The Importance of Culture in Succeeding within an Organization

Culture is such an important aspect of being able to succeed within an organization, and it's something that will affect you 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. That's why I'd love to discuss this topic further with you.

Measuring the Company throughout the Interview Process

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 today. 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? I'm so glad you asked this question because I love to talk about the future and most of the time, I'd rather be in the future than here, but it's also important to be in both places at once. So, yeah, I think there are a couple of key things that 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.

Blockchain Technology and Its Impact on Data Careers

At its core, blockchain technology is a database, it's a transaction, and it's a record. What makes it so special is that it's decentralized. From the decentralization, we can reach this consensus, and that's what makes it so powerful. There are a lot of great things happening in this space, and applications of this through web 3 will change a lot of how businesses operate. It's really important for data professionals to be aware of this because how businesses operate then changes one where you get the data from those streams of business operations that you're looking at.

Practical Applications of Blockchain Technology

As a practitioner in the space, I would encourage the use of blockchain technology in my organization because one of the most beautiful things about it is that it's time-stamped and verified. What happens to this data? It's very clean data, nothing makes the data side sing more than having very clean and accurate data. It's immutable, right? You know what record was, what happened? That's really awesome.

Technical Skills for Learning Blockchain Technology

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 3, what are the technical skills I should learn? First, I would start by getting my head around where the industry is at today. There are a lot of great webinars happening, like women in data, doing a whole series on web 3, the applications, and what this means for professionals. From there, I would pick one language to focus on, like Python or R, but not both at the same time, just stick to one.

Choosing a Blockchain Platform

I would find a chain that I want to use, because blockchain is one chain, but there are actually hundreds of chains out there, like Hedera or Hashgraph. It's similar to data science in this space where you 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 can kind of go wherever you want.

Final Words

Finally, I'd like to say that if you're listening, 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, let your true self be seen. Because that's really how you're going to find the career that brings you the most joy.

"WEBVTTKind: captionsLanguage: enyou'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 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 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 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 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 sadie st lawrence is the founder and ceo of women in data the number one community for women in ai in tech series 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 relationships portfolio projects combating impostor syndrome and 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 data camp radar or 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 and 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 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 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 a 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 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 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 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 and 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 type of data careers available for aspiring practitioners looking to break into data today yeah great question because a lot has changed since 2014 in 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 u.s there were only five universities even offering master's degree at the time so i just share that because if someone has been interested in getting a masters or going through formal education they'll know the plethora of you know 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 exist 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 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 the job function means is it a marketing size is it a financial is it an operation 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 but i would tell people though if if you need both of them how do you balance learning both of them on your learning journey and gee i like to use for people is pick your way but know your ocean and what does that mean well the ocean is a very vast place right and and that's a lot of times what the 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 in 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 way 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 goal making sure you're not just reading the technical articles of what's going on in business but also just the broader business field 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 a 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 so 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 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 leave 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 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 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 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 you know 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 of people's resumes and giving some feedback and i was like i think i'm gonna 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 right 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 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 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 goal the biggest thing i see is 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 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'm 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 a 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 how this now applies to the analyst rule 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 master's degree if you already have those things you 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 at 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 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 filled 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 you're trying to break into a role is let your personality be seen because you're gonna 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 right maybe it's an interactive dashboard like 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 daily 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 names out there it's 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 pictures it's like real words world data right it's like a classic meme in the game and it's so true like we 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 why this meme 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 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 this data 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 kegel 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 unique 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 you know preparing for this podcast learning about your story and i find it to be a great testimony for the power of courage and community so do you mind expanding on how you first launched women and 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 you know there was 30 people in our first cohort and there was only myself and one other woman in the program and so i really just felt the need to connect with other people like myself and so women in data started with a meet-up 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 start 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 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 um 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 and what are some of the lessons that you can share when mustering the courage and the forty-two 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 when 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 job and that's truly what has happened to me is just a small step to say hey i'm gonna 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's 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 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 a mentor well that starts by just building relationships with people so how do you do that you you do that through conversations through finding commonalities right and creating connections 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 mentee and and it happens very organically right and that's that's the best case scenario right it's where those connections happen organically and so i 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 we 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're going to be so happy and they're going to want to pour more back into you and by working on yourself accepting show up to your meetings on time do the things they ask 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 mention 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 trying 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 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 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 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 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 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 with as well you know i host the podcast here and imposter sermon is still something that i struggle with given your experience as a community organizer as well someone who's worked on kind of increasing diversity and equity and data science i'd love to understand from you if i'm an applicant right um and i'm from a minority group and i'm applying for a job and i'm interviewing with the company how do i understand what are 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 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 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 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 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 and tonal 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 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 today 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 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 3 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 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 key look into web 3 and into blockchain technology and as a practitioner in the space i would be someone who would be encouraging the use of this of 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 the data side to 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 3 what are technical skills i should learn yeah so 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 today to 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 all 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 block chain is one chain but there are actually hundreds of chains out there there's hedera 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 timeyou'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 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 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 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 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 sadie st lawrence is the founder and ceo of women in data the number one community for women in ai in tech series 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 relationships portfolio projects combating impostor syndrome and 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 data camp radar or 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 and 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 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 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 a 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 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 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 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 and 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 type of data careers available for aspiring practitioners looking to break into data today yeah great question because a lot has changed since 2014 in 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 u.s there were only five universities even offering master's degree at the time so i just share that because if someone has been interested in getting a masters or going through formal education they'll know the plethora of you know 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 exist 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 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 the job function means is it a marketing size is it a financial is it an operation 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 but i would tell people though if if you need both of them how do you balance learning both of them on your learning journey and gee i like to use for people is pick your way but know your ocean and what does that mean well the ocean is a very vast place right and and that's a lot of times what the 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 in 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 way 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 goal making sure you're not just reading the technical articles of what's going on in business but also just the broader business field 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 a 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 so 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 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 leave 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 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 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 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 you know 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 of people's resumes and giving some feedback and i was like i think i'm gonna 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 right 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 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 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 goal the biggest thing i see is 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 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'm 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 a 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 how this now applies to the analyst rule 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 master's degree if you already have those things you 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 at 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 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 filled 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 you're trying to break into a role is let your personality be seen because you're gonna 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 right maybe it's an interactive dashboard like 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 daily 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 names out there it's 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 pictures it's like real words world data right it's like a classic meme in the game and it's so true like we 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 why this meme 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 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 this data 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 kegel 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 unique 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 you know preparing for this podcast learning about your story and i find it to be a great testimony for the power of courage and community so do you mind expanding on how you first launched women and 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 you know there was 30 people in our first cohort and there was only myself and one other woman in the program and so i really just felt the need to connect with other people like myself and so women in data started with a meet-up 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 start 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 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 um 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 and what are some of the lessons that you can share when mustering the courage and the forty-two 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 when 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 job and that's truly what has happened to me is just a small step to say hey i'm gonna 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's 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 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 a mentor well that starts by just building relationships with people so how do you do that you you do that through conversations through finding commonalities right and creating connections 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 mentee and and it happens very organically right and that's that's the best case scenario right it's where those connections happen organically and so i 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 we 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're going to be so happy and they're going to want to pour more back into you and by working on yourself accepting show up to your meetings on time do the things they ask 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 mention 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 trying 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 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 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 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 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 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 with as well you know i host the podcast here and imposter sermon is still something that i struggle with given your experience as a community organizer as well someone who's worked on kind of increasing diversity and equity and data science i'd love to understand from you if i'm an applicant right um and i'm from a minority group and i'm applying for a job and i'm interviewing with the company how do i understand what are 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 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 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 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 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 and tonal 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 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 today 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 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 3 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 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 key look into web 3 and into blockchain technology and as a practitioner in the space i would be someone who would be encouraging the use of this of 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 the data side to 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 3 what are technical skills i should learn yeah so 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 today to 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 all 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 block chain is one chain but there are actually hundreds of chains out there there's hedera 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\n"