Starting Your Career in Data | The Best Tips For Getting Started

Today, I'm going to share all the tips and tricks that I have for landing a job in data. We'll look at skills, we'll look at the hiring process. I'll share some statistics along the way too, but just a little bit about women in data before I jump into things. We're a community of over 60,000 data professionals across 85 countries. Our mission is really simple. We would like to create gender parity in the data space. Absolutely everything we do for our community is totally free. How we do this is funded through our partner support and sponsorship. So all the activities, all the events, all the mentoring, all the networking is totally free for community members, which are comprised of men, who we refer to as allies as well. We have 60 and counting partners who look after us. And I honestly think I have the coolest job in the whole world. I get to work with them every single day, growing their data teams with diversity in mind, of course, nurturing their internal women in data community, which, as I've mentioned, is often comprised of men as well, who we refer to as our allies and who we desperately need. And my favorite part is curating all the fulfilling activities like workshops, men mentoring opportunities, networking, hackathons, and lots and lots of different types of engaging events. The aim of these is so that our partner community can connect with each other, so that they can meet you and recruit talent, knowledge share. And often there's lots of opportunities for everyone to get involved in activities and use their data skills for good. So, happy to answer any questions on that a little bit later on. But an organization like Women in Data exists, unfortunately, because there is a massive underrepresentation of women in the data industry, especially in the UK. As you can see here, there's just 22% female representation in data and AI in the UK, compared to 26% globally. It means that for every four men entering a career in data, we don't even have a full woman. That figures at about 0.68% of a woman, which is just insanity. As I said, our mission is really simple. We would like the same amount of women as men working in the data space and we need everyone, including men, to help us shift that dial. But we are here today to talk about careers in data and there are so many. This is only a handful of the super common job titles that I've come across. And I hope throughout this presentation that you'll be able to discover how diverse data jobs can be. And the beauty of data is that it sits across every single industry and there's something for everyone, whether you're more technically inclined or not, there definitely is. I love this expression of squiggly careers and this example is taken from one of our community members who works for for a large UK retailer. It was actually just recently crowned the nation's favorite supermarket for the third time according to a survey by the Consumer Watchdog Witch. If you know them. So if you can guess who this retailer is, do so definitely in the chat. Reece, you can shout out to me if anyone guesses who it is. But the point I'm trying to make is that careers are unique and honestly, totally individual. If I asked each of you to draw a squiggly line, they would all look so different and none would be right or wrong or better than another. And it's exactly the same with a career in data. This particular individual had no idea that on from her art degree she would be head of adoption within the data and digital sector of one of the largest global retailers and renowned retailers in the world. I was speaking to her a couple of weeks ago and she said the skills she learned in her art degree like being resilient or harnessing her creativity or working to deadlines really helped her as she embarked on her career in data. She also said that having roles like working in hospitality as a bar manager, she shaped her soft skills and actually made her comfortable being customer facing and just simply engaging with other people. I think people think that presenters are, you know, naturally chatty or good at presenting, but it's actually a skill that you need to practice and learn how to do well. So after three years of working as a bar manager and still not really knowing what to do with her art degree, she took a role as a shop supervisor for different UK retailer. After about a year she moved on to take a position at Whole Foods actually as the store supervisor where she was for a year and a half before being promoted to data data manager. Now this is in the food categories department and interestingly she didn't realize that embarking on a career in data was even a thing or that that was what she was doing until this point in her career. After 2.52 and a half years working as a data manager, she took a role with her now current employer. I don't know if any of you have guessed who that might be, but in the food central planning team and she worked within pricing and promotion so she was looking at things like product master data and technology transformation programs and she held this role for about two years. Now the reason I'm telling you how long she was in. Each role is important and I'm going to touch on that a little bit later on in the presentation. But she got to the point within this role that her data analysis was having an impact on critical business decisions. So she was promoted to data strategist and it was here where she was looking at the enterprise data strategy. She was there for a year before she took her current role as head of adoption within the digital and data sector. She has used all the skills she's accumulated along her career journey, like design thinking from her art degree, looking at large programs, strategy, people and culture in her now current role. At the moment she's working on a particular academy program which is aiming to equip the this particular organization, 65,000 colleagues with the relevant technical capabilities to play a part in this huge digital transformation that they're they're currently going to going through. I'd like you to know that this kind of unconventional route into a data career is totally normal. Most people land into the data industry in really rogue ways. When you're shaping your cv, absolutely highlight the transferable skills that you've accumulated throughout your experience. Show how they can be applicable to the role that you're applying for. I do want to talk about skills and I don't want you to overlook the importance of soft skills. I've put my top three here with communication being the first. As you progress in your career in the data field, you'll need to be able to communicate in ways that your audience can understand. And this will change based on who your audience is, how data, data literate they are, that kind of thing. Presentation skills fall into this category. The more presentations that you do, the more confident you'll become. They are daunting. But honestly, if you push past that anxiety and nervous feeling you have, I promise it does. It gets easier. But preparation is key and practicing it loud really helps. I advise using data to shape your presentations. It will help validate your arguments and it will help you communicate clearly. Active listening is such a good skill to be aware of. And do you know what? We're all guilty of it. When someone's speaking, especially virtually flicking through email simultaneously or just zoning out and not thinking about what they're actually trying to communicate to you. It makes such a difference for people who interact with you if you're actively listening to what they're saying, and it does help to summarize what they've said back to them. But it's a really important skill to have. Everyone is so busy, so when somebody actually takes the time to digest what you're saying. You really feel listened to now. Influence and Persuasion this is not as daunting as it seems, and data can really help you here. Throughout your career, you'll need to use data to influence decisions. This is a skill in itself, but data can enhance your argument and have a positive effect on your persuasion skills. For a really simple example, I think most of us have been there back in the day when we were kids asking our parents if we could have a puppy or some kind of pet or a dog and we were often met with a very firm no answer. But I was thinking today. Imagine if we were really honest and we had researched and gathered the data on the benefits of having a pet, showing our parents the studies where children who have pets tend to be more active and that pets can actually reduce showing them the data that demonstrates pets can actually reduce anxiety and stress. We would have had a much better, stronger leg to stand on in that scenario. But for a business example, imagine you're working for an E commerce company and you're trying to convince your team to invest in improving code customer satisfaction. By presenting data driven insights, you can build a really compelling case for investing in customer satisfaction initiatives. So if you're looking at response times to customer inquiries stating things like oh, data analysis shows that customers who receive a response within 24 hours are 30% more likely to make a purchase. My point is having good persuasion skills sounds daunting, but data is really going to help you master this. I'm briefly, briefly going to touch on some technical skills because the list is honestly endless and it's ever changing and it depends what kind of data role that you're looking for. You may not obviously always need those hardcore coding skills, however, my advice is always try to upskill, stay on top of what's hot and never get complacent. Every year you should be trying to add a skill to your cv. I'm not saying you need to learn a new language like Python or Scala each year, but upskill at a pace that works for you. There are some really common technical skills that I see on a daily basis and while I was a recruiter as well, I've included some of them here that are kind of most in demand within the data industry. There's definitely a crossover between professional skills and software soft skills. However, the World Economic Forum highlighted that these three skills are some of the most important to have. I think they're self explanatory and I think your analytical thinking would definitely have been enhanced through Any datacom training you would have done. But resilience, adaptability and flexibility are all super important, especially in what is an ever morphing, ever changing data landscape. The three most common data jobs that we see. I'm not going to go through each of these technical skills for each role because there's a lot to unfold here and you can flick through these later when you get a copy of the deck. But I just wanted you to see some of the really common technical skills that are needed for these particularly common data roles. Data analysts. They analyze numeric data. You use it to help companies make better decisions. So most entry level data analysts have strong statistical knowledge and understanding of data handling, like modeling and reporting techniques. You'll need to be able to gather insights from the data and have good visualization skills. Looking at tools like Power, Bi and Tableau tend to be the ones I've seen most often. Looker is another one. Data engineers are more technical. They prepare the data, design, construct, test, maintain data architectures, mainly looking at data pipelines. And then so it is a more technical role. But then when we look at data scientists again, it's analyzing and interpreting what can be more complex data. They're often described as data wranglers who organize big data. You'll need statistical knowledge as well, machine learning expertise to actually develop the predictive models. But in summary, each of these roles play a crucial part in the data ecosystem, with obviously analysts focusing on insights engineers building and maintaining the data systems. Then we have the data scientists who dive deep into complex data and actually drive those strategic decisions. Now, we saw at the start of this presentation there are so many different variations of data roles. I'm going to go into a bit more detail about what I mean shortly, but before I do, I have a bit of a statistic for you. So job flexibility is super important. And job ads that promote job flexibility tend to attract 30% more applicants. And the reason I'm sharing this with you is so you can keep an eye out for it when you're doing your own job searches. And also it's something that all of the women and data partners offer in some shape or form, whether it's hybrid working or integrated working. The main difference between the two is that hybrid working requires you to be in the office maybe say two or three days per week. And integrated means that you have the flexibility to work around your life commitments so you don't have fixed hours like 9 till 5. Now our partners are always, always, always looking for data talent. This is only a handful of Them represented here we have 60 plus as I've told you already, but that number is increasing weekly. Their values mirror women in data's values and I can 110% vouch for their working environments. I'll share some more information on how to see all of their vacancies and the data roles that they're hiring for in one place. A little bit later on it is the Women in Data Jobs Board, but just to know that we welcome absolutely everyone to to use it. Women, men, absolutely everyone. About 87% of people who apply through the Women and Data Jobs board are women. But men, you're totally welcome to apply through it too. And the jobs there are globally representative, not just UK based. The majority are, but a lot of them are global. In the next three slides I'm going to show you the types of jobs that some of our partners are currently recruiting for. It's going to demonstrate how diverse roles in data can actually be. There's so much more out there than just the traditional data analyst or data engineering or data science role. And it will also show you probably that job titles don't often fully encompass the job description or even embody the role entirely. Lego is one of our partners and I'm using this example because a lot of people go to LinkedIn to search for a job and just searching for data analyst roles. I'm pretty sure a retail data solutions analyst isn't the first thing that comes to mind when you're, you know, trying to see what's out there. But if you actually look at skills that are required, there's nothing overtly challenging or complicated here. Proficiency in power bi, fluency in SQL and then the responsibilities, maintaining dashboards, working with a product team to onboard data. My advice is be totally open minded. When you see job titles like this, read through the description, understand exactly what's being asked of you and nine times out of ten you probably absolutely have the skill skills required. Another one of our partners is Expedia Group. I work quite closely with their marketing teams who are just amazing. They offer totally flexible working and promote diversity in every way possible within their teams. Have a look at this job title though I don't think proficiency in SQL or data manipulation skills are something that you'd immediately associate with this role. Or maybe it is. Correct me if I'm wrong, but this is very much a data focused vacancy. For those of you who might not know, data manipulation is just basically the process of organizing, transforming, refining the data just to make it more usable, readable, structured, that kind of thing. But if you have a look at the responsibilities where it says look for new ways to identify growth opportunities across Bing, Yahoo Japan and Naver. If you see something like this, do not freak out and think this role is not for you. Expedia is big in the States, South Korea and Japan, and these are just the different types of search engines that are commonly used in those locations. Women in particular tend to see something they're not entirely sure of in job descriptions and then immediately shy away from even applying. So even if you've never heard of Naver and you have no idea that Yahoo. Japan even existed, it doesn't matter. Apply for the role if you like the organization and you have other relevant experience Men, you're better at this than us. You see a job advert and if you have no idea what 80% of it is, you apply anyway. Women, if you're missing 20% of the skills, you tend not to apply. My advice is apply for the role. If you like the company and it sits in your area of expertise, whether or not you have 100% of the skills, absolutely go for it. The Kingfisher Group is one of the newest additions to the women in Data Partner Community B and Q and Screwfix fall into the Kingfisher Group. Now this is an interesting role. It combines two very different sets of skills, and you can see the more technical requirements here, like proficiency in SQL and other languages like Python and R, and also experience with databricks and airflow. But then there's also the visualization skills that come into play. I would say that a lot of the traditional data engineering skills are needed here, but you can see there's no mention of that in the job title. Like developing ETL pipelines is classically a data engineer's task. Again, it is so important to know where to look for these kinds of roles. If you search for data engineering roles on Google or on Read or Total Jobs or wherever you search on LinkedIn, there's a chance that this job won't come up. So I do have a solution for you, and that is the Women in Data Jobs board and this is how to access it. It's constantly updated so it should form part of your search when you're job hunting. We encourage absolutely everyone to apply, so as I said before, we're not exclusive. Although 87% of the applications come from women, our partners post five vacancies each per month. They have the opportunity to do that, so it's definitely worth checking out. I do want to talk about the hiring process just briefly because I have some tips and tricks to help your CV stand out. I can also help you ace your interviews and give you some tips as well on how to continuously develop your career. And please pop any questions you might have or if you want me to explain anything in a bit more detail into the chat, but your CV first. We'll start with that. It can be longer than one page. I don't know who came up with the one page rule, but I would definitely like to talk to them because it's a total, total myth. I would just say make sure your experience from the past five years is there and at least if you haven't heard of a data role before, talk about where and how you have previously used data. In other experience, you don't need to put your full address or your photo on your cv. I would say leave them out altogether and just make sure. This is going to sound so simple, but make sure your contact number is correct and visible. There's no need to push references down at this point. It's something that a lot of people in the UK do. I know that. Or they'll put the hiring manager or their previous manager's email address and contact details on the cv. Absolutely. Don't do that because for GDPR purposes a lot of third parties or employers, they can't keep your CV on their site if there are contact details for someone else there. So what you can do is just say on the bottom of your CV that you can provide any references that are needed on request. If you have any more questions about the actual layout of the cv, pop them in the chat and I can answer them for you. But don't leave gaps. What I mean by this is not the physical space between the lines on your cv, but anytime that you weren't working throughout your career, stay state what you were doing. Maybe you were traveling, maybe it was a redundancy and you took some time off to recoup. But any gaps over three months do need an explanation. Make sure your CV is updated and definitely get it posted on sites like CV Library and CW Jobs. Potential employers and recruiters will be able to get in touch with you that way. That's often the first place that they'll look for candidates, AI relevancy and keywords. This is important to know about because when I was recruiting, which was last year, keywords were the in thing. Our systems matched your CV to our search based on certain keywords and how often they actually appeared on your cv. So for example, if you put SQL in as one of your skills and then you mentioned it through throughout your CV multiple times, it would flag quite high on a relevancy search for us. However, things are changing thanks to Gen AI and large language models. But you need to be aware that now, no matter how many times you put SQL in your cv, if you don't talk about how you used it, for example, or at what capacity, your CV will score quite poorly on that relevant scale and it won't match. So things are changing on that front. I do want to talk about the interview process as well because you could have the most incredible cv, but if you can't talk about your experiences or you don't really know any good interviewing techniques, then it can have a negative effect on you landing a job after you've applied for a role, most of the time the talent team will get in touch with you first. Sometimes they have a specific set of questions from the hiring manager to ask you and these could be in the form of what is a SQL join or can you tell me what an API is? They'll be relevant to the job that you're applying for, but once they've qualified you, they'll then send your CV to the hiring manager and then you'll have your first formal interview. The hiring manager will want to see how your previous experience is relevant to the current role, so definitely come armed with some examples. There could be some technical questions at this point too. I would say come armed with your examples and be ready to ask some questions yourself too because it shows you're interested and engaged. Normally the final stages will involve meeting some various team members and then the final final stage you'll normally meet a senior stakeholder and they can ask some funky questions. So I would be prepared and I do have some advice for that. So listen carefully to the question being asked and this applies for every stage through your interview process. Answer very concisely and very clearly. Don't over talk. But I would ask if they would like you to go into more detail because a lot of the time the interview will just want to make sure that you know your tech and your experiences are relevant and sometimes they may want you to go into more detail. So just ask if you're not sure. Do dress smartly. Even if it is for virtual sessions. First impressions are very much a thing in matter punctuality. I mean this is pretty self explanatory but be on time and don't be too early. There is nothing worse than when a really busy hiring manager is really busy and they can see that an interview, if it's virtual, has been started 20 minutes or 30 minutes before it's actually supposed to start. So I would say anywhere between five and 10 minutes is the perfect time to be ready to get going. Obviously make sure that your whatever platform it is, Zoom or Teams is updated and you're ready to go. But yeah, I would say don't be too early. It's almost just as bad as being late, I would say, in my opinion, and honestly, preparation is key. Have your examples ready. Also, be ready to think on the spot when a challenging question is thrown at you. So a tough question that comes up a lot is what is one thing you would criticize yourself on or what's your greatest weakness? Now don't fall for this and do not say I'm always late for everything or I procrastinate all the time. The trick here is to go for a non assumption skill relevant to the job that you're applying. For example, if you're interviewing for a database administrator role, it would probably be a bad move to say something like troubleshooting is not really my strong point, but there's obviously little risk in saying something like I struggle a little bit with public speaking where I can be a little bit shy. So that's my advice for those difficult questions. On your career. Interestingly enough, the average tenure of a data scientist is about 18 months. So you'll stay in a role for about 18 months before you move on. My advice here is stay put. As long as you are learning, developing, gaining experience, soak up as much as you can. Employers don't really like to see that you move from company to company regularly. At least two years at each organization looks attractive because if you think about it, when you start a new job, it takes about three months to learn the ropes, six months for that honeymoon phase to dissipate, and up to a year plus is when you actually start to knuckle down and deliver on projects. So if your future employer sees that you tend to leave companies at that year or 18 month mark when things are starting to get a little bit tough and the actual input and work begins, doesn't really bode well. So don't be happy. Obviously, if everything's good, you're being looked after and you're learning and you're developing and you're growing, stay put, stay relevant. I advise you to keep your portfolio updated. If you don't, it can be more of a hindrance. So stay on top of what's hot, read up on things, add to your portfolio Constantly upskill. That would be my advice there. Network like crazy networking is so, so, so important. Get to know what events are happening, what activities, if there's data for good projects that you can get involved in. With some charities, you never know where an opportunity is going to come from. And the larger your network, the more opportunities that will come your way. I would say it's really important to put yourself out there. I have a mini quiz for you to get involved in. It's just one question, don't worry. And hopefully it shows you how important it is that we all work towards promoting diversity in the data industry. But bias in AI is perpetuating gender stereotypes. This is just something to be aware of as you embark on your career in the data field. Field, there is inherent bias when it comes to working with data, right from data collection through to data analysis. It's widely known that the one way to eliminate bias is not just to broaden diversity of data sets, but also to look at diversity across teams and ensure there's a varied team of data professionals working on a project. But sadly, the figures and statistics show us that women are still underrepresented. But I'm wondering how many AI systems demonstrate gender bias. Don't Google the answer, but let me know in the chat what you think the answer is. Richie, I don't know if you have any ideas what the answer to this question might be.

I suppose it sort of depends on whether it's an AI system that's actually involving gender data, but. So I presume that's just what we're looking at. I would say I would have thought most of them. Is it going to be 50%?

Yeah, I thought it would be actually a lot higher than this as well, but the answer is actually 44.1%. And of course then this leads to lower quality of service, unfair resource allocation, and then of course it reinforces those gender stereotypes. But for example, like Google speech recognition feature, that's 13% more accurate for men than it is for women. So I'll leave that with you all. I do want to touch on the importance of building your network and getting involved in the data community before we open up for Q and A. You might even be wondering how to go about doing that and it can be daunting. So my advice is use the QR code here, register to become a Women in Data member. It's totally free. We welcome everyone, men and women. There are lots of things that we offer and it's a great way if you're just not sure how to start building a network to get involved in the community. There are so many opportunities to network, to learn, to take part in activities and events. Every Wednesday we virtually host Lunch and Learns. This is an opportunity for you to meet our partners, learn some tips and tricks from them and hear about the type of data work that they're undertaking within their organization. So absolutely, please do scan the QR code, register as a member and yeah, you can join us each Wednesday to start off with for our Lunch and Learns. I didn't mention at the start, but we host the world's largest data event for women and it's actually taking place next week. We have a stellar lineup of training sessions, keynote speakers, panel discussions, and as a community member, you will have access to all of these things going forward. So this would be really great starting point for one, having a look at the jobs board to see what roles are available and out there and two, just getting involved with our community. And we have so many incredible partners. So it'll be a good starting point for you to get a feel for what is out there. And I think on that, Richie, we can. I'm more than happy to open to Q and A if there are questions from you or from anyone.

Sure. Brilliant. Thank you, Chloe. Yeah, it's very informative. So for anyone in the audience, if you've got questions for Chloe, please do ask them chat. I see we've got a few questions lined up already. I actually have a question for you. So you mentioned about staying in jobs for two years before you move on. Now I'd say when I was in my 20s, I spent a lot of time doing temp work like going Amos had burned through like a dozen different jobs, like a few weeks or months at a time in each. How would you go about explaining that sort of rapid career change?

I would say as long as you have an explanation, then it is absolutely okay. What I have seen a lot in CVS is that people go through a period where they're trying to get settled and finding their feet in the right kind of role. Or if it's contract work, for example, there might be three month contracts or six month contracts. As long as you have a reason and you can explain that that's the case, that is absolutely okay. And that you're constantly learning so that the skills from maybe each contract or piece of work that you've had have contributed to the next contract, then I think that that's okay. What some people do is they'll stay in a role for six months to a year and when things get difficult, they decide to move on. But I think it's at that point it's just to dig deep. If it's a good organization, it's going to be the same no matter which organization you go to. Just stay put, try, learn as much as you can. Because when employers are hiring, they want to see that you're reliable and that they're not just hiring and investing in someone who's going to leave next year. In 2023, data scientists was voted the sexiest job of the year, surprisingly. And yeah, people tend to get excited when they see another project or another organization or a startup is doing something cool and they move on quite quickly. But, but what will make you stand out and what's attractive to employers is if you stay pushed for between that 18 month and 2 year mark is the sweet spot. You should be learning, you should be developing, you should be compensated and paid right for your skill set. So if none of those things are happening, then it is time to move on. And if that is the case in less than a year, then I would say totally move on. But if everything is okay, stay put.

Yeah, it just feel like there's a big difference between I was on a temporary contract or I was trying to find my feet in a career versus I kept getting fired for stealing things from the office. You mentioned data science being described as like one of the sexiest jobs of the year. That always mystifies me. It's like obviously no one's ever seen me just like sat there mainlining coffee and swearing at my Python code. It's not a sexy look at all.

All.

All right, let's move on to some of the questions from the audience. So this comes from Chris. So Chris says, I'm 56, I would love to get started in data. I've been working insurance for the past 20 years. Will people hire an old worker with zero experience? I'm presume that's zero data experience, not like you've had zero career experience. If you're starting your career at 56, that's going to be a, a tougher ask, I think.

I think so. I think data features in our everyday life constantly and that we actually are all data people. It's just about finding how you've used it in previous roles or in previous ways and shaping that into what another particular vacancy is looking for. You can be clever in how you do it, but you have definitely worked with data before. Um, somebody asked me a question recently and they said, what's your earliest data memory? I was like my earliest data memory, maybe when I started working as a data recruiter, you know, and actually what she said was, no, it was when she was sitting at the table doing her maths homework with her mom and learning about the percentages and how to get a percentage of a number. And I was like, wow, okay. Actually, if we think about when we first started using, it's, it's everywhere. My point is, data is everywhere and you can be creative in how you shape your experience to make yourself look attractive to employers.

Now that does strike me as kind of one of those, like, slightly tricky interview questions. What's your first memory of data? Because someone asked me that, I'd be like, I can't even remember what I had for breakfast this morning. I don't, I don't remember that far back. So this is the sort of thing where you have to prepare for your interviews and come up with like, ideas of like what you've done with data beforehand rather than trying to just freestyle it during the interview.

I think interview questions like that can be really off putting, especially if you're nervous. My advice is if you get a question like that is just take a breath and smile and just say, that's a great question. And I would say, I can't remember what I had for breakfast. And if you can change it, you can say, because a lot of the time when you're asked these tough questions at interviews, interviewers just want to see how you think on the spot and how you're going to react. They don't really care about what your earliest state of memory is. So I would say I can tell you what I like most about data and then tell them what that might be. And then maybe you can ask them a question, be like, by the way, what's your earliest data memory? And have it more like a conversation and it shows some personality. But yeah, another great piece of advice that someone told me a few years ago when I was interviewing, they said, Google the 10 most difficult interview questions. So I remember at the time going through them all and I think when I was interviewing for roles, a lot of them came up and it's just being prepared and knowing how you will think and answer the question on the day can really help you. So you're not going to know what you're going to be asked, but you can be prepared for those rogue kind of questions that do come up. Yeah.

Oh yeah. So it's not happened to me, but I have heard of places asking to tell it. You've got to tell a joke in the interview process. And that seems incredibly hard because, yeah, they just want to make sure they're not hiring boring people that they have to work with. But yeah, coming up with a joke on the spot has got to be tricky, especially if you're trying to do a data science related joke. I'm not sure how that even works. All right, next question comes from Prabal saying, currently I'm enrolled in a data analyst in SQL. This is a certification track where you get a professional qualification in SQL at the end of it. So it says, can I directly put my resume after the completion? Okay, so this is more of a Data Camp question, so if you don't mind, I'll answer this. So after you've done your certification, you can show that on your LinkedIn profile. As a Data Camp subscriber, you also get a portfolio so you can show off all your certifications, all the content you've completed. You can show off any projects that you've done around Data. So if you're wanting to get hired, maybe you recent post link the chat to our portfolio page. You want a portfolio, A Data cup portfolio to show off all the data work you've done. All right, so next thing is from Elsa. So Elsa says, I have a huge interest in projects related to women in data. I'd like to know what networks you have to to join and start to have some experience in data. Okay. Yeah. Do you want to just talk a little bit more about Women in Data and any other sort of networks people might be interested in joining?

Yes, of course. I can talk about women and data till Christmas, so stop me anytime. Richie, my first thing that I would advise you to do is sign up as a Women in Data member. It's totally free, so you'll get my slides after, after this and there'll be a QR code. Or you can just Google Women in Data. You'll have access to all the activities and events that are upcoming for our network. Next week we've got our flagship event, which is the biggest event in a lot of Women in Data's calendar. There are lots of things to get involved with for hosting a hackathon next week as well, so people with all skill levels can get involved. Basically, we'll be solving some of society's biggest problems. We're looking at women's safety next week, but there's phenomenal opportunities to get involved in lots of activities like this throughout the year. So the first thing I would tell you to do is register as a member. Follow us on LinkedIn you'll get real time inspiration and updates on what's happening and you'll be able to get involved in things that are upcoming.

Excellent. Yeah. So sounds like tons of opportunities there. And then next question comes from Mary. So Mary saying, I'm struggling to find entry level positions and internships because I don't have experience. Where can I start to get some experience? Yeah, always a tricky one, that first bit of experience there.

Yeah, it can be tough. I would shape your previous employments around maybe the types of roles that you're looking for, so you've definitely come across data. I remember while I was writing my thesis was right in the middle of COVID and I was working as a contact tracer for the Irish Health Service. I was back home working and when I was updating my CV and before I obviously became a data analytics recruiter, I was like, how can I shape this to make it data relevant? But actually what I was doing is I was talking to patients, I was collecting their data, I was entering the data into the database, I was working with the database every day and I was actually in a data role and I didn't realize. So I would sit down, I would write down the list of all the experience you have. You can totally use Google to help you out and try match it to some of the relevant experience for the role. So you'll see in the job descriptions the kind of skills and requirements that they'll need you to be able to do. And definitely there will be overlaps in previous stuff that you've done and you just have to try and make your CV as attractive as possible. And if it's those entry level roles, there is a little bit more of a buffer there in terms of you're not applying for something, something really senior with absolutely zero experience. So people will be understanding to that. It is tough when you feel like you've got no experience whatsoever, but I'm telling you, you do and you have transferable skills. So yeah, I have faith in you.

Yeah. And really just to add to that, it's like you were talking before about how you need things like communication skills, you need some sort of business skills like understanding of the domain, even if you can't demonstrate data experience, presumably you've got experience of communicating with other humans is I hope you have and you may have some business experience if you come from a different background. So those are things that you can really highlight as well in your application. And did I pitch data portfolios yet? That's going to be how you demonstrate that you've got skills. If you've not got your work experience, you can at least say, hey, I've done a project in data in this area. Go and go and take a look at it. All right, next question comes from Darrell saying, I'm looking into the data analyst certification as well. Is it respected when it comes to looking for a job? Yes. There are probably some stories on our site about people who've got jobs after taking the qualification. So actually when you complete one of the one the data Camp certifications, you get to join special data camp community where, where there's lots of people trying to find jobs and get their first job in data. You get to chat with each other, network with each other and they you get extra bonus webinars just like this on how to get careers in data. So, yeah, it's definitely worth it. Do take the certifications now. We're talking about transitioning now from other careers. So Suan T asks, how can I transition from digital marketing to data engineering? I've got eight or nine years of experience in digital marketing. That's a very specific ask. For the eight or nine years, we might go a bit more general. Chloe, do you want to talk about transitioning either from marketing or from other roles and how you go about doing that transition?

Yeah, I think the beauty of data is it sits across every single industry and vertical. That is specific. The typical data engineering skills that you need, I would list out what they are and I would start upskilling. So you have eight or nine years of work experience, you're working in digital marketing. A lot of those skills might overlap, some of the technical ones. But then if you can show that you've upskilled and you have a portfolio or a piece of work or a project, then I think you shouldn't have any issues with transitioning. And again, networking, A lot of people say, oh, it's not about what you know, it's about who you know, which is not true. It is about what you know as well. But the larger your network is, the more opportunities you create for yourself. And if people know that you're job hunting and that you have all these eight or nine years of wonderful experience in digital marketing that you've upskilled, they'll be more inclined to open some doors for you, refer you in, endorse you. A lot of the women in our community find jobs from that endorsement piece. So they just job hunt and search differently to men. So that networking piece is huge. I would totally advise you to sign up as a women in data Member, get involved in the community and the community will help you with your transition. It's difficult and can be daunting to do it on your own, but I have every faith that you can do it. That's a great career move. Something very technical as well.

Absolutely. And just think like marketing in particular, you've got a mix of people who are very creative and also people who are very data driven. And there's an increasing shift towards data driven stuff. So just being able to show off, like which tools have you used to measure the performance of some of your marketing campaigns? That's going to be a really good boost if you're trying to switch from doing a bit of marketing to a more data focused role. All right, so we've got a few more questions along the same sort of theme. But how do you get experience if you've got no experience, how do you transfer into data roles? All right, so. We've got a lot of duplicates. Okay, so maybe this one from Zaya saying, how can I brand myself to stand out as a data analyst? So yeah, we talked a bit about like, what can you do? Is there anything on branding? Is that a different question? Like, how can you show off?

In that case, what I would do is I would take the relevant skills from my experience and I would put them at the top of my cv. And I think this is my skill set, the ones that are going to be relevant to a career as a data analyst. And then within your previous role, I think I saw that she had used SQL. I would explain how you use SQL in what way? Because that's going to be relevant to the data and analytics roles that you're looking for. So it can be tough though, right, if your job title isn't data analyst and you're looking for a data analyst role. A lot of recruiters, when they're looking for candidates, they will steer away from candidates who aren't historically data analysts because. And my advice to you is to apply for roles directly rather than potentially go through a recruiter. Because what they're doing is they're competing with other recruitment agencies and often in house talent sometimes to produce the best possible candidates because they want to make that placement, they want to get you that role. So it can be difficult when you go into that career switch to work with a recruiter unless they're very, very experienced and you have a wonderful relationship with them and they've got a good relationship with their clients and they can pitch you in. It can be difficult to find a good recruiter. In this market, I can totally recommend Data Tech, which is partnered with Women in Data. They're an amazing bunch of recruiters. But yeah, branding yourself. My advice is apply for the roles directly and highlight the skills that are relevant to the data analyst skills that you're going to be applying for at the top of your cv.

Absolutely. All right, so next one comes from Raphael saying, I live in Colombia, I'm struggling to get vacancies in Spanish and Columbia. Do you want to talk about like the more worldwide situation then about how you go about getting a job? I only have a very small amount of advice for Colombia, but Gordon, you go first.

So Women and Date is primarily UK based, but we've just launched chapters in India and the US and we've done a lot of recruitment in India and also in Denmark. So I don't have much experience with companies that are Colombia based, but I would Spanish speaking ones as well. I would maybe try to research and see what those Spanish job boards are like and then there will be relevant roles there. A lot of tech and data opportunities are remote working base now and they're open to having people from different time zones or different areas work for their company. So I would maybe try research the different types of job boards based on where you're looking for roles and that would be a good start.

Absolutely. If you're based in Colombia, then you're at least in the right time zone for US roles as well. So yeah, I agree absolutely with what you said is like look at local job boards around and then in Colombia, I know Banco de Vivienda has a very good data program because they use, they use Data Camp for that. So that's, I'm sure we have more customers in Colombia. That's, that's the big one that I know of. Maybe start there. And in general, yeah, do have a look around and just see what's in your area because there are jobs in data all over the world. All right, next one comes from Gurujan saying, I need to get committed to spending time on learning every day. Yeah. Building up a habit of learning is very difficult. But Chloe, do you have any advice on that?

Absolutely. Some people can sit down and do 30 minutes or an hour of learning every day and I think they're so cool and kudos to them, but I could never do it. Discipline is huge. Whether it's five minutes or 10 minutes or 15 minutes, you will feel so much better once you've just ticked the box and you've done it. And I'm pretty sure Data Camp Offers you can choose how long you want to spend on each section or course. So I would say do what works for you. There are some days where you can absolutely bang out an hour of learning and there'll be some days where you can just do five or ten minutes, do what works for you and just commit to doing something every day. It doesn't have to be five hours every day.

Yeah. And certainly having someone else who's learning with you is going to help. So there is a Data Camp, Global Slack. So you'll be able to see it if you log into Data Camp and there you can chat with other Data Camp users if you want. You can try and set up some agreement where you push each other, you know, in the same way that you have a gym buddy to help keep you committed and actually go into the gym if you. You get a study body that way. So great, great way to network on the Data Camp Slack. And of course, yeah, women in Data has all these sort of community places chat too. All right, so this one comes from Catherine. So Catherine says, are there any specific qualifications you'd recommend for analytics engineering? I'm looking at. I have no idea what comptial is, but I think it's a new qualification, so I'm not, not sure if it's a good choice. Okay, so for this, datacamp does have a data engineering certification and it's actually very, very close to analytics engineering there. So that's probably the one you want from Data Camp. Chloe, I don't know whether you know about any analytics.

Yeah, this is a question I get asked often enough. It really depends on the organization and what they will consider to be relevant. There was one consultancy that was one of my clients when I was a recruiter and they wouldn't take anyone with less than a bachelor's degree, which is crazy because you can have so much relevant experience, be working in data for 20 years and they won't consider you because it's in their contract that they can't hire anyone with less than a bachelor's degree. So it's very specific to the organization and their requirements. So I would have a look and see within the organisations that you would like to work for, what their requirements are for qualifications and work from there and they should give some advice.

Absolutely. Because if you're not meeting those fundamental qualifications, then sometimes you might just be wasting your time. Although actually this goes back to your point. So before you were saying that if you've got 80% of the what they're asking for, then it's worth applying for. Are there some hard and fast rules on, like if you don't have this one particular thing, like if you don't have a degree, then don't bother. What, what's the guidance there?

A lot of the time in job descriptions or advertisements they'll say absolutely necessary. You need these skills. The women in Data Jobs board is just stunning because they're quite open and they try to encourage as many people to apply as possible and have the least amount of barriers there. But they should specify what they need you to have as a minimum. The example I showed you with Expedia Group saying, oh, you'll be working with Bing and Yahoo Japan and Naver. A lot of people will see that and go, I have never worked or I don't even use those search engines, how am I supposed to work? But in that sense it is totally okay that you haven't worked in that environment before. Still apply for the role if you've got all the other relevant experience. But for example, if it's a data engineering role and you have never created an ETL pipeline before, then maybe steer clear from applying for a role that that specifically is what you're needed to do. You need to be confident in your skill set as well. It's okay if you don't have absolutely everything there because you'll see often there is the basic skills that they need you to have and then they'll have desirable. So it's okay if you don't hit everything, but you'll know yourself whether you can do a job by reading the job description.

Absolutely. And if you're not sure whether you can do that job after reading the job description, then it might not be the right one for you. Yeah. Okay. All right, so next over at time. So we'll make, we'll make this last question. So this one comes from Mohamed saying, I've worked mainly with NGOs and think tank. How do I transfer into like the business market? So yeah, there's a lot of. Sometimes there's a bit of weirdness between going from like government to business or back again. Do you have any advice on that transition?

I again go back to. The beauty of data is it sits across every single industry and there are certain industries that will say we need you to have financial services experience or you've been working for government organizations, you've got no financial services experience, you know, need this for this role. Again, depending on the leveling of the role. So my advice is go for it. If you're transitioning from industry to industry. As long as you have the basic skill sets that are needed, you can learn a lot. The beauty about changing industry is that it's exciting. And the beauty about data is it sits across every industry. So you're in the right space to be transitioning to a different vertical. My advice is just totally go for it and don't worry about it too much.

All right, fantastic. Thank you for all your advice there. I can't believe we're at time already. I thought we had hours, but we scotted in the chat. Now, before everyone dashes off, I just want to say we've got some very exciting webinars for you next week. So on Tuesday we've got a session on ML Flow. So if you're interested in putting your machine learning models into production, then that's the session you need to attend. And on Wednesday, we've got a session on getting started with databricks. Databricks are very important. Cloud data warehouse. We got Ari Kaplan. So he was the guy, he remember the Moneyball Hollywood movie about baseball analytics? He inspired one of the characters in that. So big name coming along for you there. Please do show up for that on Wednesday. Hope to see you all again in future webinars. Now. Thank you once again to Chloe. That really, really informed session. I feel like I learned a lot about applying for jobs as well, so I'm glad.

I'm glad. Thank you for having me, Rich. And thank you everyone for your questions. They're great.

Excellent. Thank you to recent moderating. Thank you to everyone who asked a question. Sorry, don't get. To all of them. So many great questions today. Well done, everyone. And thank you for everyone. To everyone who showed up. All right.