So did they.

I had an interview with Peter Schoolbus, who's a bit of scientists with experience at companies like to an inn, which is on the audio streaming company in San Francisco On in this interview, hey.

Shared some tips that he used Thio get his six.

Figure that a science job on.

I thought it was really hopeful.

So I want to share it with you guys today on.

Hopefully you'll find it helpful to s.

So you're a data science job at tuning was you're really first data science job, right?

Right.

Yeah.

Uh, could you tell us a little bit about like what you actually did there?

Yeah.

Um, so I feel like the work I did there can kind of separated into sort of a couple of different categories.

Um, one ah, sort of.

My job was kind of helping product managers, um, design and implement and analyze a B tests.

So I'd sit down with the product manager and discuss, you know, a product or feature change, uh, sort of the best way to test that change, Um, and then helped design the experiment.

And then I sort of put together an analysis of that experiment to sort of help the product team, uh, think about how to move forward.

Um, another aspect of my work there was sort of designing various metrics and putting together different types of dashboards to help.

You know, the marketing team where the product team, it would be able to understand how things are working.

Um, but ah, lot of my work.

And I feel like, sort of the bulk of my work was centered a little bit more around a modeling and prediction, Um, so answering things like, you know, if our customers did X, y or Z, can we predict what sort of I want to do on the platform?

Or can we put, you know, a monetary value on a certain feature?

You know, meaning if we we got rid of that future, how many of our, um, you know, paying customers would would turn?

No, eso probably to this call.

You were telling me that you struggled a little bit when you were trying to get your first did it sized job.

Uh, could you tell us a little bit about that?

Um, yeah, yeah.

Um So, like I said, like a couple of the issues was, um you know, learning like the different type of skills that I needed, Um, and sort of stuff like that.

Um, but it kind of felt like there's five.

Ah, so I'm, like, really, um, like, major things that I learned that air, like, super important for getting your first data signs job.

And the 1st 1 is sort of just making sure that you want sequel is kind of the, like, foundational skill of data science.

Um, and and it's definitely not the most like, exciting skill, but it's just, like incredibly necessary.

Um, another thing that's really important is, uh, you know, just being able to, like, explain your experience in a way that makes sense, too.

Product managers into executives.

Um, so when you're in school and when you're doing research, you're, you know, mostly surrounded by other technical people.

But when you're working for a company, you know you're gonna be interacting, you know, with nontechnical people all the time.

Um And then, um, let's go through those sort of one white one s o for a sequel.

What do you think would be a good way to learn a sequel?

um So what I think is probably the easiest way is to kind of just, like, learn by doing, um And there's definitely a lot of, um ah, a lot of tutorials out there that I feel, like, kind of differ and how helpful they are.

Um, I found this tutorial, um, from mode analytics.

Um, that was kind of like an introduction to sequel that I thought was super helpful.

Um, and, you know, it was ableto sort of help me prepare for, like, even the what I thought were the hardest sort of sequel questions I was given during the interview process.

Right.

Okay.

So for your second point of being able to explain your experience in a way that makes sense to PM's and executives, uh, because you may be you know, expert, Could you maybe give us an example or maybe tell us a little bit more about that?

Um, yeah.

So, um, I feel like when I was first starting to apply.

Like, I don't know why I didn't think about this, but just that there's gonna be no problem.

Managers and executives that are gonna be part of the interview process.

Um, and so um, you know, a lot of times you'll be asked, um, you know, buy them to talk about, you know, like a time that you use data to, you know, like analyzer problem or like, analyzed, like a business problem.

Um, and sort of.

When I first started interviewing, I feel like I prepared for that question, but I prepared to answer that question toe like other technical people.

So, like, I feel like I had a good answer to that, but I only had a good answer to that when I was explaining it to someone who Mike had a background in programming, had a background in statistics, and so I basically had to sort of, like, go back and figure out a good way to explain it, you know, But to someone who doesn't have that background s So how did you change the in your particular case, I mean, um, yeah.

So, um, at UC Berkley, um, I ah, researched carbon tax is a sort of the impact of them, uh, unlike manufacturing prices and so sort of in my whole life technical discussion of that.

I talked about the different models I used in the different assumptions I made, um, And when I tried to do when I shifted that, um, to sort of talk Thio, you know, product manager about that, I tried to walk them through more like a more ones, like, simple version.

But just like talking about, like, how low prices are impacted by taxes and sort of like how, um, like we went about the process of, like, trying to determine by how much s o.

I guess you focused on the end result.

More than the process itself, like, Yeah, yeah.

Um, yeah, less on.

Um, I feel like that.

Sort of, like, technical part of, Like, how we got there and yeah, more about, like, sort of like the impact of that project.

Like what?

It meat and stuff like that.

Okay, uh, let's go through the rest of the tips you had earlier.

Okay?

Yeah.

Um, yeah, s o another.

Another tip I have is just sort of putting together and showcasing personal projects.

Ah, I think that this is sort of especially important for your first data job.

And, um, when you're, uh, it's sort of like inexperienced and maybe don't have much experience outside of school.

um, and this can be something as simple as just kind of going online.

Ah, and finding like a free public data set, Um, and putting together visualizations and sort of doing your own analysis.

Um, or in my case, what I what I also did was just kind of used some connections, and I was able to, uh, convince a company toe to basically let me do sort of, like, basic data science work for them for free as just sort of like a This is, like, a little bit of experience I could put on my resume and sort of, like, talk to talk about companies during interviews.

Nice.

Um, what kind of datanalisis did you do for them?

Um, so it was mostly just, like, visualization stuff.

Um, it was a I was like a commercial real estate company that had, like, various, um, like, sale and purchase data, um, on, like, different Ah, like real estate transactions, like across the United States.

Um and so, I, um, just put, um, like a couple of visualizations together about like, um where different, like types of real estate transactions were happening.

Um, and sort of like what?

Different trends, They could see stuff like that.

All right, Um, do you have any tips on how to, like, showcase your personal projects?

Yeah, um, I think like a really good way to do it is to just sort of, like, uh, put together like a basic, um, website.

Um, Where, um you can, you know, if you're putting together, like, different types of visualizations, you can, um you know, like post a couple of them there.

I sort of, like, talk about them and then also, um, have a link thio to kowtow.

Um, like where your code is, Um, so that, um when you, um, you know, are interviewing, um And you've sent out your resume, and it has that website, you know, on your resume.

Ah, recruiters And whoever else is looking would have a chance toe.

Sort of like browse your, um, web site.

And just, like in general idea of, um, of your skills related to that, right?

Uh, okay.

So earlier you mentioned that you have five tips.

I think we've discussed three so far.

Sequel being have to explain your experience in a certain way.

And Shokhin seeing projects, what were the other two.

Um, yes.

So the other one are one of the other ones is, uh, just, um, being able to sort of, like, showcase your like business or your products ends.

Um, so you can kind of think of this as like, um, you know, being able to discuss with an interviewer how you would use data to define, like, the success of a product or whether, like, certain aspects of the business, you're doing well, um, and, you know, being ableto like understand and ask questions about, like, the product road maps with, like, stuff like that.

Um, I think that, like, your technical skills are super important.

But, you know, at the end of the day, your job is, you know, to help the company, you know, grow or scale or, you know, improve their revenue stream or something like that.

So you need to take sort of like your background and always think of it in terms of, like, more of, like, a business mindset.

So how these you cultivate your business and product sense?

Ah, I feel like a couple of ways.

One was I had found this, uh, tutorial that was on has also on Mod Analytics.

Actually, it was sort of like a like a business analytics type thing.

Um, where it was more, um, like, focused on, like a study.

Questions like in terms of like, oh, um, this metric for some.

Like, hypothetical.

Cos down.

How would you go about, like, thinking through, sort of like the process, Like trying to figure it, figure out, like, why that's the case.

Um, and I felt like that was helpful to prepare for, like, some of those case questions during the interview process.

Um, but I also feel like, um what was also kind of helpful waas like, really, Just as I was going through the interview process.

Like learning more about, um, like various startups and like, thinking through like what their goals were And just like thinking about, you know, like I can use data, you know, help answer this question.

Dr are like, Oh, I know what I could do for for this.

Um, and it's kind of like stop thinking about, you know, like data and statistics in a vacuum, and instead, think of it more as like I can do this or use this methodology to, like, answer this question.

So, like something that eso was it, like using some maps and thinking about how you might use data for them?

Yeah.

Like that?

Yeah, that's like a, like, a really good strategy.

Um, is, um, you know, like, So you do have an interview at I don't know.

Let's just let's just say like, uh, like Twitter or something.

Um, you know, you could, you know, use the app and think about, like, go.

Like I think, um, like, this would be, like a good, like, product improvement or a good feature improvement.

Or, you know, I wonder if, like, this is like, a pain point for user's.

And so you think about those questions and then think about toe answer that.

I wish I had, like, this data, I think, like, you know, this type of data would be super helpful.

Um, and just kind of Yeah.

Like having an example of, um, like a product and and and sort of thinking about ways to improve it.

And sort of how you would, um, like, define improvement Or how you would define, like, success of a feature.

Okay.

And what was the last step?

You You have um, the last one was like being really good at the basics.

Um, I think that a lot of aspiring like data science and analytics applicants, um, kind of just immediately jump into, you know, fancy machine learning and deep learning models.

Um, and they kind of forget about the basics of, ah, like how to run a good Abie.

Tests have a clean data, sort of the basics of statistics, stuff like that.

And, you know, there's definitely a place for, you know, machine learning and deep learning type stuff.

But figure first data science job you're most likely not gonna be working on, you know, that type of stuff.

Um, you know, unless you are coming out with a PhD and like, even then, um, you just don't have, you know, any experience, and so, like, there's just a good chance you're not gonna be on.

Um, you know, a team that sort of has the capability toe toe, let you do a lot of that work, and so, just like knowing the basics, um, and starting there is what I think is sort of like a really good strategy for landing your first date, a job and How do you actually become good at those basics?

I mean, in terms of, like, programming.

I mean, we talked a little bit about, like, just being really good sequel.

Um, to get better at stuff like Python and are I think you know what's really important is, um, you know, like, finding your own data or something like that and just kind of, um, focusing on, um, you know, getting better at cleaning data, getting better at, um, you know, putting together, you know, clean, nice looking visualizations.

Um, and then in terms of statistics, um, I don't really have, like, a great recommendation for, um, like a statistics course.

Um, but ah, I would definitely, you know, spend time, um, like reading about how to run a good Abie test.

Just sort of the more like stats 101 stuff that I feel like people just, like tend to forget about sometimes.

Okay, so those are Peter's five tips.

But one thing he didn't mention is that in a lot of data science interviews, they ask you probably related questions to practice those.

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Uh, anyway, I think he was always for which my videos on.

You know, if you ask me about my eyes, thank you, they are getting better.

But you know, it's taking me some time so you might see something strange or weird about my eyes, but hopefully it's gonna go away in a couple months.