Episode · 8 months ago

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


Today is the start of a four-day careers series covering breaking into data science in 2022. With so so much demand for data jobs today, we wanted to demystify the ins and outs of accelerating a career in data. In this series, we will interview a diverse range of thought leaders and experts on the different aspects of standing out from the crowd in the job hunt.

Our first guest in the DataFramed Careers Series is Sadie St. Lawrence. Sadie St Lawrence is the Founder and CEO of Women in Data, the #1 Community for Women in AI and Tech. Women in Data is a community of over 20,000 individuals and has representation in 17 countries and 50 cities. She has trained over 350,000 people in data science and is the course developer for the Machine Learning Certification for UC Davis. In addition, she serves on multiple start-up boards, and is the host of the Data Bytes podcast.

Sadie joins the show to talk about her career journey in data science and shares the best lessons she has learned in launching data careers.

Throughout the episode, we discuss

  • The different types of data career paths available
  • How to break into your data science career
  • How to build strong mentor/mentee relationships
  • Best practices to stand out in a competitive industry
  • Building a strong resume and standing out from the crowd 

[Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this 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

You're listening to data framed, the 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 you're a data leader looking to scale data driven decisions in your organization, join us for indepth discussions with data and analytics leaders at the forefront of the data revolution. Let's dive right in. Hello everyone, this is a dull data science educator indiventelist at data camp. In case you missed our previous announcement, today is a bit different on the data frame 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 the 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 too and doing well on the interview or table sticks 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. CD St Lawrence is the founder and CEO of women in data, the number one community for women in AI and tech. Sad strained over three hundred fifty thou 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 ten most admired business women to watch out for in two thousand and twenty one and has been listed as a top twenty one influencer in data. Throughout our conversation we covered data science resumes, the different types of data career paths available, how to approach mentor Minti relationships, portfolio projects, combating imposter syndrome when sharing your work and much more. If you enjoy 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. For 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 data camp, radar, or digital summit, on June twenty three. During the summit of 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 to data campcom slash radar. The link is in the description. Now on to 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 I'm super excited to talk to you about breaking into data science today, how inspiring 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 working, you give us a bit of a background about yourself and how you got into the data space? Yeah, I'd up to share. So I came into data career in two thousand and fourteen. At the time I was working in a neuroscience lab with the plans to go and get my PhD in neuroscience, and soon realize 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 a quite a bit discouraging. And so what I did was I step back and looked and said, what parts of my job do I really love and what parts could I do without? And what I 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 last shot. 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 I was like, okay, I just think they get a job working with data in some way. And so I started off as a...

...research analyst, then started taking some courses through some Lukes, realize I really loved it and then decided to go and get my masters in the field as well, and that started just a really exciting to 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 Moss, which is to lead women in data and help coach others and build pathways for diverse audiences to get into this space. I love the story and I'm very excited to unpack a lot more of your journey, but there's definitely a lot to discuss the day 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 the analysts or data scientists, and this is in some sense still true today. But we see a lot more variation specialization between these roles. We have the emergence of hybrid rolls like financial analysts that require more data skills, marketing ABS, Biz offs and even visits intelligence roles. So, as an educator, you someone who's been embedded into space for a long time. What do you think are the different type of data careers available for aspiring practitioners looking to break into data today. Yeah, great question, because a lot has changed since two thousand and fourteen at this space, when I first entered. So on the positive side, there are so many more resources for learden today. So when I was getting into space, I'm in the US, there were only five universities even offering masters to get the debt. So I just share that because if someone has been interested in getting the masters or going through formal education, don't 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. Right, that are available through, you know, 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 analyser 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 and love 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? I really narrow in on industry you care about, like healthcare is, I think, a really exciting place to be because, well, when 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 say. So I would say make sure if you're looking to give 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 the job function means is in a marketing side, this is in a financialism and operation side of things. I think if you put those two combos together, you'll have a really clear grand story that will make it a lot easier to be able to break into the field. That's really grade 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 expert keys, 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 wig win all wrong. How do you assess the importance or the trade off, this erarnningxtent, between these business skills and these hard skills? What do you think are the most important skills and that skill makes 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.

But I would tell people, though, if you mee both of them, how do you balance learning both of them? When you're learning journey and GE I like to use for people is pick your way, but know your ocean. What does that mean? Well, the ocean is a very vast place, right, and that's a lot of times with 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. So That's enough just above itself. On the technical side of things, and now you've saying Saty, 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 making sure you're not just reading the technical articles of what's going on in business, but also just the broader business spille of things. And so for me, one of the ways I are 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 a insight view into how do they make money, how do they lose money? What are the products are trying to sell? And at the end of the day, my understanding, business is quite simple, right and see if it's how do we make money so that we can continue to grow and support our employees and support the customers that were servicing, and you mentioned here something in your answer around communicating your brand or communicating the technical skills that you have. How important our 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 opt 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? Will then you're going to be 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 like technical skills and communication. How did you grow your communication skills over time. I know there's some form of it that is innates, but I'm sure you got in 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 need this presentation or I'll present a portion of this right, but one take any opportunity that presents itself and also, if there are any opportunities that present itself, volunteer yourself to be able to lead that communication. So 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 in your work. So I would say practice makes perfect and take every opportunity and seek out opportunities to communicate the great work your debate. 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. To Day, there are a lot more learning resources that you just 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 our 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 the data career because the forecast of the opportunity in this space is looking really, really well. So the world economic form produces this job report that predicts the top jobs over the next five years, and so in two thousand and twenty they predicted again for the next five years. So that goes through two thousand and twenty five and in the top ten, three of those top ten jobs were all data careers, machine learning engineer, data scientists, data analyst and I think it was a big data specialist right. So the opportunity is really, really great in this care. But you're right, it can feel like there is a lot of competition in this space because, unfortunately, hear from people a lot of times like, I took this class and no one's giving me a job right of it. 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 trap 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 twenty two 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 the job. What do you do having sew A, coma? 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 attractive to you. So those are really and in 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's comes see the practical side, as we mentioned here are 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 day to role? Yeah, I'm glad you're asking this question because just two weeks ago was reviewing a couple of people's resumes and giving some feedback and I was like, I think I'm going to create a post from not bad to possess for a resume, right, because that's usually what I see the resumes isn't 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 in 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 a resume or put things that aren't there? No, but what you want to do is you want to shape your resume and obey 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 pull 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 pole. 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 it is 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. So one thing I would say is pick have a really clear vision of the role that you are going after, right again, not just the data science roll. Focus on an industry or business sector and then crack your resume as a story that's going to tell a story of why you're a perfect person for that role. But the biggest thing I see is with the 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 into 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 get it out through an example. I want to be at the 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 projects on healthcare data. How would you structure a resume for an 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 will bullet point. I know Pithot, I know sequel, 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 it'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 how this now applies to the analyst will as well. And then, finally, I usually end with the education side of things, and the education can go a couple of ways. People off and asks. 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, you the additional education you've done should come through in the skill city path, right. Not True Bachelors or Masters. If you don't have the bachelors or Masters, 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 project. 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 bitten movie up and they did an analysis of all the movies that they've watched over the last five years and they categorize 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 the 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 to as we it's you're trying to break into a role, is let your personality be seen, because you're going to them find the right fit and 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 get hub 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. Right, maybe it's an interactive dashboard. Like fine, 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 saying, who I interviewed as well on the podcast on a seeing 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 so can that genuine and authenticity and that interest in that enthusiasm that you have for the podcast, which will translate for a much better interview experience overall. Yeah, I kind of agree more, I think so often times, 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 when 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 thing. 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. It's that I think it's like a golden retriever sitting next to like a Werewolf, right, and the golden retrieverer house like an iris data set, and then the the like Werewolf pictures, like real worth world data. Right. It's like a classic meme and the day and it's so true, like we y'all like this is why memes are so great, because we see it and automatically get it. But I think also, more importantly, not just in terms of that 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 Irish stata set, it's so overused in terms of what people have done with it. So again it it can when you cap into what you're really interested in, you'll find more interesting data sets. Right, maybe we'll use your net for this data. Maybe you use data from your Apple Lodge or your health tracker, right, like, maybe you're really interested in are 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's done. Tables 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 unique, and so I would say the biggest problem on the stake that people do is just not make a unique or folio. So the last thing that we mentioned when we were talking about principles for breaking out from the crowd is hearing your work building community around you. I'd love to anchor this actually, and your experience launching women in data. I had an amazing time you're preparing for this podcast, learning about your story, and I find it to be a great testimony for the power of courage and community to do you mind expending on how you first launch you with him in 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 can 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 broad our vision for more equality in the space. So, unfortunately, in my master's program you know, there was thirty people in our first cohort and there was only myself and one other woman in the progrium, and so I really just spelt the need to connect with other people like myself, and so women in data started with a meat of 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 has showed up and I was feeling very discouraged and really just wanted to pack my banks and go home. And thankfully I decided to wait fifteen more minutes after the third time and one person came rushing in the door and she brought three other people, and so that was really the birth of women and data. And I think it also just goes to show like 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 as a community of over thirtyzero people and thirty countries and fifty 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 dedication, it will grow. I'm really in awe about the story because it the psychological barriers of getting over that discouragement and keeping on the journey is super impressive to me. And what are some of the lessons that you can share when mustering the courage and the forty two two keep forward and fostering a community of peers and mentors that can help you grow? I really look at courage is and muscle, right. It's something that we have to practice and they 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 we get started doing that? You start with small steps, right. You start by raising your hand and speaking in that maybe you start by voluntary 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 of bad things that will happen, we're able to relax and take a bigger job. And that's truly what has happened to me, is 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 muscles key, but then, more importantly, I would say consistency and tenacity really praise able in here. I think a lot of people are familiar with the heroes Journy 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 will maybe a little high, but you must keep going on because there maybe 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 part 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 is through women in data, have had both mentors and have mentored a lot of people. How should aspiring Practitioners Treat Mentor Mental 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 the word because I think a lot of times every 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 the mentor? But 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 have way through you go, are you my mentor? And they go, are you my mentor, and and it happens very organically, right, and that's 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 the 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 you could be abused to your mentor, like maybe help them out or volunteer, and that's good. I think if there's that opportunity that presents itself, we definitely should. But for me, why I mentor people, it's 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 yourself, but stepping show up to your meetings on time, to the things they ask and the whole work. Come in with questions and be prepared. There are simple things, but it will show up for them, mentor, and they will be happy to give you more once they see that it is pain off and they want not being 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 and 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 and just changes right. So, and 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 to still move up in your career and lead. You may e would have more of it because there's more responsibility on your shoulders. So how do you make friends with Your Post Syndrome? That's what I like to do. Of like, how do I look at that and and really not use that to limit me, but use it as a way to build my courage muscle. And so I think imposter syndom 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 to go for yourself to maybe just post once a week. I know people who, when they started posting to 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 ten minutes see that somebody like, did somebody comment? That's a good starting point and just put it out there. And then as you start... do that right, you'll realize Um, it's not as scary. There aren't as many trolls out in the world as as we think that there are, right and actually people, actually people are, you know, rather kind and supportive, and so once you start to get over those first barriers and you'll be able to do it more. So my advice is use your imposter syndrome to see where you need the 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 as being friends with your imposter syndrome and using it as a tool to push you forward. That's something that I find struggle with. That with as well. You know, I host the podcast here, and imposter syn rooms still something that I struggle with. Given your experience as a community organizer as well, someone who's worked on can increasing diversity and equity and data science, I 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 our questions I need to ask to understand if this is the type of organization that will lift me up or I will have to fight much harder than mail 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 the versity right, and we're not inclusive. Right. No one will ever answer that question that way, and thankfully. But what happens is sometimes they may say, yes, we supported 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 interviews. So I tell everyone's going into interviews they're not just interviewing you, you're interviewing that. How do they respond to your answers? Do they respond in a claver way to say yes, or 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 in total things that will give you insight into what that overall culture looks like. I couldn't agree more like 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'd love to I love your perspective here. I'm being able to measure the companies throughout the interview process to be able to make that decision. So now, sad before we were UPUP, I'd be remissed not to talk about future trends that really shape the future of the industry and how we think about data jobs to day. 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 ask this question because I do love to talk about the future and most of the time I'd rather be in the futures than here, but it's important to be able places at once or so, yeah, I think there's a couple of cute 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 career. 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 web...

...three, 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 than changes one 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 in to web three and into blockchain technology and as a practitioner in the space, I would be someone who would be encouraging the use of this and my organization, because one of the most beautiful things about blockchain technology is it is time stamped and verify. So what happens to this data? It's very clean data and nothing makes the data side to sit 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 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 three, the applications and what this means to day the 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 or likes. Are you start with python? Are you starting with all our don't do both at the same time. Like to 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 Dara, Hashpack or Hashcraft, which is a chain. There's lots of different chains that you can work with. So it's similar to data sides and that space of like don't try and do it all at once. Just pick one and understand how smart contract works, how a token works, and then from there, you know, you can kind of go wherever you want. Finally, say to as we close out our episode, you have any fund wars before we wrap up the day? Yeah, I would think I would just say to all the listeners is stay curious and don't be afraid to start with the blank page, a blank notebook, a blank campus. Start with something new and create a new yourself, to let your true self be seen, because that's really how you're going to find a career that brings you the most joy. That's really awesome. Thank you so much, Stadi, for coming on. Data framed my pleasure, hope to talk against it. 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,.

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