How To Pivot To A Career In Tech Without ANY Prior Background (Music -> Tech POV) - w/ Shifra

I'm Naman Pandandy. This is the ready subdu podcast and in this episode feature not expert is Shiffra is shifra is a developer relations advocate at ascend.io and this is part one of my two-part conversation with her. The topic of this discussion is how to switch to a career in tech especially if you come from a non-technical background much like Shiffra did with her background in music before she became a data scientist and finally pivoted to the deell role. A common obstacle for minorities and women especially is that they have, you know, 11 out of 12 skills in the job description and they still don't feel ready to apply. What advice would you have for these people that are looking to break into not data science specifically, but really just tech roles? It can be data analysts. I know

roles? It can be data analysts. I know those are lower rungs of the ladder. Just want to quickly push back. I don't think that like analysts are lower than scientists or lower than engineers. It just feels too inaccessible. It can just be very overwhelming and seem super daunting to enter this field. do something you like rather than something you love because is this industry agnostic or does it actually matter what industry you're trying to? In line with our theme of learning from somebody that's just two steps ahead of us instead of an expert, my goal with this episode is to spotlight a path for anyone that is willing to break into the world of tech and all the salary heights and better lifestyle that it brings.

and better lifestyle that it brings. Usually my biggest advice to career switchers and people looking to learn is subscribe on YouTube and follow on Spotify or wherever you get your podcasts for weekly episodes featuring not experts from all walks of life ever and daily tips from those episodes. And now without any further ado here's Chipra. Welcome to the Ready Set Do podcast where we learn from journeys of not experts who are just two steps ahead of us. Shiffra, welcome. Thanks. Thanks for having me. So excited to jump into all of the amazing stuff that you've been doing. And we will be peeling back the layers one by one. But where I want to start is with your experience teaching people not necessarily from um you know computer science/technical backgrounds about that type of world.

backgrounds about that type of world. So, do you mind setting the stage for us just a little bit about how you found yourself doing that and then we can get into what that experience was like for you because yeah, that's where I'm really curious to pick your brain on. Yeah, absolutely. So, I've always been the type of person who works really hard to get to any sort of higher level with a skill. And I feel like the people who kind of write textbooks and rule the academic world, for better or for worse, they're the people who it comes naturally to them, right? So, in some ways, the people writing the textbooks are like the worst people for the job because it wasn't hard for them, right?

because it wasn't hard for them, right? They didn't have to go through every setback and like work really hard to get it. They just kind of into it and that's why they're at the top of their field. Um, and that's awesome. That's not me, right? So, I've been I've been teaching everything I know since I can remember, since I was basically in high school, whether it was music, whether it was math, whether it was English. And so naturally when I learned computer science and I got deeper into data and technical concepts, it's just kind of the way I operate is like I'm always trying to share that with people because I feel like you were I was in the same place as you two three months ago, whatever it is. And I understand where

whatever it is. And I understand where the setbacks are. I understand where the complication is. And I can help you get from that difficult part to the part where you understand things. Um, and so I've always just been passionate about helping people get to that moment with whatever subject it is, you know, that it's, you know, it's so funny as you were sharing that, the first thing that came to my mind was that that is actually very closely related to the theme of the show, you know, which is that as you said, the people that write those books, they are so far advanced and I'm sure they have the right intention to appeal to somebody that's just starting out, but just because of the nature of how expertise works, at least in my mind, it can be very difficult to you know necessarily be able to put yourself in

necessarily be able to put yourself in the shoes of somebody that's just opened a textbook for the first time that has no idea what Java is but is you know trying to learn that. So um so yeah and I also want to start off I guess with your experience with music because obviously as anybody I'm sure all of our listeners at this point are like how do you go from teaching music to uh you know Silicon Valley stuff necessarily.

know Silicon Valley stuff necessarily. So do you mind providing like a kind of background snapshot of your career uh starting clearly with music and kind of where you are currently just so that we can contextualize your journey so far? Yeah, totally. I will say it's it's a little bit of an era because I haven't been doing music in a while, but um I've always been really passionate about music. I think it's a hobby for a lot of us and that's part of why the field is so competitive and why people expect you to just be like kind of lucky to be there because it's everyone's hobby and everyone loves it, right? So, it's in a

everyone loves it, right? So, it's in a position where you have to be really lucky and really good to succeed. And so, I guess my story with music was I wanted to be surrounded by the thing I love. So, when I went to business school, I I decided, hey, I want to be a music manager. I want to support this industry. I want to support artists. I want to give them a good experience where this industry is known to be super cutthroat, but maybe I can kind of go in and change that for people I work with. Um, and there is this niche in the music industry that people might not have heard of. It's called artist and repertoire, shortened to ANR. And these are basically the scouts who go into clubs, who go into

scouts who go into clubs, who go into shows and listen to artists and discover them and find them and bring them into the label, bring them into that industry piece of it, right? So, this is what I really wanted to do when I was like a freshman in college or something. And so what you naturally do is you get your uh you get your degree in, you know, music technology or whatever minor is offered by your school and then you go seek out industry experience, I guess, if you're like me. That's what I did. And I ended up getting this uh unpaid, mind you, this unpaid internship in the music industry for a small indie label. And I

industry for a small indie label. And I was basically working with like really disorganized people. I'm not going to say who they were. Um, but people who didn't know how to run a business as far as like just being competent with how you handle things and how you manage your data, how you help your artists. And it was very much like, oh, we need this. Go make this video. Go organize this data. How much money do we owe this person? How much money did this cost?

person? How much money did this cost? And everything is just like scattered in random local Excel files that they just kind of blindly trusted me and didn't check any of my work even though I'm like this random freshman from a business school. That's crazy. Wow. I mean, it can be both a very good thing and a very bad thing, but I'll let you continue here. No, totally. Yeah. And feel free to interject because this is a two-way conversation. But basically, I ended up working in an environment that was like not teaching me much and not giving me high confidence about the industry as a whole. Especially if you want to work with smaller indie uh artists, smaller indie labels, this is kind of how it's going to be. Things just get done ad hoc. There aren't

just get done ad hoc. There aren't really a lot of systems in place, at least in the place I worked. And on top of that, I didn't like the the music at all. Um, and I thought some of the artists were cool. Like, I enjoyed talking to them, but at the end of the day, the music was not something I was passionate about. And gotcha. I think that's really depressing because if you're in something for the passion and then that kind of gets taken away from you, you kind of say, why am I doing this? Right. And I think sometimes that happens when you're too close to something, when you care too much about it. And that's why when people ask me, I

it. And that's why when people ask me, I advise them for your career, we all need to make money. We all need to survive. So do something you like rather than something you love because there's a point where it's too close and it's too personal. It's too meaningful. And then when that gets taken away, you don't really have anything left. That's such an interesting thought and definitely not something I have been exposed to before. Can I ask what music type this was that you know that you detested so much? Clearly, I'm just curious to know.

much? Clearly, I'm just curious to know. Yeah, it's not that it was bad. It's not that it was terrible or anything. It was kind of like uh R&B pop and the majority of music was just like covers of top 40 R&B and pop songs. Ah, gotcha. Gotcha. It's not It's not artistic. It's not creative. It's not interesting. And when people talk about like expressing yourself through music, that's not like an Ariana Grande Christmas song cover.

an Ariana Grande Christmas song cover. Right. Right. You know, or or like a covers rock band, you know, that those are just Yeah. They're just different things and yeah, so that yeah that's very cool and I'm going to you know continue to um you know nibble on what you said about do something that you like not something that you love necessarily which like I said very interesting thought and definitely will need more time to digest that but continuing then I'm just now even more you know just curious that yeah how did you start there and then yeah I guess what was your next uh career step here cuz yeah you were definitely Definitely in a completely different world at this point. Totally. Totally. So was definitely discouraged about music at this point. And then I started thinking about how in in the 2000s the music

about how in in the 2000s the music industry goes far beyond traditional labels and it's gone through a lot of changes in the past couple decades. And one of those big changes is Spotify. Um and part of going to like a really big school like I did for university is I had the chance to to go to a lot of different kinds of events even in the pandemic which is when I went to school.

pandemic which is when I went to school. Um, and so there was a data science society founded by this awesome person called Shin Naran. Um, she's incredible. And she was hosting an event with a data scientist who worked at Spotify. And I knew that I had to get into this event. I had to go hang out and hear hear what this guy had to say. And I really wasn't there for the data science to be honest with you. I was there for the Spotify, right? So I came for the Spotify, stayed for the data science.

Spotify, stayed for the data science. It's kind of the story, I guess. Um, and that's how I found out about this work. Um, and a little bit of context, I love doing AP statistics in high school. So, like in the US, we have AP tests where you can essentially get college credit early by passing a test in uh in secondary education. Y and so I did that class and it's math essays pretty much.

class and it's math essays pretty much. So, it's really not not appealing to the average high school kid. I don't think it's like the two things I hate the most. Wait, did you say math essays? Yeah. Yeah. So, you have the multip Yeah, it's kind of crazy. You have like a multiple choice section on the exam and then what's called the free response questions, the FRQs, which is essentially math essay questions. And it's it's it's the typical high schooler's worst nightmare in America to be to be honest with you. But for some reason, I really liked it. So, when I when I heard about data science in the context of like, oh, this is applying all of this like stats knowledge that I really enjoyed and basically seeking the closest thing we have to truth about the world uh through experiments and stuff

world uh through experiments and stuff like that. I thought it was really cool. So that's how I that's what impressed me about that uh event that I went to only for the music piece. Gotcha. And then once you were there, that was kind of it sounds like your first uh time dipping your toes into the tech world. Is that right? So from there, I'm I'm envisioning you self-eing yourself about data science. Is that how that kind of went down? Yeah, pretty much teaching myself and then also aligning my studies with it wherever possible. So I picked the the closest major to it. got myself into some computer sciency focused classes and taught myself. Um, and then when this person Shri who started the data science society, excuse me, when

data science society, excuse me, when she graduated, she actually asked me to take over from her. Oh wow. What a what that must have felt so good, right? Almost like a full circle moment almost, you know. Yeah, a full circle moment. And it's like when you're when you're a student, it feels so small now, but when you're a student, that's like the biggest deal. It's like, oh, you're the chosen one. Yeah, you know, you've been chosen from on high or whatever. And so I I took over the data science society.

I I took over the data science society. Um didn't necessarily do the best job to be honest with you because it was a there was a lot of like personal stuff going on in my life at the time. Um but that led me to reach out to Nick Singh who people in the data space know him. He's the guy who wrote Ace the data science interview which is the book that you should all read if you're going into data science career job hunting stuff.

data science career job hunting stuff. And after eight months of him not noticing my messages, he actually reached out to me independently and I was able to get an event with him and um we had some conversations just like planning the event as you do and he thought I was memorable. He thought I was interesting. I obviously thought he was memorable and interesting and he's been my mentor ever since. Um, and so these things if you reach for them, they just kind of compound. And like I see there are so many kind of checkpoints in this story where like I wouldn't be where I am today without Nick. I wouldn't be there without Shri, you know, and these people who who take a chance on you get you to where you are.

chance on you get you to where you are. Absolutely. And I want to double click on a couple things here. The first being um as somebody that so I went to undergrad for computer science, so I was kind of always immersed in this field from the get- go. However, I have a bunch of friends that did not and that really struggle at irrespective of what age they're at, whether it's, you know, whatever 22 or 27 or whatever. But the thing that I get from pretty much everybody is that it just feels too inaccessible, especially like you, you know, if you're not from data science or tech in in general, it can just be very overwhelming and seem super daunting to enter this field overall in general. And I do think something Yeah. And I never know what to tell them. I just give them

know what to tell them. I just give them clearly not helpful advice like, "Oh, you just need to get your hands dirty. Go on a compiler or like open a Jupyter notebook and start writing, I don't know, code to make a black uh circle white or something. It's just not useful, you know, and I I can sense that as I share that. But having done this in your life and career, what advice would you give to such people who are we can just assume any field that is not tech, maybe we can assume they have some math background. That's that feels like a fair assumption to me. But yeah, what advice would you have for these people that are looking to break into not data

that are looking to break into not data science specifically, but really just tech roles? It can be data analyst. I know those are like the lower rungs of the ladder. So yeah, any advice for these people? Totally. So, I want to answer your question, but I also just want to quickly push back. I don't think that like analysts are lower than scientists or lower than engineers or something. I think it's just I meant accessibility wise. Oh, accessibility wise. Fair.

wise. Oh, accessibility wise. Fair. Yeah. Yeah. Yeah. No, I would I'm a business analyst myself, so I would never shoot myself in the foot here by Yeah. But that's a fair call out. Yeah. That's not what I meant. Yeah. Yeah. know, just because, and not to say that you were even uh insinuating that, but just because I think in the technical community, I feel like everyone wants to look down on someone less technical than them, and I just think that's pretty stupid. So, I just like to call that out in these types of conversations. That's totally fair. Yeah. Um Yeah. So, I think that it's almost the same problem as big data itself, right? Where companies like big companies like Fang or IBM or any large uh company, they have the pla the problem of too much data. And that's the

problem of too much data. And that's the same problem for career switchers or people who want to learn a new skill because before it was hard, let's say in the 60s or something because there was no way to learn a new skill if the book wasn't at your local library, right? But now it's like you have a hundred YouTube tutorials, you have a million muks, you have a million places you could start.

have a million places you could start. So what do you actually do? And I would say that my biggest advice to career switchers and people looking to learn is understanding the field and then the tools associated with it. So let me explain. As a as a data scientist or data analyst, your job is not to be good at SQL. At the end of the day, your CEO doesn't care how efficient your query is, right? Your job is to solve business problems. And the tools available to you in that domain are SQL, Python, Excel, data storytelling. So once you understand this, the rest of the path becomes clear, right? So you need to frame everything as a business problem that has a data solution. And that is a skill

has a data solution. And that is a skill set you can learn by reading, by listening, and by uh basically just like looking at the kinds of problems that businesses solve and understanding how data helps you do that. Then once you understand that it's all about okay how do I use SQL to solve the data problem you understand how to frame the problem then you understand how to frame the solution then you understand how to actually solve the problem by using the tool at hand so then when it comes to that basically what I did and what I recommend to people is like okay you need to solve business problems with data the tools that you need to do that are SQL Python Excel let's say for a data analyst and now how

let's say for a data analyst and now how am I going to learn those things well you're going to take whatever the first MOO you find you are not going to spend three months deciding which course to take. Pick something free, especially if you're a student, whatever is accessible to you. In my case, I got lucky. I had access to LinkedIn Learning through my university account, something I like wouldn't have been able to afford probably without it. So, if you have those kinds of things, ask your academic adviser what resources your school or your context can give you. And if not, you can even go to YouTube. There are full length courses. But just find the first one that has like decent ratings, decent view count, and do it. And then

decent view count, and do it. And then once you have taken like your intro course cuz they're all the same. They're course cuz they're all the same. Let's not pretend that they're all that different. Once you've taken your intro course and you know how to like select star from table in SQL for example, then you need to do a project. And that's where it comes to what you were saying with getting your hands dirty. I think the the piece that adds on to your advice, which my Mac just did a thumbs up. Cool.

which my Mac just did a thumbs up. Cool. Yeah. It's still annoying when it does that. Yeah. Yeah. And it's like I'm not even doing it. Like it's 2025. We we have generative AI. Why do you think I did a thumbs up anyway? Yeah. Yeah. Back to the story here. Your to the back to the story here. Your advice that you're giving people is not bad. It's just missing some context, I think. And the context is okay, what is your field about? And then get comfortable with the initial tools so that you can do some basic operations in whatever your chosen tool is. Um, and then that's where it comes to getting your hands dirty doing your own project because that's where you're going to hit bugs. You're hitting data that's not necessarily clean. If you're doing computer science, you're practicing reading stack traces and understanding

reading stack traces and understanding how different systems talk to each other in uh in full stack apps, right? So basically it's understand the context of the field, learn the basics so that you can get your hands dirty and then go do your own project so that you're not just talking about your skills, you're showing. Absolutely. So yeah, really appreciate you sharing that. I think that's such a helpful framework and definitely one that I'm going to use here going forward. My one I guess follow up on that is for people that don't necessarily know what so you you remember how you mentioned that you have to first pick a problem that you want to solve right that's like step one or not that you want to solve but that you know

that you want to solve but that you know that organization wants to solve are there any you know industries in general that come to mind at first or should people maybe be just looking at the like big tech companies does that question make sense like is this industry agnostic or does it actually matter what industry you're trying to focus? I believe it's industry agnostic. I would say that it's good to pick something that you as a student are familiar with.

that you as a student are familiar with. So I think most 20somes or most college students, teenagers even are uh they're they're familiar with social media, right? So then f companies might be a good place to start and you might say, "Okay, how is Instagram recommending stuff to me?" Mhm. Yeah. How how could data facilitate that? Okay, well maybe every post is a rona table and then maybe every post connects to another table that has all the comments for that post and then maybe if there's like posts that have some kind of similarity index to each other and I've liked post one and two, maybe they'll recommend post three that's quite similar. Just think about the structures of like how how are companies optimizing things for your life that you're already doing, right? Yeah. And it's also easy to find interests from there given you probably

interests from there given you probably spent some time at least if you're like most people on whatever social media. It doesn't even matter which one. So yeah, I love that. And then also from there I guess just kind of to close the loop on this um you know this segment. Um, is there such a thing as a escape velocity in terms of your skill where you're like if I'm here that means now I'm ready to start applying for jobs or would you recommend that that just comes down the road and is not even worth something that you should be thinking about? So to make sure I understand the question, are you asking me like at what point do people know that they're ready to apply?

people know that they're ready to apply? Exactly. Yeah. Yeah. Um, I would say that it's all about interview readiness. I think applications are kind of fake at this point anyway. It's about being able to have a conversation, right? So, can you have a conversation about SQL? If you're interviewing for a SQL job, can you talk about a project that you did and understand what the hell you did? You didn't just vibe code it, right? Um, so can you have a conversation about the field such that like you would be confident in hiring yourself? So, go talk to a computer science major who's two years ahead of you. Have a conversation with them. See how it goes.

conversation with them. See how it goes. You know, network with people, have a coffee chat with people, ask them about their experience and make sure you can hold down that conversation. And this isn't performing. This is like performing with knowledge behind it, right? Um, but you need to know your stuff and perform your stuff. And if you're missing either of those things, you're not quite ready. But I will say, um, a common a common obstacle for minorities and women especially and fem presenting people is that they have, you know, 11 out of 12 skills in the job description and they still don't feel ready to apply. Whereas typically um on average a man who has like five out of 10 skills will be like, "Oh, I looked at

10 skills will be like, "Oh, I looked at this once. I'm ready." And basically, this is actually a post that I'm I'm planning to do this week on LinkedIn is like you don't want to self- select out either. So my typical barometer is like if I feel 70% ready, I'll go for it. Right. Yeah. Let the company reject you, right? You don't have to reject yourself. yourself. That's so helpful actually. Yeah. Yeah.

That's so helpful actually. Yeah. Yeah. Um, and yeah, I definitely didn't know that there was such a disparity, but that does make sense that, you know, that would exist. So, yeah, appreciate you calling that out as well. Um, yep. So, cool. And then, so now, you know, kind of going back to where we were before I dragged us down this uh random detour. Um, yeah. So, you have now gotten experience with uh data science. You have a mentor that's been really helping you. Um, where do you go from here? What's your next adventure uh at this point in your career? Yeah, totally. So, what I'm doing right now is actually a little bit the left field of data science and um a job that I started recently is I'm a developer relations advocate or like Devril for short at Ascend.io. And basically at Ascend, our

Ascend.io. And basically at Ascend, our mission is to make data engineering delightful. So, it's a little bit to the left. It's data engineering rather than data science. And the way we do that is by building a platform that gives data teams AI and automation tools and that helps them build and deploy their data pipelines faster and easier. There's a lot of super cool tech here under the hood that we could like talk about or get into another time. But if folks are curious about it, they could check out our website. They could check out our LinkedIn. But essentially, all this community building and all this career advice and all of this trying to give back to the community and pay forward my knowledge has led me to this role. And it's it's it's very interdisciplinary,

it's it's it's very interdisciplinary, right? So it's the data piece, it's the community building piece, it's the talking about things at technical education piece. And I think one of the best things about approaching your career in this way of paying it forward is that it does open more doors for you and you will be the best communicator, you know. So it has a lot of benefits.

you know. So it has a lot of benefits. So just want to make sure I'm understanding this cuz this is so cool. Yeah. I didn't even know such roles existed. But you're essentially uh trying to talk to the developers of the world and put forward what uh not necessarily this but in the area of what SN.io brings to the table when it comes to those AI data engineering pipelines. Is that somewhat right or did I butcher that? Yeah. No, it's it's pretty much exactly right. I also will say we do we do call ourselves ascend for short. So if you want you can say that ascend.io is also fine. Awesome. Um, it's about explaining where Ascend can kind of solve your pain points. And that's also how I frame all my content surrounding this, right? Because I'm not a saleserson. I'm not here to make a deal

saleserson. I'm not here to make a deal with you. I'm here to say, "Hey, this part of your job sucks and Ascend is going to help it not suck." And that's that's both how I feel about the product and why I genuinely like it. And also how I want to frame it to the community because like I'm not here to make a sale. I'm here to help you and help you solve your problems. Totally. Yeah, that's such a fresh outlook. And I was also just reading this post, which I will probably butcher if I had to paraphrase it, but it was something along the lines of um the best sales pitch is not a sales pitch, you know, and like the best marketing never feels like marketing. And yeah, it sounds like you you're getting the chance to do that a little bit, which which is really

a little bit, which which is really cool, honestly. And yeah, I'll have to, you know, look more into that just because it sounds something that's, you know, I I feel really uh interested in. But separately, I know that now that road here did also take you through this point where you were writing, I believe it was technical documentation as well as I think teaching people in some way or educating them about computer science and or programming languages. So yeah.

and or programming languages. So yeah. Yeah. Can you share a little bit more about that aspect of your journey as well? Totally happy to talk about it. It's my favorite thing. So, thank you for asking. Um, and I also am doing a lot of that now. I am I'm also largely responsible for the technical documentation at Ascend. So, just wanted to highlight that that that piece has also helped me get to where I am now.

also helped me get to where I am now. Awesome. Um, but yeah, so the first big one is I mentioned Nick Singh briefly earlier and Nick as people know is the founder of data lemur.com which is a SQL and data science career prep platform. I'm not sponsored by them. I love them and I still think that we're probably the best, if not one of the best in the space. And Nick called me up and he said, "Hey, I really liked working with you. I like the way you phrase things. I like the way you write about things."

like the way you write about things." Come help me build my startup as a college student. Which was super cool. Yeah, especially because he's your mentor. That that's like the biggest vote of confidence, right, that you can get from somebody that's your mentor. So that's awesome. Yeah, totally. Again, when you're a student, it's like feeling like the chosen one. It's kind of crazy. Um, so yeah, I spent I want to say about six months like really working every week part-time on this and my role was essentially writing uh writing solution explanations for the platform. So we would have people solving SQL queries from top companies and then I would be explaining the solution step by step.

explaining the solution step by step. And then the really cool part of data lemur is having these kind of uh leading question hints that guide you to the next step without telling you the answer. And was this happening live or were these kind of like recorded videos in in pre in advance? This was um technical writing. So this was like me writing content that went on the platform. Okay. I gotcha. I gotcha. Wow. But I think it was Yeah. Go ahead. No, just that. So you would have to read all of the questions, think about where somebody might trip and then pre in advance prepare for that by writing stuff that would help somebody crack that. Is that kind of right? Yeah. And the the way to do it efficiently is you

the the way to do it efficiently is you write the full solution explanation and then you break that up into pieces and then hint toward each of those pieces. And sometimes it was like one or two hints for the easy, you know, 100 level questions. But things got to a hard level where we had recursive comment table expressions which are like a very niche thing in SQL. We had uh we had things that have like eight intermediate steps between the the question table and the final answer table. And so the more steps you have and the more kind of systems thinking you need to do around how are we going to get from A to B, the more hints you'll have. Um got it. So, it's just I thought it was a really fun

it's just I thought it was a really fun way of doing things and a really interesting style of writing of like how do you know how to give someone just enough without robbing them of the experience to learn. Exactly. Yeah. And so much of the experience hinges on that. I would actually go so far as to say that all of the experience is that you know it's like a video game that you're playing that if it's too easy then you're going to drop it but if it's too challenging you're also going to drop it. So it has to be just right. So yeah, I am curious. What were some of your biggest learnings from doing this?

your biggest learnings from doing this? Did you was there such a thing as you where you they could give you feedback in terms of how they felt each explanation was and such? Just curious. Um yeah, I think it was very much startup mindset of like done is better than perfect and just get it out there and like if there's a typo, we'll find it later. And this is before chat GBT really took off too. So you're actually writing, you're actually knowing stuff and writing things. Not that using AI means you don't know anything, but it's just a little bit harder without it. For sure. And time consuming, right? Yeah.

sure. And time consuming, right? Yeah. Yeah. Exactly. So, you're spending time. Um, the biggest learnings I think were exactly what you just said of finding the sweet spot where this is still educational, but it's also motivating and it's also helping you along. So I think learning how to speak in that way has helped me a lot because um we can get into this separately and I won't go too deep into it now but essentially a big part of being a data scientist or analyst is knowing how to hit that sweet spot of explaining something to an executive or a stakeholder who is not as technical as you in a way where they understand everything that's important for them to understand to help make a decision and they also don't feel like you're talking down to them or condescending to them or making it too

condescending to them or making it too far out of reach that they're falling asleep in the meeting. Right. And so communication is all about finding these sweet spots. So data lemur was great for that. Um it was also great in terms of just like exposure to technical things.