when
when we train our models we have an R script we run that generates a report to
help us understand how good they are cuz for exploring data and visualizing I
think it's it's hard to be the speed at which you can explore data with hours
for me it's much faster than Python so you have a 95 percent Python - mm-hmm
and my company is pretty mixed so this project that I was on and I was the only
data scientist so I got to basically choose what I wanted and so I did our
just because we did our in my capstone mostly and I was working on a a project
with one of the Teaching Fellows a for a for a paper that were submitting to a
journal soon and we just decided to do that all in our so that's just my go-to
language at the moment but I know other data scientists in the companies in
Python on mighty deeds Python oh my next project it's it's pretty mixed alright
thank you
are there any other skills so this is going back to the original question was
should they learn ironpython before they enter the program all of you
resoundingly said yeah it wouldn't hurt and it would help additional question is
there any other skills that you would recommend people tune up on before they
they arrive linear algebra linear algebra time management yeah
time management and linear algebra all right okay this is an interesting
question we are a data science program not a big data program but will the
program cover big data tools like Apache and spark if so yes if not comment okay
so you didn't cover a patch inspired to do we did so we worked a little bit with
AWS yeah we learnt the foundations of MapReduce that are going to be helpful
there was a lab a MapReduce lab wasn't that that's right yes a brutal oh yes
yeah yeah so I feel like we dipped our toes into it a little bit I feel like
now that I'm working though that's something like I was just on Coursera
looking at maybe strengthening some of those skills I mean we did get into
neural nets and things like that it's up a little bit of deep learning but not
some of those tools but I feel like that's something I can pick up now
mm-hmm and having the foundation from here I think would also help with
learning that stuff because I know I've done tried to learn a bit of Hadoop and
deal with MapReduce and whatnot in the past and I've looked at it actually
within the last month too and just having the background so far that we've
learned has helped with the understand just the basic understanding of what's
going on there so I think that's an added bonus
you're definitely capable yeah yeah even though I haven't reached an
I think like the the general attitude that you get from the course is that
you're not intimidated by picking things up after this like you picked so much of
yourself that yeah you know you walk into a company and they say we need to
spark up I spot cool like you just start learning great thank you
so let's talk a little bit more again about your different backgrounds based
on your different backgrounds was there anything that led you to data science or
was it a bit of serendipity like I said when I graduated like out of high school
which is quite a few years ago my strengths are always math and music and
in my first year I was trying to double double both of those and I was taking
calculus and statistics and computer science and things like that in my first
year University but then I ended up going down the music path and then yeah
I decided you know what I think I want to change careers in and get back to
this and so I wasn't looking at data science but looking at all those fields
which just happened to be the fields that are necessary then for data science
and I guess what drew me to data science is just there's there's so many
different applications for it like there is the possibility to a bit you know use
music and data science eventually I mean now I haven't done but to that yet but
but I know that that exists and then and so I just like the fact that it's so
open-ended and there's constantly things to learn yeah great yeah I was my
decision was a bit gun ho I didn't really know what I was gonna be had I
was working as an engineer before that the mechanical engineering scene in
Vancouver is not the same as in the UK okay so for me I was kind of like all
right I'm time to make a move into software cuz I like the idea of
something that's not really there's just so much freedom there and I looked up
some courses like lighthouse labs and things like that initially but my
initial thought was like alright my strength is maths and you don't need to
be good at maths to write code at a really high level
but I would argue to an extent certainly in certain areas of data science you do
so that was kind of the answer to why date science for me is like software but
applied mathematics as well great see my lore around about like like coming out
of high school my strength was definitely math but I came into
university thinking oh I'll do chemistry and then by the end of first year
biology was a science I hated the least whose I went to that and it was just
kind of like trying to figure out what I wanted to do with that cuz he's never my
first choice yeah and then we got into third year bio stats and that was fun
I'm like well okay maybe this was just a fluke and then my last year I finally
took like literally my last term I finally took a programming course and I
was like this is fun I'm gonna so then you got into the more massive open
online courses and just everything I gravitated towards too was data sight
hmm and then this program came up and I was like hey oh why that's great in your
application to this program did you apply to other programs or did you
consider applying to other programs and what were they I also applied to The
Bachelor computer science one here okay but that was all I applied for no I was
taking courses at SFU looking at their actual science program
so that was my plan until I heard about this program okay I got it
so if I hadn't got it I probably I would still be right now doing dr. Birol
science courses as opposed to having a job great alright so the next thing that
I think we could talk about more of the student experience I want to bring back
to that that theme and let's talk a little bit more about your experience in
depth with the instructional fact but the instructional staff so we touched on
that a little bit but maybe one of you could comment on the continuity of the
teaching fellows that go from block to block and how you interact with them and
someone could comment a little bit more on the
instructors how you in the lecture and how you interact with them and where
they come from in the program okay so we have our Teaching Fellows that they're
kind of the bridge between the lab and the instruction team and they're great
because they come like we have Vincenzo and gvd are they come straight from
block one and follow you all the way through so they know exactly what you've
been through they've been with you as you've
struggled with some of these things and you know you're asking questions and
whatnot but then the actual instructing team is some of you guys but then also
like our stats instructor right now he's an expert on missing death and missing
values and whatnot so that's awesome to learn about - from him and he's
accessible by email office hours whatever and so that you have that nice
bridge where you bring in these people who you know might I don't want to say
they're more experts in certain specific fields whereas we have the TAS also who
come with us throughout the different courses - who kind of can bring it all
together with us so it's kind of I don't know if this is making sense but like
it's that you're getting kind of multiple depths with it mm-hmm that's
great yeah just touching on that as well so there's twenty-four one credit
courses and so you get a lot of different instructors that are all
basically teaching their specialty right and so you're learning little bits of
information from different people and then if you cover the same thing by but
slightly differently with two different teachers I mean I think that's really
helpful so I like the setup of the courses and the diversity of the
instructors for sure yeah you can definitely see the threads between the
different modules and you kind of link things up as you learn which is nice
great so another aspect of the student experience we've been very focused on
the technical nitty-gritty parts of data science but this is a professional
master's program so maybe you can comment on some of the other support
that the program gives you to prepare you for the job for the job search so
maybe someone could comment on start calling anything on that and we can talk
about that for a little bit we actually have a career fair today
but yesterday like we've had an employer session where they come in and they
describe their data science team and what they do and what not which has been
really interesting but we also which to me this is a huge highlight for the
program but we had a networking workshop we're doing a resume workshop a LinkedIn
workshop anyone for me is and we're doing an interview prep workshop which
is huge because I mean I was preschool teacher like I can interview with
three-year-olds but I don't know how to do so that to me is huge yeah those kind
of built-in experiences and they're also tailored to data science you're not just
going to a generic cover letter workshop words answer this in Swift that it's
more tailored and that I think it's gonna be hugely helpful in the done job
search sure and the capstone project as well is really helpful that way so not
only are you getting into industry and seeing those kind of data sets and those
kind of problems that you're actually making contacts with people in industry
and some of us were offered jobs from those companies I've kept in touch with
my supervisor at Rio Tinto and so that's that's good it's been really great yeah
and also the the help with the resume was great and it was very personal like
that got to sit down face-to-face with Malad and and hash out know what was in
my resume and what should be there what shouldn't and what I should be thinking
about when I'm writing it so it was it was very personal great and then let's
go a level up so do how how did you interact with departments that support
the program so the port the program is shared between statistics and computer
science do you have interaction with those departments at all are you pretty
focused on MDS will you complete the program it's pretty focused yeah yeah
like our Teaching Fellows are doing their PhDs in or have completed their
PhDs in the computer science or statistics Prout department but they're
all pretty focused on MDS your lounge is in computer science so you're physically
in that building sometimes yeah but but mostly you're very focused on the
program I don't say that's a strength of the program the fact that you don't feel
like you're just you know part of two different
departments they actually went another way to make sure that this is its own
thing with our own courses and you know best parts of those worlds but I don't
know I didn't personally have much contact with either department but a lot
of support here within ideas yeah a lot of it yeah and the people who teach the
the courses are greatness that there's some real experts teaching the program
as well and that's quite exciting you know in your you know in our advanced
machine learning course you know that the guy teaching that is sort of a
renowned expert in optimization and things like that so there's there's some
real talent to look up to while you while you're studying all right again
keeping on the student experience let's talk about the soft skill development so
how much emphasis in the program is there on soft skills development and
we'll call soft skills anything that's not you know statistics or programming
so we can talk about workflows or writing communication as well as resume
building and then how useful have those things been in your work experience yeah
so we did have courses on communication which I think has been helpful
I mean I've had to present to multiple types of groups so you know to the
higher-ups you don't want to hear the intricacies of the model and how do you
shape that talk versus next week we're doing a technical handoff and and I feel
like I've gotten a lot more experienced I was actually just thinking about that
yesterday we were being able to go into a 20 minute presentation and you know
what basically mostly prepared on like the plane over like I was gonna do as
opposed to like the agurbash but the three-minute presentation I had to do in
communication first thing which I think of some of the other well good
communications yeah and our communications course was actually
taught by someone that was in your cohort that's right and he said an
English teacher from the in the past and that was a really good course at the
time we didn't realize just how important it's gonna be but as you
progressively go through more of the blocks
it really gets highlighted and also we're close to you were our close
instructor and that's not the kind of course you
find online like it's not it's one of those things that you really need to
practice and it was all laid out and it's still we're still applying it to
blocks that we're doing that we're in right now yeah and we had course is all
my confidentiality and security those are coming up for us yeah and those
those are really helpful right it kind of gets you the bigger picture of what
its gonna look like other national data scientists in the real world yeah yeah
not amazing data also courses on communicating how to communicate
statistically statistics with people yeah which is something I have zero
experience in before the course can you comment on maybe you're a wider
experience at UVC so how did you enjoy being at UBC and maybe you could also
comment about living in Vancouver too at the same time um I I liked UBC but I was
also finishing my doctorate in piano so I've been at UBC for way too many years
here at UBC it so as beautiful as it is I was happy to kind of get off campus
and graduate but no at UBC is a beautiful campus it's probably one of
the best in Canada not be best in Canada great city to be in yeah I think with it
being a professional master's and being a lot more intense than your average
degree for me at least I wasn't in the kind of student community at UBC you
know I felt sort of like this separate thing where we're just hammering through
this content and I have my social life outside of work outside of studying yeah
but that's that's kind of how it felt for me see because I did my undergrad at
UBC so I have a bit more of a different background in it so like day of the
Longboat which is a huge thing at UBC we dragged out two teams to do that we had
a soccer team I didn't play on it but that everyone did take part where
possible mm-hmm so there's been a balance in that I know we have a girl
who's in the dance okay so there's totally options out
there to get yourself more involved and I know like I live downtown I live right
by Stanley Park and living off-campus cuz I spent my whole undergrad living on
campus like you said you got to get off huge yeah I mean do many of the cohort
live on campus that are in either of your core parts majority live off I
would say okay but there definitely are a few and there's different options like
I know there's Green College where they actually have dinner and breakfast made
for them which is super convenient when you're trying to get all your stuff done
you don't have to worry about that but yeah if you if you manage your time you
might have something left to enjoy Vancouver which is I love it here yeah
you like skiing and riding bikes and mandarine snowing in the mountains thing
that's true yeah depends on your elevation right yeah another question
coming from online how do you feel career opportunities have grown are
there more integrated jobs in data science with research and science or
they mostly jobs that are in industry and companies so if you can think about
who your jobs that you saw when you are looking for yeah looking for work or
what your colleagues are doing now so are most people working in industry on
like a web company or some finance company or there are people doing things
in government not-for-profit academic research groups or did you see jobs out
there like that I think every company knows they need a data scientist not
necessarily what for so there's there's a lot of job offers out there with you
know big clothing companies and things like that where you're I would imagine
your work would be mainly focused on sort of increasing sales on it on a
website for example yeah so you see a lot of companies like that you also see
quite a few small companies that do a really wide variety of things company
companies like semi else who I think they're doing capstone yeah yeah and
they do like machine learning for manipulating pests so they can kind of
help help us out with the problems with moths and things like
so like that's like a really interesting problem we're using data science to
actually solve a thing run sell more of something to someone so that was the
kind of thing that I was looking for and yeah that there's a bunch of companies
like that and then you've obviously got like the Microsoft and the Amazon and
Google coming and things like that so they're gonna have sort of very high-end
research being done there I would guess
okay so this year in a lot of the blocks most of the blocks you're having a
course that has a project so you have three courses that are your typical lab
and lecture structure and now we're having a more open-ended project course
what what type of prep projects have you seen people doing maybe if you can
comment on or what have you done for some of these projects well we had
what's the workflows class we had a just a project that was basically
understanding the workflow but it was cool too because a lot of people did
different things with that like some people went quite in-depth like I know
one guy he did a whole full mental health analysis and it was meant to be
just a workflow thing and he's expanded that into this block when we're doing a
mini feature selection project and he's taking that and he's running with the
two so you get more in depth depending on how you want to work with stuff and
ya know it's the project they're awesome too for our portfolios I'm excited about
that yeah some all the projects are oh you guys are putting on so maybe we can
talk a little bit of a github so when you're working maybe you can comment
about how you interact with github maybe I'll leave this to you Jordan this year
so there's two github that you interact maybe you can tell the audience about
that okay so yeah so we have the to get house we have our public one that
everyone can see and we have the UBC Enterprise one so that's where we find
all of our lectures and our lab material and where we submit all of our stuff and
submit all our quizzes and that one is completely private but we link a lot to
our public github if we're doing a project cuz that's where you want your
portfolio to sit and it's we do get for everything like you're pushing to get
every day because that's where you're storing all of your assignments even if
they're only partially done so at least your computer crashes which I've seen
happen is here people can recover their files and know there's all the work
they've done that was a lesson learned not for me one of my friends but great
and maybe you can comment me beyond the projects that you're doing in your visit
class oh yeah we're doing a shiny app which it's just kind of an interface for
it's nice actually I'm doing a wine data set that they provided with it for us
but you're able to allow the user to select different variables that they
want to visualize and you manipulate how they see and what they
see and the output whether it's a figure a table I know some guy one guy sorry
who is doing 3d I don't even know how to describe it he's basically doing stocks
and analyzing them through that but it's meant for the user to analyze it's
actually really interesting in that respect because there's a lot of
different things you can do with it see how these are very diverse because I
forgets to choose their own data sets to work marry so it can go from very simple
to it from a scatter plot to the 3d rotational stock model great more
questions rolling in here do students and alumni share I'm guessing this is
the coursework on github that the question is about probably so do
students and alumni share the work that they did on the course on give github
comm do share the course record I mean I think the labs are complete they're open
mm-hmm so that that's in the in the in our internal internal exactly so if you
wanted to compare notes and you know see what your classmates were doing yeah I
never did that a lot but I know some other people were sort of interested in
how some other people have implemented things hmm
and so that's available mm-hmm but not from like cohort to cohort no thanks to
me yeah that's right we have access to some of your lab or so your lecture
notes from last year okay but we don't have access to your work I don't know if
that's what's oh yeah I think so but the viewers want to see so the viewers are
interested in seeing some of the labs and such oh okay maybe we can post this
to Facebook we do have not that work but we do have some of the instructions for
a typical lab public on our website so we can put a link to that on github.com
and one thing also that perhaps we could do as a program is that we could take
some of the ask some of the students if they'd be willing to share some of the
work that they already have on github.com for their projects and share
that so that's perhaps something that we
could do okay how are we doing for time here okay we still have some more time
this is cool great recommendations for how you balance studying in life in the
program we've already talked about how this isn't an intensive product program
but let's maybe talk about some of some little some like actual practicalities
did you have a coffee every morning did you do some Sun Salutations for example
what did you do how did you what did you do personally to balance I steeled
myself to the idea that it was just gonna be tough for a ton guys personally
I knew I was changing careers it was gonna be hard i I've got two kids and I
I still needed to work a little bit and so my my year was just nuts and at the
machine you know postal and I was finishing right my dog staring at the
same time so I was like beyond crazy and so I just accepted that and that was
fine I think all I had on my plate was the course I didn't have any children to
look up which sound great I was grateful for but for me I just set rules like I
would turn up it I'd be on campus by 7:30 working for an hour and a half
before I went into lectures and then I found if I did that then by 10:00 p.m.
that night I could have the lab done so I could have all of Monday's work done
by the end of Monday and I've found by that I could get to Friday afternoon and
have nothing to do which I think as one of the only people who who worked like
that and it took a lot of like setting rules for myself but I got to ski every
weekend and I had it had a weekend during the course which I needed I think
I would have dropped out to do that unless to you yeah with you on that one
like all of our I think it changed from last year all of our assignments are due
Sunday at 3:00 I don't know if that's gonna be the same for next year but so
Monday and Tuesday night if I make plans with friends it's only Monday or Tuesday
right because by Friday Saturday like
most of them like 70% of the lab work you can be done by Thursday night Friday
morning but then I spend time tweaking and like trying to further my
understanding which is really me playing around up until the Sunday crunch time
but that's just more of a personal thing like that but I'm also like you on
campus by 8:00 yeah working I like it that way and if you can find a quiet
space and kind of make sure there's no way to be interrupted you can get a
great deal done in less time as well a family so maybe we can put some numbers
on for the folks they're asking how much time did you spend on coursework per
course so we can talk about maybe we can bring it down a little bit so we have in
one block which is about a month long you basically have four weeks of work so
there's four labs and two roughly two quizzes per per course so how many hours
let's say how many hours prepping for a lab or completing a lab and how many
hours prepping for a quiz and then they can do the math scaling upwards I
definitely spent laughs I spend less time studying for a quiz than a lab for
sure ya know I'm also a very slow worker so I don't know I can throw up my
numbers but it's gonna be relative yep broadly a lab could be anywhere from
five to twelve hours depending on how intense it is I would say the same and I
would say I made sure I always went to classes I had a very skipped classes
have been if it goes too fast I don't I don't know how how you could manage if
you decided not to go class and then I felt the classes if I just reviewed the
material and do the labs I was often prepared for the quizzes without having
to do a whole lot more and it's helpful to to read the lecture material before
you do the lab so I find there's a lot of explanations that otherwise you'd be
sitting there banging your head over I found actually the opposite so like
I would start the lab before so the morning before the lecture that was for
the lab so I'd spend an hour and a half working on this lab that I didn't have
all the knowledge to do and I found myself sort of not understanding things
and asking questions that then would get answered in the lecture and be really
valuable and kind of stick much more so it would kind of be an hour and a half
on lab then come out of lectures what time would we finish like clock or
something yeah that's at 4:00 yeah and you've
spent two hours on the lab by then so it's three and half hours and then go
home and have it done by 10:00 p.m. so probably about eight or nine hours per
lap something like that this feels like you're all on the same ball 5 to 12 hour
ballpark yep yeah but then I know some people who manage to get all their work
done by Friday morning like you and go skiing for the weekend or it's totally
up to how you want to structure your time and do the students work together
at all are there study groups have you seen that yeah oh yeah that's probably
the main reason that I've enjoyed the program so much when it comes to the
work because we sit there and we go over the concepts like we don't really talk
about how you would code something but just the overall understanding of the
concept before jumping into it and everyone is much more prepared and it's
kind of like a 42 person study group in some respects because everyone kind of
has with each other and before you know it you've ended up 15 people ahead back
at the same conversation you just had in the person next to you mm-hmm
and then maybe you can comment on any of you could comment on the use of slack in
the program and how that affects the study groups and completing the labs and
yeah that's really helpful we're all connective you know if you if you run
into problems you can just you know post a question yeah cuz every class has a
channel yeah and if we're all working like 10 hours on the labs no just you no
matter where even a question somebody's online
so and if you're too nervous to post there's always someone who's gonna post
that's great
next question who's that has just come in is how strongly would you recommend
data science is someone who wants to remain close to the business world I
kind of just recommend it to everyone can't you're trying to get my sister to
make the move as well yeah yeah I mean I'm in the business world for the first
time right now is what it feels like it's true and and I'm really enjoying
the data science in the business world can we think it a lot ok so maybe what
I'll do for the last couple minutes before we wrap up is just ask each of
you to offer any final thoughts reflections on the program as well as
any kind of like advice to somebody considering applying for the program
will start from the sound Jordan and the other way I would honestly say do it
go in totally open minded it's not about the grades in this program if you do the
work and you understand what you're doing you're going to do fine grade wise
but spend time understanding that's important that's the whole point of
being here you here for the foundations and you're here to learn from your peers
and from your instructors everyone has different knowledge be totally open to
it even people who think they really know a
concept going in someone might ask a question and because they think in a
different way and then it totally turns it on their head for the other person so
just be open right yeah I mean I'm not being paid to say this but this best
academic decision I ever made by a mile like I I was working as an engineer
before and I actually worked for a Formula One race team which is kind of
like the pinnacle of like how interesting can engineering get you know
the the time to produce things and do R&D is just like so fast very fast
moving company full of smart people but you know once I was done with work I
wouldn't I wouldn't do any engineering on the weekend you know whereas now I'm
in a place where I'm enjoying my job so much that I'm
blogging about it and spending time doing it outside of work which is
arguably not a good thing no it still still get time to go skiing but yeah I'm
just in a place where I'm just absolutely loving what I'm doing
workwise right now so yeah I would strongly recommend the program great
thank hrus yeah I still I'm surprised sometimes I kept back from work and I'm
like I can't believe that I've got the skills that I've got at this point but
you know a year and a half ago like okay and yeah it's still sometimes a bit of a
shock at how prepared I am for the work that I'm doing and how much I enjoy
doing the work that I'm doing and it's it like I mean it was this program right
that prepared me for that and it was intense it was tough but totally worth
it yeah just to add on to that to thing it's intense and it's tough I still look
forward to Monday's like I don't wake up and go oh it's Monday I don't wanna do
this I feel like Oh what are we can cover this week and the slides are up
and yeah it's tough but really interesting really interesting yeah
that's a key point well I want to thank you all very much for your time this has
been a really wonderful discussion as an instructor it's always very interesting
to hear about the experience from the from the other side of the looking glass
and I hope that everyone here in the audience has got the questions that they
had answered so again this webinar was focused about the student experience and
we're gonna have a follow-up webinar on February 27th more information will come
on the Facebook page and will be announced but that's going to be focused
at the more technical aspects of applying to the program so we can talk
about prerequisites and other things like that and that's all we have for you
today from Vancouver and UBC but the master data science program thank you
very much
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