0:01 they just trying to see how the heck do
0:03 you think like what is your thought
0:04 process I love that you like pulled out
0:06 a calculator and I loved at the
0:07 beginning how you like asked for clarity
0:09 I think that's really important welcome
0:11 to the data career podcast the podcast
0:13 that helps aspiring data professionals
0:15 land their next data job here's your
0:17 host Avery Smith in this episode you're
0:20 going to see me interview two random
0:22 strangers and ask them data analyst
0:23 interview questions in hopes that
0:25 preparing them for their upcoming
0:27 interviews if you guys like this episode
0:29 you are going to love this tool called
0:31 interview simulator I recently released
0:34 it interview simulator. that's interview
0:36 simulator. it's called interview
0:37 simulator because it simulates an
0:39 interview where you basically will have
0:41 this exact same scenario with me where I
0:43 ask you a question you respond with
0:45 video or audio you get to see how I
0:46 would answer the question and then we
0:49 also have an AI interview wizard that
0:50 will give you critique it'll score your
0:52 answer on one to 10 and also give you a
0:54 list of Pros things that you did well
0:56 and some areas where you could maybe
0:57 improve so if you guys want to check
1:00 that out interview simulator. let's get
1:02 into this episode you prepared for an
1:04 interview question yeah I'm in a
1:07 graduate school and I'm just looking for
1:09 kind of like internship and a full-time
1:12 job within a data analytic side so I
1:13 think this is a good chance to practice
1:15 well first off congrats on grad school
1:17 and for being here and being brave to do
1:19 this the questions that we're going to
1:22 be pulling from today are going to be
1:25 straight from interview simulator so you
1:26 can answer them today and then you can
1:29 always go back and practice them here
1:31 we'll start off with with a behavioral
1:33 question and then we'll move to a
1:36 technical question so let's go ahead and
1:39 hop into it Richard okay so we're gonna
1:41 start off pretty simple and because I
1:43 don't know anything about you we're
1:44 gonna start off with the question that's
1:46 probably going to be asked in every
1:48 interview and that is tell me about
1:50 yourself so Richard tell me about
1:53 yourself yeah sure yeah my name is
1:56 Richard and I'm graduate school from the
2:00 U msba program I'm on the second
2:02 semester and I'm very interested into
2:06 the utilizing the data and AI in my
2:09 field for my work experience I will work
2:11 for the data analysis data engineer and
2:14 bi engineer for three years and for
2:18 during that times I utilize over 10
2:20 products and built it for the data
2:23 pipeline for the various companies
2:27 including attech proptech and the
2:29 financial vintch area that was a great
2:31 answer it's it's always scary giving
2:34 those answers always especially on on a
2:36 live call like this so that that was a
2:38 great answer I like that you kind of
2:40 identified what you're currently doing
2:41 right you said you're a graduate student
2:43 that's always key to make sure that like
2:44 they know what you're actually doing
2:46 right now but I also like that you said
2:49 what you've done in the past so yeah you
2:50 said you had been working like as a data
2:52 analyst for three years I and and it
2:54 seemed like you you had done it in a
2:55 couple different Industries I didn't
2:57 catch any of the company names so what
3:00 companies did you work for yeah I work
3:05 for the Buu pathway and the other is
3:07 sorry I forgot the names of the the pro
3:11 TCO companies it was located in Arizona
3:12 okay cool one of the other things I
3:14 thought it it sounded like at those
3:16 companies that You' worked for you have
3:18 like you develop some experience and you
3:20 said You' use multiple tools what were
3:22 some of those tools I would like to know
3:23 in that answer like what tools so what
3:26 tools did you did you use yeah for the
3:29 dashboard building I've used a tableau
3:32 powerp and the quick side and for the
3:35 data pipeline I most likely use the AWS
3:37 stack which is the glue and the the
3:41 Athena or the Thea uh wrinkling and uh
3:43 cleansing oh see that's super awesome
3:46 and very impressive yeah so that I think
3:47 that would be my my critique is
3:49 mentioning the companies and the tools
3:52 by name because like AWS is no joke it
3:53 is not the easiest tool stack to use so
3:55 that's like oh I'm already thinking
3:57 Richard knows knows some pretty good
3:59 stuff so that's great I think that was
4:01 really important to add those things to
4:02 your answer but I thought I thought you
4:05 did a fairly good job one of the cool
4:06 things is you can go back and go to
4:09 interview simulator. and practice the
4:11 question here you know with either video
4:14 or with audio and it does a pretty good
4:17 job you'll get a response from our
4:19 interview wizard that basically looks at your
4:19 your
4:22 answer analyzes it gives it a score one
4:25 out of 10 and list the pros and the cons
4:27 as well and then also lets you watch the
4:29 the replay as well because even when you
4:31 answering the question me as a human I
4:33 was like trying to remember every single
4:34 thing that you said that's the nice
4:36 thing about the interview is is it's not
4:38 human so it doesn't have to try to
4:40 remember so anyways I think your answer
4:41 I I would probably give your answer
4:44 probably around a seven or an eight out
4:46 of 10 I think I think you did a good job
4:48 identifying what you've done you made me
4:49 feel confident that you was like oh
4:51 Richard does have you know experience in
4:53 the past and I think to to make it like
4:56 a nine or or a 10 out of 10 adding a
4:58 little bit more specificity around the
5:01 tech stack and the tools would probably
5:04 be the key there does that make sense
5:06 Richard yeah totally makes sense thank
5:08 you okay cool you you down to do a
5:10 technical question yeah I'm ready to do
5:12 that I think okay let's try a technical
5:14 question I'll I'll actually let you
5:16 choose out of these questions you see
5:19 over here on the screen which technical
5:20 question because technical questions are
5:22 a little bit scary in my opinion so I'll
5:24 let you choose since you've been brave
5:25 which technical question would you like
5:27 to answer today yeah I think the SQL
5:29 window function from Amazon would be
5:32 interesting for me okay let's go ahead
5:34 and do it so I'll pull this up once
5:37 again I'm not going to play the video of
5:39 me asking the question like you normally
5:41 would in interview simulator because I'm
5:43 here live with you right obviously so
5:45 it's probably not necessary but
5:46 basically in this question which is
5:48 taken from an Amazon I think this was
5:49 actually taken from a business
5:52 intelligence engineer position at Amazon
5:55 and let's go ahead and I'll ask you that
5:58 question the question is what is a SQL
6:01 window function and when would you use
6:02 one so I'll open it up to you Richard go
6:05 ahead yeah for the window function I
6:07 think it is very similar for the group
6:11 by or aggregated function but you can do
6:14 without just aggregating for that one or
6:18 or transforming the original or low data
6:20 set so for example like if you want a
6:25 partitions by one of the data or each of
6:27 the users for example then you can just
6:30 like doing group bu and just like
6:32 aggregating some of the the numbers or
6:36 counting the numbers for examples but uh
6:39 window functions allow you two just like
6:42 a without group buy you are just like
6:44 maintaining some of the original data
6:47 set and you are just like adding some of
6:51 the aggregated numbers onto the data set
6:54 so like for example count of the how
6:57 many users are existing per each of the
7:00 men's or women then if you get seven in
7:03 total then you will get the number of
7:06 each each LW as a total number of seven
7:08 by using the window function sweet clap
7:10 it up for for Richard you guys that was
7:13 a great answer and he did a good job
7:16 that is not an easy question the SQL
7:18 window function at all Richard I think
7:21 you did a great job on this question I
7:23 think I loved the thing I really once
7:25 again so just just to kind of highlight
7:26 you can always go back to interview
7:29 simulator and ask you know go through
7:31 the exact same question again and you
7:33 would actually be able to watch your
7:35 answer back you would actually get to
7:37 see me answer the question and then of
7:39 course you'd get your feedback from the
7:42 interview whiz in interview simulator as
7:43 well if I had to guess what interview
7:45 whiz would say about that question I
7:47 honestly think that was probably an
7:49 eight or a nine out of 10 you pretty
7:51 much nailed everything so things I'm
7:53 looking for when when I ask this
7:55 question is yeah saying it's like a
7:57 group by without aggregations I think is
8:00 probably the easiest way to describe it
8:02 like in simple terms I think that's
8:04 really good and then you said for
8:05 example twice which I think is always
8:07 good trying to give like a tangible
8:10 example of when it's useful so I think
8:12 that's really good and it's like way
8:14 easier to show like a table of it being
8:16 used versus explaining it via word so I
8:17 think you did a good job with that my
8:20 only critique would probably be to talk
8:21 I mean you did kind of talk about it
8:24 with that girls and boys example I guess
8:27 I was kind of looking for like some some
8:29 common use cases like some of the and
8:31 you kind of gave it I think you were
8:32 what we what you were talking about the
8:34 boys and girls was like a running count
8:36 I was looking just for that word like a
8:38 running sum or like a running count or
8:40 like a running a rolling average or
8:41 something like that so I think that that
8:42 would have the only thing that could
8:44 have made it a little bit better but
8:45 overall I think that was a pretty good
8:47 answer to a pretty tough SQL Rush what
8:49 do you think does that make sense yeah I
8:51 think it'll be better to if I just give
8:54 the length example so that like people
8:56 can easily understand like how the
8:59 window function can be applied to just
9:01 make the the length of each of the GLW
9:03 yeah 100% but overall I feel like that
9:06 was pretty solid and I think you did a a
9:08 a great job everyone clap it up for
9:11 Richard that is not easy to do and come
9:13 up here on this stage speaking of which
9:15 we have someone new to the stage Joey
9:18 Welcome to our show today thank you so
9:21 much for having me hey no problem thanks
9:23 for being brave I know this is a hard
9:24 thing to to do where are you calling
9:27 from today calling in from Houston Texas
9:29 so I've been following you you know
9:31 LinkedIn you're one of the main reasons
9:33 why I attribute a lot of My Success I
9:35 recently accepted an offer with concast
9:37 MBC Universal as a senior business
9:39 intelligence analyst but I always
9:41 constantly on Lookout to improve my
9:42 interview strategies I have an interview
9:44 coming up tomorrow well a presentation
9:46 on how to deliver the best interview
9:48 tips so also you know looking forward to
9:50 learning about yourself and you know
9:52 this new platform that you have sweet
9:54 that is awesome and congrats on the new
9:56 job I'm going to challenge you if you
9:58 don't mind let's do one of these like
10:01 logic thinking question this one is from
10:03 Airbnb all of you guys watching you guys
10:07 can try it at interview simulator .io
10:08 but the question is how many meeting
10:10 rooms so Airbnb once again I'm not going
10:12 to press play on the question that's how
10:14 you do it in real life in interview
10:16 simulator but basically Airbnb is
10:18 looking to expand and they're building a
10:20 new headquarters and they're trying to
10:22 think through how many meeting rooms
10:25 should they put in this new headquarters
10:28 they're asking you as you know a data
10:30 analyst to try to to solve this problem
10:32 so Joey go ahead how many meeting rooms
10:34 should we build as Airbnb sure
10:36 absolutely first I would have to start
10:38 off with how many people are we
10:41 intending to to relocate to this office
10:44 for example I would also want data on
10:46 sort of the the department titles you
10:48 know I would imagine meeting movs would
10:51 be mostly taken up by folks in the upper
10:54 you know level executive right I also
10:56 would ask about you know the square
10:59 footage data on the square footage and
11:01 as well as the the number of of meeting
11:03 rooms are AV well that's the question
11:05 right so I would say he the square
11:08 footage I would also ask Dana on you
11:09 know the number of hours you know are
11:11 there like peak times that you know
11:15 certain Executives or teams meet most
11:18 regularly I would imagine that you know
11:20 traffic you know a lot of meetings take
11:22 place in the morning with those horic
11:25 ones coming up in the afternoon but if
11:26 you can guide me more through this I'd
11:29 really appreciate it okay great so yeah
11:32 let's say we're going to we anticipate
11:35 around 3,000 people being at this
11:37 building there is a little bit of a
11:40 hybrid schedule however so you know it
11:42 might not be 3,000 every single day
11:45 let's say 20,000 square feet so 20,000
11:47 Square sheeet absolutely so now that we
11:50 have sort of you know I think also
11:53 information of the meeting culture so
11:55 sometimes employees you know take on
11:58 Virtual uh meetings right let's assume
12:00 that 25% of those employees are in
12:04 meetings at any given time and so we
12:06 would say the average meeting size can
12:09 be anywhere from I would say five to 10
12:11 people of course there can be more
12:14 people in the meetings there can be less
12:16 and so with that being said I would say
12:19 each room can be used around you know 60
12:22 to 70% of the time with 30% is where
12:24 like you know just being used like by
12:26 people that just come into the meeting
12:29 rooms to just to get work so I would say
12:31 based on the assumptions so the total
12:33 number of people in these meetings
12:37 simultaneously can be 20,000 right every
12:38 of the ones that you just mentioned I
12:41 would multiply that by 25% of those
12:42 employees that are in meetings every
12:46 time so 20,000 times
12:49 25% I would be around you know I would
12:52 say let me do the ma
12:57 2,000 at 25% 5,000 people right and so
12:59 the number of meeting rooms that are
13:02 needed based on the average size I'd say
13:04 an average meeting size is four to six
13:06 people so I would go around with five
13:08 people I would divide that by five and
13:10 so that would be that's that's not that
13:13 doesn't make sense so a thousand I think
13:15 I think I I I confuse you because I I
13:18 think I said I accidentally said 20,000
13:20 employees but I really meant 20,000 feet
13:22 or square feet but regardless I think
13:24 this was a pretty good answer I think
13:26 you did really well because like
13:28 basically with these questions they're
13:29 just trying to see how the the heck do
13:31 you think like what is your thought
13:33 process and you did a really good job of
13:34 basically you were almost like streaming
13:37 your full of of Consciousness and like
13:38 thinking through everything I love that
13:40 you like pulled out a calculator I
13:41 thought that was that was really good
13:42 and you're and I loved at the beginning
13:44 how you like asked for clarity I think
13:45 that's really important and then
13:47 something that's that a lot of people uh
13:49 probably don't feel comfortable doing in
13:51 an interview all the time is like can
13:53 you can you explain a little bit more so
13:55 I think I think you did a really good
13:56 job I love what Daniela just said
13:58 Daniela says OMG these questions made me
14:01 real I need to do more mock interviews
14:03 well that's that's good and that's the
14:04 point of interview simulator is to get
14:07 questions like this and to take a stab
14:10 at at answering them because like I mean
14:11 Joey correct me if I'm wrong but I'm
14:14 assuming that you don't like spend your
14:16 time every day even as a as a data
14:17 analyst even as a senior data analyst
14:20 like really thinking through how many
14:22 meeting rooms a a a building should have
14:24 right that's not something that you do
14:26 absolutely not but I think where the
14:27 valid comes in is your thought
14:29 processing and your ability to think
14:32 critically and I think that is what you
14:35 know when we're talking about the AI era
14:36 the the ability to think critically will
14:39 take us far and make sure that we will
14:41 not be replaced right because that's
14:42 sort of the trend that we're a lot of
14:44 people say we want to be replaced by AI
14:46 but I think that ability to think
14:49 greatly is what will make us stay yeah
14:51 100% And so these types of questions I
14:53 mean of course every day you're thinking
14:55 critically but it's like you're not
14:57 thinking critically out loud on like
14:59 this type of a weird question so really
15:01 practicing like Daniela said like doing
15:03 these mock interviews is is really
15:04 useful but but I thought you did a good
15:06 job and like in the end I don't even
15:07 know I'm going to cut you off before you
15:10 actually gave a real number but that's
15:11 not the point of the interview is or the
15:13 question is like we don't actually care
15:14 about what number you actually end up
15:16 saying it's more what did you lead up
15:17 before the number I thought you did a
15:19 good job asking for clarity asking for
15:22 more data you know going through it
15:23 almost looked like or or it almost felt
15:25 like you had like a Google sheet or like
15:26 Excel open and you were like putting
15:28 these numbers in and kind of crunching
15:31 it and I I as the interviewer could be
15:33 like okay yeah I could see how Joey
15:35 could solve these types of hard problems
15:37 at our company so I thought you did a
15:40 good job I think at on a rating I would
15:43 give you probably about an eight or a
15:45 nine the only thing I think that you
15:48 could approve on is probably just not
15:50 having to do it in front of people on
15:52 LinkedIn and have like like a piece of
15:54 paper in front of you just to keep track
15:56 of it all that's being me being nippi I
15:58 thought you did a great job so it would
16:00 be fun for you to test this out on interview
16:01 interview
16:03 simulator. and and see what interview
16:04 whiz thinks of your answer because I
16:06 thought it was pretty good yeah
16:07 absolutely thank you so much Avery so
16:09 I'll be presenting this uh I have going
16:12 to take a screenshot of this and uh
16:14 presenting it to my presentation uh
16:17 tomorrow with black and technology so uh
16:18 you know promoting helping you promote
16:21 this hey I I appreciate it Joey thanks
16:23 so much for being brave and coming up
16:24 and uh let me know if there's anything I
16:26 can do for that as well absolutely thank
16:28 you so muchy have a good one I recently
16:30 released the mock interview tool it's
16:32 called interview simulator you guys can
16:34 check it out at interview
16:37 simulator. um for the next 14 days it is
16:39 completely free um and you guys can just
16:40 click this button right here try
16:42 interview simulator and just enter your
16:45 email boom and you will be taken to this
16:47 page right here which is where we have
16:48 all of our questions from all our
16:51 different companies and you can try a
16:52 question from Apple or a question from
16:55 Airbnb or a question from Chevron from
16:58 the NFL from Netflix that good stuff and
17:00 practice using audio and video you
17:03 basically will click on a question
17:05 you'll hear me ask the question and then
17:08 you'll be able to respond via audio or
17:10 via video once you give your response
17:12 you'll actually hear me respond give my
17:14 like example response and then we are
17:17 going to give you a grade from the
17:18 interview Wiz the interview Wiz is our
17:20 AI tool that will look at your answer
17:23 give you a score from one to 10 and list
17:25 all the pros and the cons like what you
17:27 did well and where you could improve and
17:29 we'll also be adding expert examples
17:32 down here at the bottom as well from
17:34 other students inside the program as we
17:36 expand you guys can find the link in the
17:38 comments thank you guys for joining I'm
17:41 excited to be doing this more in the
17:43 future and we'll talk soon have a good
