[DataFramed Careers Series #1] Launching a Data Career in 2022

Hi, I'm Maggie, VP of Curriculum at datacamp, and I know firsthand how overwhelming it can be to keep up with the rapid changes in data and AI. But staying ahead doesn't have to be hard. That's why our curriculum team has curated the best learning content to help you and your team stay ahead, covering everything from data and AI literacy for busy executives to cutting edge generative AI for developers, and everything in between. Whether you're upskilling yourself or your team, we make learning practical, engaging and flexible. It just works. Power your data and AI transformation journey on datacamp.com. You're listening to Data Framed, a podcast by Data Camp. In this show, you'll hear all the latest trends and insights in data science. Whether you're just getting started in your data career or or you're a data leader looking to scale data driven decisions in your organization. Join us for in depth discussions with data and analytics leaders at the forefront of the data revolution. Let's dive right in.

Hello everyone, this is Adele, Data Science educator and Evangelist at datacamp. In case you missed our previous announcement, today is a bit different on the Dataframe Podcast Today marks the first episode of a four day series covering the ins and outs of building a career in data. One thing I always get asked about by practitioners and aspiring practitioners is how to stand out from the crowd, especially in a tight hiring market. There's a lot that's expected today out of candidates, whether junior or senior. Building a tailored resume for data roles, developing portfolio projects, creating a personal brand, and of course actually making it to and doing well on the interview are table stakes when it comes to making it to the finish line for a lot of data roles today. So for this four day series I wanted to interview a set of experts and thought leaders on exactly these topics and our first guest today definitely has a lot to offer. Sadie St. Lawrence is the Founder and CEO of Women in Data, the number one community for women in AI and tech. Sadie has trained over 350,000 people in data science throughout her online courses and has developed multiple programs in machine learning and career development. Sadie was named one of the top 10 most admired business women to watch out for in 2021 and has been listed as a top 21 influencer in data. Throughout our conversation we covered data science resumes, the different types of data, career paths available, how to approach mentor, mentee, relationship, portfolio projects, combating imposter syndrome when sharing your work, and much more. If you enjoyed today's podcast, you will definitely enjoy this week's remaining three episodes that go into much greater detail around portfolio projects, the Data Science Interview, and building a brand within data through writing. If you enjoyed this episode, make sure to rate the podcast and leave a review. Also, I'm pleased to let you know that Sadie will be joining us on Datacamp Radar Digital Summit on June 23rd. During the summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there's definitely something for you. Seats are limited and registration is free, so secure your spot today on events.datacamp.com radar the link is in the description. Now onto today's episode. Sadie, it's great to have you on the show.

Oh, it's great to be here. Thanks so much for having me.

I'm super excited to talk to you about breaking into Data Science today, how aspiring data practitioners need to think about their career paths, best practices to stand out in a competitive space, your work, leading women in data, and much more. But before, can you give us a bit of a background about yourself and how you got into the data space?

Yeah, I would love to share. So I came into Data career in 2014. At the time I was working in a neuroscience lab with the plans to go and get my PhD in neuroscience and soon realized I really loved the analysis side of things and didn't so much enjoy taking care of rats and then unfortunately having to kill my rats at the end of using them and that was quite a bit discouraging. And so what I did was I stepped back and looked and said what parts of my job do I really love and what parts could I do with that? And what was left again was the analysis, the scientific method. And I was lucky enough to find the term data science. And when I found the term data science through a Google search, I immediately latched on. I was just like, yes, this is me. This is like everything that I want to do and want to be. And so I quit my job at the lab within the next few days and was like, okay, I just need to get a job working with data in some way. And so I started off as a research analyst, then I started taking some courses through some MOOCs, realized I really loved it, and then decided to go and get my master's in the field as well. And that started just a really exciting journey where I was able to be a research analyst and then an analytics engineer and then a data scientist and then I was able to lead a data science team and Then I went into AI strategy. So I've had a really fun journey in this space. And then now today, I get to do what I love the most, which is to lead women in data, help coach others, and build pathways for diverse audiences to get into this space.

I love this story and I'm very excited to unpack a lot more of your journey, but there's definitely a lot to discuss today when it comes to breaking into data science. When I first joined the industry, and that wasn't necessarily that long ago, you'd only see two main roles, right, to hire for data analysts or data scientists. And this is in some sense still true today, but we see a lot more variations, specialization between these roles. We have the emergence of hybrid roles like financial analysts that require more data skills, marketing ops, biz ops, and even business intelligence roles. So as an educator, you're someone who's been embedded in this space for a long time. What do you think are the different types of data careers available for aspiring practitioners looking to break into data today?

Yeah, great question, because a lot has changed since 2014 at this phase when I first entered. So on the positive side, there are so many more resources for learning today. So when I was getting into the space, I'm in the us there were only five universities even offering master's degree at the time. So I just share that because if someone has been interested getting a master's or going through formal education, they'll know the plethora of resources and options available, let alone the courses available through. I don't even know if Data Camp existed at the time that are available through private and online education. That exists as well. So I think it's really exciting that there's so many resources available. But the hard part now is today is exactly what you mentioned. There's so many more jobs in this space and now they're getting a little bit more specialized. So one of the things that I see is people are looking not just for a data scientist or an analyst or a data engineer, but they're looking for someone who has those skills and also has the industry skills or the business function skills as well. Right. So as you mentioned, it's really important for people not to just say they want to be a data scientist, but what type? Right. Do you want to be a product data scientist? Do you want to be a financial data scientist? Do you want to work at a consumer goods company, like, really narrow in on the industry you care about? Like, healthcare is, I think, a really exciting place to be because, well, one we've seen how important health is in the last two years of the pandemic and how important data is in this space and the models that we build how many lives they can save. So I would say make sure if you're looking to get into the space, you're not only learning those technical skills, but you're learning those business skills as well, whether it be from an industry or a function. And a job function means is it a marketing side, is it a financial, is it an operations side of things? I think if you put those two combos together, you'll have a really clear brand story that will make it a lot easier to be able to break into the field.

That's really great. And in some sense this creates an easier career pathway into data science because if you're a marketer, a financial analyst, or someone who has the subject matter expertise, you just need the technical expertise on top of that to break into data science.

Exactly. And it also really helps to distinguish you as well with the crowd. So it's just a win, win.

Overall, how do you assess the importance or the trade off to a certain extent between these business skills and these hard skills? What do you think are the most important skills in that skill mix?

Oh yeah, that's a hard question, right? Because both are important. And so that doesn't really answer your question of one versus the other. What I would tell people though, is if you need both of them, how do you balance learning both of them on your learning journey? And I like to use for people is pick your wave, but know your ocean. And what does that mean? Well, the ocean is a very vast place. Right. And that's a lot of times what a data career can feel like. Even if you're just focusing on data science. There's all these skills you need to learn from data cleaning and data handling and data governance and data engineering. And then you get into the analysis side and the machine learning side and the data visualization side and communicating all those skills, that's enough just in and of itself on the technical side of things. And now you're saying, Sadie, you're asking me to also learn these business skills like how do I do it all? And that's where the knowing your wave comes in, right. Of having a really clear vision for where you want to be and end up. And so I'd say on the business side of things, really make sure you're taking the time to talk to people who already work in that role, making sure you're not just reading the technical articles of what's going on in business, but also just the broader business scope of things. And so for me, one of the ways I really like to understand businesses is to read through their website, but more importantly, if they're a public company, read through their financials. And so I think that's the beauty of a public company is when you look at their financial statements, you really get an insight view into how do they make money, how do they lose money, what are the products they're trying to sell. And at the end of the day, understanding business is quite simple, right? It's how do we make money so that we can continue to grow and support our employees and support the customers that we're servicing.

And you mentioned here something in your answer around communicating your brand or communicating the technical skills that you have. How important are communication skills and data storytelling skills as a means to break into data science and jump out and stand out from the crowd?

The analogy I like to use is like a music box. So if you've ever seen a music box, if it's closed and just sitting on the table, you never actually get to hear what the beautiful sound is inside of it. That's similar in terms of data scientists not having communication skills. They may have these amazing skills, but they're all locked in this box and then no one ever knows. And so you have to open the box. And how do you open the box? You open the box by being able to tell those stories and to communicate those skills. So it's really up to you, right? Do you want people to hear your story and to hear your amazing skills and ability? Well, then you're going to need the communication skills that you can open the box and that can be told.

That's great. And you're someone who's, in my opinion, a great communicator, and that sits at the intersection of technical skills and communication. How did you grow your communication skills over time? I know there's some form of it that is innate, but I'm sure you've gotten better at it over time. What was the way that you've been able to get better at it?

I would say take every opportunity to use those communication skills. So I know early on in my career it can be daunting to say, yes, I'll lead this presentation or I'll present a portion of this. Right. But one, take any opportunity that presents itself. And also if there aren't any opportunities that present itself, volunteer yourself to be able to lead that communication, because it really is a matter of practice. The other option is we live in a digital world and we have these great tools of social media through Twitter or through LinkedIn that are readily available for all of us to just start to write and communicate. And that is such a great option in terms of one practicing. But more importantly, as you go through that practice of communicating, it also helps you to refine your process and your work. So I would say practice makes perfect and take every opportunity and seek out opportunities to communicate the great work you're doing.

That's awesome. I couldn't agree more, especially on taking that leap of faith. I think there's never been more interest in a data science career as a career path. Today there are a lot more learning resources, as you said, a lot more organizations opening up data science departments, more data skills and combination of business skills and data skills that are needed. This means that the demand for data roles is higher, but the competition is also getting higher. So what would you think are top principles for standing out in the job market today for any aspiring practitioner?

First, I would say I think it's great there's this momentum and so much interest in a data career because the forecast of the opportunity in this space is looking really, really well. So the World Economic Forum produces this job report that predicts the top jobs over the next five years. And so in 2020, they predicted again for the next five years. So that goes through 2025. And in the top 10, three of those top 10 jobs were all data careers. Machine learning engineer, data scientist, data analyst. And I think it was a big data specialist. Right. So the opportunity is really, really great in this career. But you're right, it can feel like there is a lot of competition in this space because unfortunately I hear from people a lot of times, like, I took this class and no one's giving me a job right away. And so what some of the factors that I see as an issue with that is companies are really in need of people who not just have the education, but have the experience. They need to know that, hey, right away we're strapped for time because we don't have enough resources. We know that we can put you into this role and you'll automatically be able to succeed because you have the experience more than just the education. So for people out there who are in the catch 22 of like, well, I'm trying to get the education right, I'm trying to get the experience. That's why I'm applying to these jobs. What do you do? How do you solve that problem? So this is where building projects and building a Portfolio works really well. This is where volunteering for organizations where you can use these skills can help build that progress. And then lastly, this is where those communication skills come in of sharing your work. Right? Because as you're building out your project portfolio and you're sharing what you're doing and your journey online, the right person is going to be able to be attracted to you. So those are really kind of the two tactics that I would take right now in this space.

I couldn't agree more. I love every single point you mentioned. One, from building a portfolio project, sharing your work and even putting yourself out there and getting that experience and volunteering. So of course, when it comes to the practical side, as we mentioned here, breaking into data science, we need to talk about resumes, portfolio projects more deeply and also sharing your work, building a community. So I'd love to first talk about kind of resume tips. Right? How would you structure a resume for a data role?

Yeah, I'm glad you're asking this question because just two weeks ago I was reviewing a couple people's resumes and giving some feedback and I was like, I think I'm going to create a post from not bad to pizzazz for a resume. Right? Because that's usually what I see with resumes is it starts off, it's not bad, but how do we make, how do we get you to really shine out? And so I think that there's a couple key factors to remember. The resume is not supposed to be a word dump of everything you've done and a linear journey through your career. The resume should tell a story, and it should tell a story for the target market that you want to get in. Does this mean that you should lie on your resume or put things that aren't there? No, but what you want to do is you want to shape your resume in a way that highlights the key attributes that you have done for the job you're looking to have. And so why is this important? So let's say you're going for a marketing data science role, right? You want to make sure that when you're putting out your experience and your education, you're pulling out just the portions that really relate to that goal. Why? Because people who get resumes have thousands of resumes to go through. And so you want to make it as simple as them, as simple for them as possible to be like, yes, this person has the right skills. You don't want the person reviewing your resume to have to go through and try and dig and see, oh, I saw a little Bit here and a little bit there's a. So one thing I would say is have a really clear vision of the role that you are going after. Right. Again, not just a data science role. Focus on an industry or a business sector and then craft your resume as a story that's going to tell a story of why you're the perfect person for that role. The biggest thing I see with a resume is people don't have a clear vision for what they're going after. They're just throwing all their skills out there, their experience out there, and throwing it to the wind and hoping that something sticks. So prior to drafting that resume, get really clear on what that role is you want, and then pull out the portions of your experience and your education that apply to be able to tell that strong brand story.

That's really great. So let's kick it out through an example. I want to be a data analyst in the healthcare space. I have a few experiences here and there, maybe not in healthcare. A bit touching data. I've learned a lot of data projects. I've done a portfolio of projects on healthcare data. How would you structure a resume for a data analyst going into healthcare, for example?

Yeah, so this one, because it's a technical role, you definitely want to have your technical skills at the top, right? So this is a role where you're not going to be managing people, you're going to be an individual contributor. So you want to show right away, here's my technical skills, right? So I have a bullet point. I know Python, I know SQL, even putting in some of the libraries that you may have used and what you're familiar with, and then right away go into your experience, Right? So on your experience side of things, you may not have worked in the healthcare space, but I bet you've worked on problems that are similar to what you would work on in this healthcare role. So what you want to do is pull out those problems and shape that story in a way that's going to apply here as well. And so that's going to be really helpful in terms of just making it easier for the reviewer to read. Okay, yeah, maybe they worked in a consumer goods company before, but I can see how this now applies to the analyst role as well. And then finally, I usually end with the education side of things. And the education can go a couple of ways. People often ask, should I put all of the additional education I have on my resume? This depends for me, in terms of whether you already have a bachelor's or a master's degree, if you already have those things. The additional education you've done should come through in the skills that you have, right? Not your bachelor's or master's. If you don't have the bachelor's or master's, definitely add that on there because I think it's going to show that, hey, you've still done education, maybe in a different avenue, and that's okay. But I think it's just important to know it's one or the other, but.

It doesn't have to be all that's really great. Moving on to the second element of breaking into data science here, which is like portfolio projects. What do you think are some of the most important aspects of creating a portfolio project and what do you think makes a great portfolio project?

I think the thing that makes a great portfolio project is a subject that you are interested in. So one of the best ones I saw was someone did an analysis. They were a big movie buff and they did analysis of all the movies that they watched over the last five years and they categorized them into all these really fun categories based on like how long the film is, who the director is, how many were Marvel films and told just a really interesting and fun story. And they did it in a fun interactive dashboard. What I loved about this portfolio project was you got to see their personality. And I think that's really important to remember too. As we're to going, if you're trying to break into a role is let your personality be seen because you're going to then find the right fit in culture, right? If you're really showing who you are and who your personality is, you're going to attract people where you're automatically going to fit with. So I would say one, find a subject that you're really interested in and something that you're going to be passionate about when you're communicating those results. And then secondly, find creative ways to tell that story. So you can definitely add it to a GitHub page. You could create a medium blog post. All of those are great, but maybe you go the extra mile. Maybe you make a fun little app that people can use to filter through the videos. Maybe it's an interactive dashboard, find creative ways to tell that story. And I think that's really what will make your portfolio project stand out.

I love this, especially on the authenticity and having a great genuine interest in the subject. Nick Singh, who I interviewed as well on the podcast on Acing the Data Science interview, mentions this as the halo effect. If you are genuinely interested in a topic, people will gravitate towards you, and they will be able to soak in that genuine authenticity and that interest and that enthusiasm that you have for the podcast, which will translate for a much better interview experience overall.

Yeah, I couldn't agree more. I think so oftentimes if you're trying to break into the field, you can just feel like, I just want my first chance. Right. And so you're willing to just do whatever to get that first job. But what I would say is don't lose, don't neglect that. Like, you really want to care about the culture of the team that you're going into. And the only way to do that is to share who you are so that they can see if it's a good fit.

I completely agree. What do you think are key mistakes people make when creating a portfolio project?

I would say doing what's already been done. So there's a lot of fun memes out there. I think it's like a golden retriever sitting next to like a werewolf. Right. And the golden retriever has like an Iris data set. And then the like, werewolf picture is like real world data.

Right.

It's like a classic meme.

Yes.

And it's so true. Like, we all, like, this is why memes are so great, because, because we see it and automatically get it. But I think also more importantly, not just in terms of why this mean is so great, but it's in terms of like the complexity of the two different data sets. But you know, we say, like, the iris data set, it's so overused in terms of what people have done with it. So again, when you tap into what you're really interested in, you'll find more interesting data sets, right. Maybe you'll use your net for. Maybe you use data from your Apple Watch or your Health tracker, right? Like, maybe you're really interested in art and you start to analyze, like, NFT art purchases and what's trending in the art market. Like, go into what you're interested in and stop doing what everybody else has done. Kaggle is a great place to find some free data sets and get started. And I think that's a great place to practice, but in your portfolio, it really needs to be. And so I would say the biggest problem or mistake that people do is just not make a unique portfolio.

So the last thing that we mentioned when we were talking about principles for breaking out from the crowd is sharing your work, building community around you. I'd love to anchor this, actually, in your experience launching Women in Data. I had an amazing time preparing for this podcast, learning about your story, and I found it to be a great testimony for the power of courage and community. So do you mind expanding on how you first launched Women Data and kind of that story and how it led you to where you are today?

Yeah. So at the time, I was working full time as a research analyst, and I was also doing my master's degree full time. And obviously it was very busy doing both those things full time. But I felt very lonely in this process. Right. I felt like I didn't have people I could truly connect with to discuss ideas, to collaborate with. And it was really that need for belonging and connection that led me to start Women in Data. And it really just started with my own personal need of community and then a broader vision for more equality in the space. So, unfortunately, in my master's program, there was 30 people in our first cohort, and there was only myself and one other woman in the program. And. And so I really just felt the need to connect with other people like myself. And so Women in Dana started with a meetup group in my local city. I thought that there was going to be a great attendance and everybody was going to be excited about this thing happening. Unfortunately, as the time got closer, no one had showed up, and I was feeling very discouraged and really just wanted to pack my bags and go home. And thankfully, I decided to wait 15 more minutes after the cert time, and one person came rushing in the door, and she brought three other people. And so that was really the birth of Women in Data. And I think it also just goes to show you don't need that many people initially to connect with. Right. Like, just finding one or two people is the start of something. And today, you know, Women in Data is a community of over 30,000 people, and in 30 countries and 50 cities across the world. It's really, truly incredible when you just put that call out there to say, hey, let's connect, let's grow, let's lead. How it may take time, but eventually, with some tenacity and dedication, it will grow.

I'm really in awe about this story because the psychological barriers of getting over that discouragement and keeping on the journey is super impressive to me. Kind of. What are some of the lessons that you can share when mustering the courage and the 42 to keep forward and fostering a community of peers and mentors that can help you grow?

I really look at courage as a muscle. Right. It's something that we have to practice and we have to strengthen. And so I think we all need to strengthen our muscle of courage so that one, we can put our true selves out in the world. We can let our ideas be heard. And so how do you get started doing that? You start with small steps, right? You start by raising your hand and speaking in that meeting. You start by volunteering to do that presentation. You start by taking those small little steps of courage. And what happens is, when you take that first little step, and it wasn't as terrible as our mind leads us to believe, of all the fears and bad things that will happen, we're able to relax and take a bigger jump. And that's truly what has happened to me. It was just a small step to say, hey, I'm going to start this and see if anyone wants to show up. And a few people did. And so that first step of strengthening that courage, muscle is key. But then, more importantly, I would say consistency and tenacity really plays a role in here. I think a lot of people are familiar with the hero's journey, and it's this arc of highs and lows. And I think it's a really beautiful story and also very applicable to all of our lives. And that, okay, you strengthen your courage muscle, and there may be a little high, but you must keep going on, because there may be some lows in between that process as well. And so it's important to have that tenacity and to have that dedication and discipline. And that only comes from having a vision of what you're looking to achieve. And so to be able to have that courage and to go through those hard times, it's really important that you have a vision of either your future self or a vision of what you're looking to create, because that will carry you on through those low moments as well.

That's really great. I couldn't agree more. You're someone who, through women in data, have had both mentors and have mentored a lot of people. How should aspiring practitioners treat mentor mentee relationships? Make sure that it's very useful for the mentor, but they're also really benefiting from that relationship.

Yeah. So I would say the first thing is to look at the mentor as a relationship. And I'm so happy that you use that word, because I think a lot of times, well, everyone knows mentors are important, and there's so many people who want to be able to find one. And so I like to give people some advice of actually, how do you first find a mentor? Well, that starts by just building relationships with people. But how do you do that? You do that through conversations, through finding commonalities. Right. And creating connection. Most all of my mentors have been very organic. Started by building a relationship with them through having that commonality, that common connection. And then as that relationship grows, a lot of times you just naturally enter into a mentorship and halfway through you go, are you my mentor? And they go, are you my mentor? And it happens very organically. Right. And that's the best case scenario. Right. Is where those connections happen organically. And so I would tell people, stop focusing so much on finding a mentor, but more on building relationships with people that you really admire. And I think if you have that mindset, it takes a little bit of the pressure off of it. And then when you get into that mentorship, some of the things that you can do. I've heard people say, hey, you need to be of use to your mentor, like maybe help them out or volunteer. And that's good. I think if there's that opportunity that presents itself, you definitely should. But for me, why I mentor people is because nothing makes me happier than seeing them grow and seeing the change. And so the best thing that you can do for your mentor is to work on yourself. Because when they see that the time and energy and the advice that they've given to you is making a difference, they are going to be so happy and they're going to want to pour more back into you. And by working on your self, what does that mean? Show up to your meetings on time, do the things they asked and the homework, come in with questions and be prepared. They're very simple things, but it will show up for the mentor and they will be happy to give you more once they see that it is paying off and they want nothing more than to see you succeed.

I love that. And especially at the end when you mentioned, like, doing the homework, I think nothing makes a mentor more happy than seeing that their advice is being actioned. And that's what makes it worth it for the mentor themselves. Given that also your work as a community organizer and that you've put yourself out there, whether in women in Data or on social networks, how do you approach the imposter syndrome a junior practitioner may have? Right. When sharing their work?

Yeah. So I would like to clarify for people that the imposter syndrome never goes away. It just changes. Right. So I'm not here to discourage anyone, right. To be like, oh, I'm just trying to break in the field and I have imposter syndrome. Oh, don't worry, you'll still have it, as you still Move up in your career and lead. You may even have more of it because there's more responsibility on your shoulders. So how do you make friends with your imposter syndrome? That's what I like to do a lot. Like, how do I look at that and really not use that to limit me, but use it as a way to build my courage muscle. And so I think imposter syndrome can be a great thing because it brings up for us where our fears are and where we need to work on our courage to dive through. So if you have a fear of sharing your work online, start with small baby steps. Start with having a goal for yourself to maybe just post once a week. I know people who, when they started posting too, it was so scary for them that they said, hey, I'm going to post, and then I'm not even going to look at any of the results. And maybe that's how you have to start. Don't check back every 10 minutes. See, did somebody like, did somebody comment? That's a good starting point of just put it out there. And then as you start to do that, right. You'll realize, oh, it's not as scary. There aren't as many trolls out in the world as we think that there are. Right. And actually people, actually people are rather kind and supportive. And so once you start to get over those first barriers, then you'll be able to do it more. So my advice is use your imposter syndrome to see where you need to strengthen your courage, set small goals for yourself and stick to that consistency and eventually you'll be able to break through that barrier.

Yeah, I couldn't agree more. Definitely imposter syndrome doesn't go away, but I love how you frame it. Being friends with your imposter syndrome and using it as a tool to push you forward, that's something that I find struggle with as well. You know, I host the podcast here, and imposter syndrome is still something that I struggle with. Given your experience as a community organizer as well as someone who's worked on kind of increasing diversity and equity in data science, I'd love to understand from you if I'm an applicant, right. And I'm from a minority group and I'm applying for a job and I'm interviewing with a company, how do I understand what are questions I need to ask to understand if this, this is the type of organization that will lift me up or I will have to fight much harder than male counterparts, for example, to be seen equally, it's less.

About the questions and how you feel in the situation. And why do I say this? Because I haven't met a company who's going to come out and straight up say, we don't support diversity and we're not inclusive. No one will ever answer that question that way. And thankfully. But what happens is sometimes they may say, yes, we support it and we do all those things, but their actions are different than their words. Right. And that's a very discouraging thing and something that we want to limit. And so how do you get away from that? You really look at their actions and how you feel based on how they're treating you in the interview. So I tell everyone this going into interviews, they're not just interviewing you, you're interviewing them. How do they respond to your answers? Do they respond in a collaborative way and say, yes, did you think of this? Or is it in a closed, aggressive way that doesn't make you feel good? Right. And feel free to take the insight you're getting back from them, not as you did something wrong, but insight into what is the culture of this organization. So I would say less of, like, asking questions and more of being aware in the interview to those small, subtle body language and tonal things that will give you insight into what that overall culture looks like.

I couldn't agree more. Culture is such an important aspect of being able to succeed within the organization, and that will, regardless of your skills, if you feel like you have to fight twice as hard to get those skills out there, that's an uphill battle that I don't advise anyone to want to have. And that's why I love your perspective here on being able to measure the companies throughout the interview process to be able to make that decision. So now, Sadie, before we wrap up, I'd be remiss not to talk about future trends that really shape the future of the industry and how we think about data jobs. So what do you think are some of the trends aspiring and current practitioners should be on the lookout for as they grow in their careers?

Oh, I'm so glad you asked this question because I do love to talk about the future, and most of the time I'd rather be in the future than here, but it's important to be in both places at once. Right. So, yeah, I think there's a couple of key things I think if you're. One of the things that I'm most interested in is how blockchain technology is going to change data careers. So at the core of what blockchain technology is is a database Right. It's transaction and record. What makes it so special is that it's decentralized and from the decentralization we can reach this consensus. And so there's a lot of great things happening in this space and applications of this through now web3. And this will change a lot of how businesses operate. And it's really important for data professionals to be aware of this because how businesses operate then changes when where you get the data from, what those streams of business operations are that you're looking at. And so I think it's important for data professionals to not keep their head in the stand, which is machine learning models and data visualization, but to look a little bit further out of the broader industry. And so I would take a keen look into web3 and into blockchain technology and as a practitioner in the space, I would be someone who would be encouraging the use of this at my organization. Because one of the most beautiful things about blockchain technology is it is time stamped and verified. So what happens to this data? It's very clean data and nothing makes a data scientist sing more than having very clean and accurate data where it's immutable. Right. You know what that record was, what happened. So if I was a data scientist, I would be wanting to have my organization use this technology because that's going to make the work I do a lot easier in terms of the cleanliness of the data that I'm able to work with.

That's really awesome. And harping on a practical side, if I'm a practicing data scientist now and I want to learn some techniques or try to become much more aware of blockchain technology and Web three, what are technical skills I should learn?

Yes. I would first start, before you go into the technical skills, is start with just an awareness of where the industry is at today. So there's a lot of great webinars happening. Women in Data right now is doing a whole series on web 3, the applications and what this means to data professionals. But I'd start just kind of with a broad awareness of just getting your head around this technology and the applications of it. From there, what you're going to want to do is similar to data science where you want to pick a language of like, are you starting with Python? Are you starting with R? Don't do both at the same time. Like just stick to one and get good at one is you're going to want to find a chain that you want to use. So blockchain is one chain, but there are actually hundreds of chains out there. There's Hedera Hashpack or Hashgraph, which is a chain. There's lots of different chains that you can work with. So it's similar to data science in that space of, like, don't try and do it all at once. Just pick one and understand how a smart contract works, how a token works, and then from there, you know, you can kind of go wherever you want.

Finally, Sadie, as we close out our episode, do you have any final words before we wrap up today?

Yeah, I would think I would just say to all the listeners is stay curious and don't be afraid to start with a blank page, a blank notebook, a blank canvas. Start with something new and create the new yourself to let your true self be seen, because that's really how you're going to find the career that brings you the most joy.

That's really awesome. Thank you so much, Sadie, for coming on Data Framed.

My pleasure. Hope to talk again soon. You've been listening to Data Framed, a podcast by Data Camp. Keep connected with us by subscribing to the show in your favorite podcast player. Please give us a rating, leave a comment, and share episodes you love. That helps us keep delivering insights into all things Data. Thanks for listening. Until next time.