MOLI YOUTH CONVENTION OJODU CITY TECH CONFERENCE – DAY 2

>> prisca asije: World of Zion. What's going on is not nobody's talking here. As far section. >> City Of Life Miracle Centre: I've got a kid. You want everybody? Shut up. Here. just me. Me, I want to shout, I love you. Our father, waiting forever? Nobody can let each other when you see down. Okay. These are that God made them. Think I got that. I cannot make. It just my mother is not happy.

And I got the job because my father is because everybody, You know. Action against everything, I surrender you You neighbor and as you step in place. So every year because, I'm going to go go. here. Already. Get us again. I'll tell you With their life. We stay pretty. good. Negoshimus God. No good shameless. >> Zion Pibowei: Hi Flora. Can you hear me? It's not. I have Flora. Can you give me? Okay. now we can't hear you. Can you try speaking? >> Flora Oladipupo: Hello, Zion. I am me now. >> Zion Pibowei: I'm Flora can hear, you know. >> Flora Oladipupo: What about now? Hello, can you hear me now? That is Zion. Okay. So yeah. me as I speak now at the moment. Did you hear me? >> Zion Pibowei: Hello Flora.

>> Flora Oladipupo: Yes, I can't hear you. >> Zion Pibowei: Okay, perfect. Yes. I can. >> Flora Oladipupo: All right. >> Zion Pibowei: Just in minutes. >> City Of Life Miracle Centre: All right, good afternoon. It's the Alex again. We're here at the Judute Tech Conference 2024, October. We started since the 15th of. And the same month. since Tuesday, we've been having A huge honor, partnering and collaborating with Professionals. and to just take of tech and Alongside all our sponsors and all the communities. That are of interest to what we are doing. The big goal for us is making greatness common. And we take or saying, let's arise and build, not only the next generation, but let's democratize their access to finding the tools that will help make.

That opportunity a reality and but want to thank all of our virtual audience who are connected. Those who have to wait in between the time of how to resolve the beat of the glitch. to experienced. and it's because we just want to ensure that everyone connects everyone has access, you know? So Right next. Is our second facilitator. She's a data and data engineer. At an end. Sorry. sorry. I don't want to mix it up. I'm talking about the data scientists. Excuse me, please. And and artificial intelligence researcher. She's a tech enthusiast passionate about driving inclusivity and innovation in artificial intelligence and data science. She has cultivated expertise across multiple areas. Including data analytics. Natural language, processing machine. learning and artificial. intelligence with a focus. On creating impactful solutions that cater to low resource languages. and underrepresented communities. She's a community manager in the Microsoft Lens. Student, Ambassador Community and Women TECHMAKERS. a round of applause to her, Please. So, she is passionate about teaching and mentoring others in the field of AI. Serving as an AI Advocates. Services. special privilege.

In honor for Rose here at the city tech community. posting and partnery with you. Please. Let me welcome to the browsing a plus Flora Oladipupo. who is joining us virtually? I should be speaking with us shortly. Flora. You have a flow. Thank you. >> Flora Oladipupo: Hello everyone. Thank you for the warm. welcome. My name is Flora Electric Football and I am in Data Sciences and he high researcher. So I'll be walking us through the topic breaking into a data career today. So, it's a privilege being here and I wish I was able to come physically to present my talk. but all the same, I would still deliver. Thank you for having me. So working on, I hope my slide is visible. Can anybody hear me? is my slice? Okay. Okay. So I mean, just Okay, so Basically, the topic for today's this session is breaking into a data career. so I'll be walking us through the different carriers, we have in the field of data that can always explore as people who are into this field.

So, the agenda is an overview of the data landscape. He did have professional rose that can be looking so skills and souls needed. Choosing the Right Data. career paths and building your career as a data professional. So overview of the data landscape. So It. no longer knows that people. Organizations. Requires that. I use data for their daily activities. and almost every field, almost every sector Generates large amount of data only a daily basis and data is something that's in the past.

Well, look at it. I use a junior past most times when did happen generated. Yeah. just thought even in even in offices where where there's no digitization. So you see tones and tons of house being kept. House of data that just being study. We most of the time we get routine most of the time. It's get destroyed. So now Organization. I moved the past that's stage Whereby they just collect data and these data are not used for anything useful. Now, businesses, like making use of those data that I've been collected. So improve their processes improve their efficiency, get customer insights. Um, use it for innovations. So nowadays. basically this data is something that's is being kept being used and and is very useful for like in the past.

So It's an example. Of ways, this data being used is in prediction of stuffs. So people I look at a sector like say weather forecast, section sector Data is, what is being used to know who if ring is going to fall in place or not? Data is, what is being used to know if a product should be continued or not in an organization. So basically data is something that is helpful and and people and organizations So, I'm getting news that my slide is not moving. Sorry. I think I'll have to share my screen. Okay, I'm on the top slide and it's slightly showing overview of the data landscape. Can some confirm if it's on the toad slide at the moments? I think I have a problem with sharing my hand and I will just have to zoom in here, I won't be able to to like, Share this with a slide show.

Okay. Okay. so that's been confirm. That's it's To be seen. so, basically, like I said, I don't know why it was on slide show before, but I don't know why it's working here. Okay. Okay. I guess you should be disabled now. Still don't like three. I guess I'll have to keep sharing our sharing my slight sugar because I don't know why. it's not smoking anymore. A case. So, Basically, we are on the whole body of this data landscape. We can do a whole lot of things is data is moving. The world is data is making us. develop generating a high solutions. That's people are now using to generate different kind of stuff. So basically, data should I say data is life. Year. I remember someone saying something like, data is is an easy oracle people use. so, So moving on, like I said there is data in every industry. that I never seek to, and it's almost like, This data field is something that is inclusive. so irrespective of your background. Register of your background.

They like something that you can find in every field. So even if you say, Oh, I'm coming from a A field of heart. I don't have it. decals or scientific skill. it doesn't mean because if I, it's something that's is also applied here. Nowadays we're seeing generative a high technology as I've been produced. You can go to Church City from seats Generated picture of people dancing for me and it gives it gives this output. Of course, when this is being built, we need people with, with knowledge in those domains. So, actually build this products. So looking at, I just fight it for industry in particular, health care. finance, retail transportation in healthcare data is useful day. Us can keep personalized.

Insights for medicine. It can be used for predictive health care. analytics. So so basically predictions can be done in the healthcare sector to know people that are currently setting disease. risk and all seem also in the final sector data is useful for detection. Trading risk management. repeal to see customer behavior. Eventually management transports. industry transport industries optimize routes that casting logistics. So basically data is everywhere and data is something that is very very useful and is an inclusive space for everyone to be a part of So, moving on to my next slide. I don't know if this is showing in landscape. among Slide 4 at the moment. Can someone just confirm? this is also in landscape? and they can see Okay, so I'm going to be talking about In the field of data. This is not a comprehensive lists. There are a whole lot of carrier parts you can take in data.

So this is just basically five of them without talking about today and like, I say it's not restricted to these five. Yeah. A whole lot more and even different names have been coined up for different rules. So, sometimes a road that you may, you are supposed to apply for which fits suits your skill. You may not even just because it's not being called the data analysis, or data science. So, but I'll just be talking about this five. basically. So, and this is data analysts career part. as a data analysis. Data Scientists data Engineer, A high researcher and data governance officer. And what are their key responsibilities. So, as, as the data, and at least your work is to analyze data sets, so identify trains and insights also you are going to be creating reports and visualizations.

Working as a data analyst. So when Year time data. Analyst. So like I said, sometimes, for a rule, you might not exactly see Data analyst needed when we check the description. There's something keywords that you find out. and, you know, all these alliance to something. I can do. so, some people say in the HR in the HR field, analysis can be done there and sometimes are called people analysts. So same also in in certain organization where they need to analyze products. Yes. Something like product. analysts. So basically, it's all the same what I'm doing. basically is analyzing data sets. you're trying to sell trying to look for trains. You're trying to drive insights that come probably move to business forward that can tell I can tell the management. what to do. All people attending towards this particular products.

People attending towards this particular services. This is what we can decipher from our data sets and these skills and IT tools needed for this is basically skills. like visualization skills, using tools like power bi Blue. Luca. So they are a lot of tools that I needed for this and Another skill needed is. the SQL skill. knowing how to query data going out to take the data from the database and proceeding to analyzing it. and also Excel is needed for this room.

So this rule is one that some people classify as in low, good, local rule, meaning you don't necessarily know. Need to know how to code. Well, of course, knowing how to code to give you an edge over on us because it gives you this flexibility of using different tools to do your job. And also it can make you stand out sports. Really it's in low quote aspect of data career. which just knowing all this, all these two, all these going out to use all this to get your results is good enough. So basically, we are getting the data, you are proceeding to analyze this data. And after analyzing data, you're going to visualize it and be able to communicate your results to your stakeholders or people who are listening, people who needs this insight.

So that has the job of a data. analyst and it's a really creative skill to have you to creative career parts because people pay Relief for people to. come on board as a data. analyst, India company. And it's one that is really being embraced even in. Nigeria companies are now like opening up to this row because they're now seeing the impact of knowing the story. data is telling them. So, next on the, on this kind of sciences grow. So as a data scientist, This row is like, seeing as a much more. Okay. I don't want to use harder, but people tend to like, Shy away from these because it's involving a whole lot of technicalities. So this is a row in which I'm skills like knowing mathematics. Is actually we actually do you, lots in understanding how it works. So basically the data scientists you are coming and statistical analysis. Your building predictive model so unlike it's data and where you are, just analyzing your detached to see friends.

To identify things. We data science. we are trying to predict and basically, you'll be working with machine. learning models. Building machine learning, models and developing this models to solve problems in the business. So, the business want to go beyond just knowing what the data is about. But how to how to predict with it, what is going to happen in the next five, ten years to this business, that's your work as a data scientists to build models. That's can actually solve this problem. because actually answer the business problems. and with this rule coding is unavoidable. You are going to code like it's going to be if all part of your job, and Mostly the tools used for this python. So Python is usually use. Most of you guys actually in businesses are is a language that is mostly using academic Academic environments.

But with businesses Python is much more preferable. So but so depending on the on the, on the language being used in the organization is best just know. this, two languages. so you can be flexible enough. No matter where you find yourself. So, and which isn't a different libraries and packages and framework that you'll be using like tensorflows. I could learn different machine learning. algorithms, you need to learn how to use. So if it's a little bit of SQL is also needed. so sometimes they are actually not. Really. battling with SQL like the data and his two boxes. is a skill to, to know, to have at and So then, moving on to the data engineering aspect of things. So this is also a very technical It's also involved a lot of technicalities and knowing how to code. So what it is on avoidable in this room. So if you have a flavorful coding, Data consider data science data, engineering. So data data engineer. what I'm doing basically is like you're at the four fronts and forefront of everyone in this room.

So yeah, the one and Linda. oh, asus of the data itself, like moving the data from this software is being brought from I'm moving it into a system that is accessible to every other person. so we'll have access to. So basically, you are taking so probably this data is coming in from different sources, from surveys from going. So we had the customers feeling forms. So, basically different sources that the businesses is having this data community from. I are creating a system in which is data. Come the managed.

So that's your building in pipeline which from which is data analysis, can actually have this. access to bring out these data from So, and you'll be making use of tools. Like I said, it's always a very technical role. So you'll be making yourself tools, like Python. Java. SQL Scale is very, very important in this room.

Like, you can't do without it. You'll be writing a whole lot of queries. Then I do spark, think cloud platform. So cloud platform is where these decal will be stored on because you're going to be dealing with a lot of data. So it's this they can't. I'm going to be stored on cloud platforms. So then so this is what in compasses, the data engineer. then we have the, A Search our room. So this is a room in which basically I still have room in which when if you have like, if you have experience with disorder rules, that talked about is very easy to be able to transition into a high research. So hey I used the new thing. he has the field. Everybody's tapping from everybody wants to be into because Um, this is the era of Generatively High because, see Church, pity Gemini. everybody keeps using. So as a high researcher, sometimes, also People in this room also called themselves, a high engineers. So basically, you are building your developing new high. Hi. query things. So you are see once.

Yeah, building From scratch, probably. A system. That can help people to. Help people to launch a product. so, you're not just using And I just building models as a hair researcher or engineer, but this time around you are going deep. You are looking for ways to develop new systems for people to use. And For this rule, having the data science. Queue is actually very helpful year because having A A deep knowledge of how machine learning model works.

We actually help. Out in this room to know how to carry out this research. So and what time? this was researching end up being published and been utilized by others. So like, for example, before the opening heights, scene builds chat, stupidity. They have to. on the good easy higher research doing a whole lot of research on how to build on existing systems to create something. new and impactful and with this skill, a whole lot of skill is needed. A whole lot of skills needed. Puts the technical side and even in the soft skill side. Because you are, you're building, you are. you're basically researching. So, Skills. like having Python knowledge, knowing how to work work, with the different frameworks and I touch.

Tons of flu I mean, it understanding of deep learning frameworks is very, very important in this room. So like I said, this is a skill that and compasses, the whole lot of things. So it's best if one transitions from having skill in data science before coming into a high research skew rule, because it's encompasses the whole lot of things. So I would say it's not, it's nice for someone who is just coming in. newly without having He hands-on. and deep knowledge of like building working with machine learning models. So then we have the data governance officer. so this is the room. Not people don't really talk about ways.

Also still an important part of Of data. of data field of the data field. So, and this is because it's not like a really. Yeah, popular room. In fact, most companies keep this rule. like, Sometimes zero hobby data, Governance of science is assigned to someone building the database, maybe database administrator. Weights actually Muslim. Most of the time we find these rules in big tech companies like Google Microsoft's Meta. then you see in smaller Companies or startups. So, what it's a road that is actually quite interesting because yeah, what are doing is you are acting as a data of no you are implementing.

Need a governance framework. So basically you are Like, written a system or rule. You're creating a system that protects this data because no usually in organizations data is something that should not be kept for a lifetime. There is always like a cycle with you keep your data. So the data governance of size one in charge of Making sure all these things are complied with. so they don't get into trouble. So yeah making sure you're you're making sure everyone understands how to make use of these data and watch this data should be done with. So basically they also call them detoxing words. So basically you are yet to protect this data. So this is not actually technical room. Also it's something. it's something that has to do with no more about data privacy. knowing the laws.

That's guide. these data and looking for ways to make people implement them. So, it's a road that's if you're not really interested in the old technical technical things that can be looked into. So, actually, for for someone coming from probably a a low background, they would find this room. Quite interesting to do. So, moving on to my next slide. That's like five.

I hope everyone can see this next slide. It's called choosing and starting your data career paths. I'm trying to confirm. so, But let me just go ahead. So basically, how do you get to choose or Else. That's these your data carrier paths. So I would see you should test self-assess yourself. You are the one who knows yourself. so access yourself. Well, what do you enjoy doing? What are your strengths? What are your interests? What is your career? I use someone who find. certain things. add to do than the other.

So that way it will actually help guide you on what? to go into. So, for example, do you like looking for patterning since like, Pain. Probably as someone who pays attention to detail that systems know what a person sees then. Most likely that analysis might be the right carrier part for you. Why you want to enjoys building systems just like seeing things being built up, putting in place and probably data engineering is the right spot for you. Or you just enjoy creating models, Just don't see this thing predictions, predicting stuff, then my consonant data science. So in looking for, in looking for the exact career, I want to use myself as an example. I didn't just start out becoming a date and a high researcher. so I'm one as one the different acts in this data ecosystem. So there was a time in my life where I was simply a data analyst, does it another time where I was eating sciences, those are not a time. I also laid my hand in, they can't governance.

So really high. I I have gone around tested. Different aspect of it and decided. The part I want to go into what really? It's something that you can always like, always something about the data field is that it's kind of Interconnected. So, even if you are idita analysts there could be a time where you find yourself in your room, that's gives you the opportunity to be data data engineer. so, so even if you start with something, just that something first then along the line, you can find the one. You are interested in. I really want to stay in or move on from so, like, for example, even one that enjoys researches, you can try out data and high research. I feel interested in data security, and structure. You can be because I said this rose sometimes they don't have the same titles. Let's try when you go on LinkedIn and you're looking. Oh, I am now. I am now someone that does. I'm now in database admission.

Probably took a course and you feel like, Oh I need a course on database administrator and you, you are really like grounded in it. and you're looking for rules revolving, around that. you may not necessarily see a road that says Database Administrator. So in another company, they may be quality time Junior. Or they might be calling it. People's. analysts, some something you think is don't pay attention to titles. Just go and look for the job. description in itself. It doesn't align with these skills and it tools. I use and sometimes somewhere, nice and aligned. Exactly. Like, 100% with your skills. um, that you have but you know that, oh, I can do this. Then this and this is similar see, maybe 60% similar is still a go-to is still something you can venture into so, so basically choosing your career paths being open to like be hoping to first of all, starting out start out in something like and if you, it doesn't work for you, you can always switch.

So, another thing you may want to explore to Um, so basically look for your interests your strengths and what you eventually want to do, does it highlight with your career goals And also looking at the different aspects of the data field. Like I said, Looking at, looking at it from the domain side, so probably studied medicine in school. I am looking at also how to go into the The data career paths for the ELDS system in a health in any else. Organization. You might not necessarily say CMC, They want a data scientists. When I say something like I want about informatician about martial channel is also someone who does data. In the science. Domain. So that I said something don't get carried away by just high tools. also look beyond that. So you might not want to look at how to get these skills.

This power informaticians use. So again, they look at your career goal. doesn't align with something I want to do. I am a lawyer. What's? What's which of these data field? aligns to the carrier go? I have for myself as a lawyer. Do I really want to download to be someone who implements policy? Who want to do these, do that? So you need to look at it. The value. Italics career go and we dance it can guide you in how to choose the career paths you want to go into.

So looking at specifications and learning resources in the data field. So there are a whole lot of places you can learn from Boxing. don't focus on just getting certified voice that focus on getting this skills. So because The truth is most of the time companies. Don't look at your certificates and actually want to see what you can do this. The previous you've done to show for it. But and I look into getting certificates as you learn, you can explore this. This site. So for data analysts our recommend Google Data, Analytical course Actually causes the very comprehensive course.

It's a good one. It's right for newbies. People are just coming in, newly into the data field. The course is a great one. if you can. do it, do it. So it's I think it's available and Coursera. so you can just search online for his Google Data and it's Professional. course. So there's also the Microsoft's course. So the Microsoft courses is one. That is very, very accessible. This is a free course. If you just go to Microsoft Lane a Microsoft lane, your teacher to use Excel to child, to use power, BI so, your house. So this is not limited to this platforms. You can always search Online. As a data analyst. What courses are having able to me you see different results? From where you can learn from. So, for different engineers, you may want to get certified in In the different cloud platforms. That you make use of. So there's the AWS Google Cloud there is also has here.

So this like I said it's a schedule like it's like improve. Oh you know, these watch I didn't real profuse. You be having a I've been issue of it that I know this. So for data science is high beam platform. It's all the data come Data Science Track. I can explore so learn from then for yeah. I research there is the Stafford University. High specialization course, there's also this mitos Um, MIT courses that you can also take. So usually most of the time you see universities taking this courses, there's also the deep learning Specialization Course by hand RENJI is also available coursera, painful Dehyde developers the Microsoft's Beehive Certification Lab News desktop Specially schools. Now you can explore so it's not limited to this.

It's just simply have just check online what courses available to me as a data sciences as a bit and least like that. So, moving on. Okay. I'll be sharing this six slight. now, so tips for getting started as a beginner. So has a beginner, it's best to have solid foundation. And familiarize yourself setting tools. So for example, has a data analyst you may want to familiarize yourself is tools like Excel. Barbie. Blue Line. It's well, like, the foundation is really key in these, like having a solid foundation sets. you up for Greta momentum.

So Understand the foundational concepts. Not this concepts. well data structures databases. Very busy, programming also Python SQL. So there are different platforms. You can use to So solidify. your skills is fairly especially SQL skill, because SQL something you need to keep practicing for. it to seek. So Um, platform to practice my SQL skills. I think there is one called Alchemy So there you get different queries and you are told to write it and just called based on it.

It's almost like a game, but this game is helping you build your skills. basically, so then it slicing platforms. To build your skills. So sometimes it's not everything you get in a course. In a particular course, you are picking. You need to also search Other courses. YouTube is very helpful, you can. Go back to YouTube to explain some concepts to help you out. So taking courses or courier data Camp UDEMY to build your technical skill. is also helpful for beginners and working with small the assets to make you feel comfortable with data management and analysis.

Don't jump right into analyzing 1 million rules of data. Start small. you have to start a small as collecting data by yourself from people around you, sending a survey out. and working with hundred years of data, just based to really understand the basics of it and also consistency is key. Like you need to keep learning consistently consistently. It's better than learning at a long stretch, then going on holiday, they have coming back so like it's better if you learn.

Daily or weekly to improve your skills. Even if it's a shutting tabouts, like or two hearts, but they Oh, five hours in a week. It's something. it's better that to do that. that way and to just stay we from learning. So this learning actually helps you post your skill. to help you. Um, gets better. That's what you do. Can also join in communities. It's very, very important in communities. You get to meet people that can help you uplift. your careers. So when I see people, it's not busy, it's not really someone that is already. big necessarily, sometimes your peers. Can help you. Shop on your skills. So imagine coming into a community like Data Science. Nigeria DSN Community. you could find someone who is just like, who is a newbie? Like you they just starting out and you can say, Oh, let us be accountability partners, and two of you are building projects together working on prayer together.

posting together. Um, but we signing yourself together working collaborative together. This, you are helping yourself so you don't necessarily need to have like a mentor. by say that is always there sometimes. peer to be a mentorship is good. just as well as having an accountant. that can give you a rude. my phone. What to do? so, so John always join communities, they had yet to help guide. You give you that work map and link you hope helping it. work with people. That's that's maybe impactful to you. So then the last aspects, talk about the building of what's full you. it's projects. So like I said, since kids is not everything. Not. when you go for a job interview, they look beyond the certificates. Okay, I have this. okay, I hope that's okay. okay, prove it to host that, you know, it and this is where your portfolio comes into play. You can always show your portfolio.

I was able to build this. I was able to build that and this also helps you because I starting out You may not necessarily have a job experience in a job interview, but you can be able to show these possible as proof of knowledge. So you can always find data sets on kegu or source data sets. online that will give you sample data. and I can use to practice and also make sure to document your work on GitHub or you can view the personal websites. if you have. if if you can That's also showcases all your work. So ensure that this project that you build is something that is very clear to anyone who see the problem you're trying to solve. You can see the methodology using because it's lose you use and the insects that you can So this is very helpful, so so basically has a beginner try to be part of communities that helpful be consistent with you. It's your work name. Build a portfolio. National case. What you are doing? So with that, I've come to and the end of my talk and I don't like, there's room to ask questions or basically, this is me.

At the end of my talk. I can connect with me, I find your LinkedIn on Twitter at Flora. like people. >> City Of Life Miracle Centre: By the gleacher. and she waited back and were able to still achieve a lot more. and so quickly, we just it's not in our Ways to Keep People Waiting for Long. But of course, we just have one more session to go. And this is gonna be live. it's gonna be interactive. It's a panel session. Are we ready? Okay. So do we have questions? Let's start. Do we have questions? We have questions. Just kindly signify? At any, if we have people. in the virtual, Room. Should we have any? questions, please? Kindly send it forward or start reading them? Okay, so So is he ready for the panel session? All right. Move on to the last session. It's a Honda privilege to be working with some people way. Who's a network? has been very instrumental in helping us to find some of these.

Of this new part where forging together. Earlier in the day. We had a Nancy or Mandy. It's quite an honor. Thank you. so much, really appreciate and that was so professionally done. thank you. And I hope that I'll have time to see you much after this one and then also in the room We have. A Boa. I think that's a surname was a, but I would, I sorry, I just want to do this cuticle before I leave this place. Then also we have jumoke care. Am I correct? Yeah.

Don't mind us. You know when you're part of the organizers the tendencies that something's may just fly out from one other. So please, I have would do that properly. So I leave the stage right now. Zion or the panel session, please say round of applause to So, I'm invited first person. You know. all right. Zion people will. forward. Please a round of applause as it Okay, good afternoon everyone. I hope you having a great time. Good to see all our beautiful faces. All right. So, we are going to be going straight into the panel session as we wrap up. As a wrap up. So, we're gonna be having amazing panelists, you know, in this session Amazing people. Amazing technology professionals. We've already seen one of the faces and the session this morning. so please help me. Welcome Nancy. Amandi. The data engineer prolific data Engineers. Solid data engineer. And when you editor engineer, I just know that what it means is that you will write code, your eyes will you as you thought it? Alright.

So, please have your seats. I'll be inviting our next panelist in person of Fortress Abue. For this. abuses cyber security engineer. We've been talking AI and data, you know, for almost entire entirety of this conference. let us take a little bit of Perspective. And let's let's bring some fresh air with cyber security. So join me. Also, welcome photos of UAE to the platform. And then we'll be moving on to Jumoke. I'm sorry, if I if I pronounce this. this sodium rugly. but you more care? Odom, as she comes to the platform. She's a data. analytics lead and even though she would not like me to say this, but she's also an AI engineer. So she's these guys in a city and at least pushes an AI engineer as well, right? So we're gonna be Going straight into the panel session.

so, before I get started, I would just like to introduce, you know, each one of the panelists. So starting with fortress are beautiful disability and cloud security engineer. She's a well-seasoned professional with experience and expertise in information, security and cloud engineering. It's also a published author and an AWS community. builder. Passionate about promoting security, awareness, writing and sharing knowledge about cloud and cyber security. She currently works a trend micro a global security, a global cyber security leader as a technical account manager. So, Gentlemen, you're having an experienced professional yet? Please don't mind their sizes. And, you know, you know, people like us were portable but might see, right? All right. So, thank you. very much for just a ba for making your way here. It was a one and a half hour. Trip to this place. That's how far she's coming from, right? then, let me just quickly introduce Into more care. Okay, so One second. Alright. You know, when I see some of this professor, I just get my mind.

Just I was doing. You know, this provides I reach. So, Jumokee odoma is a dedicated data science. professional with three years of experience in the field of data analysis. She holds a Bachelors and Masters degree in microbiology. So what I just tells you is that it doesn't matter what you studied you can still come into the data or AI or tech in general, right? So she will see bachelors and Masters degree in microbiology and biology informatics. respectively. So a bachelor's degree in microbiology and in Masters degree in buying informatics. Demokers, consistently demonstrated a commitment to learning and growing with the data science. industry by expanding expertise to encompass various aspects of data science including machine. learning and data engineering. In other words, she is and all around. In addition to this is also participated in prestigious data science, fellowships both locally and internationally including Kagul X outreach and Digital Explorer.

This experiences have not only enriched, a technical skills but have also deeponed our understanding Of the global data analytics landscape. Passionate about mentorship and guiding others. She enjoys helping individuals kickstart their careers in data science and sharing the relevant opportunities with our community. I was thinking you're gonna put your hands of you know, put your hands together for that. that's such a rich profile and of course, Nancy is. No. You know, is no. No, what's the word new face to horse? We had in the morning and Nancy on Monday is a data engineer. at the Nigerian E-commerce Company. She began a career as a bit on a list and quickly, made a name for Ourself in the Nigerian data industry, true high insightful, articles and impressive performance is in akathons where she often emerged as winner a passion for community involvement. learn how to actively participate in videos. data communities, including the tech engineering community Young Data Professionals. and was a proud member of the young data professional. IDP. It's a very vibrant community. I promise you.

And women in Data Africa, in this group should not only assisted Fellow professionals with technical challenges, but also helps stimulate some important discussions by asking insightful questions. Recently, a significant contributions are significant contributions were recognized when she received the first rising. award. Fast. Fast Rising Star. Award from the Young Data Professionals Community outside of Work Nancy. enjoys watching horror and three lucky dramas already in Data related to books. so popular for that amazing profiles.

Amazing profiles. So just get there right away once again, it's a pleasure to have all here and Of course, we are going to be covering a number of a number of things. And the title of this panel session is Breaking Barriers in Tech. Breaking barriers in tech. I was thinking You're gonna put your hands together. I mean, we all want to break barriers in tech. So for those of us starting out, In tech areas. You know, this is an opportunity for you to glean insights from people have been there for those of us who are already in our tech areas. There's an opportunity for you to clean insight into people are probably experience the challenges or you're about to experience or you're currently experience in one way or the other.

There's always something to learn from sessions like this. Thank you. very much ladies. Once again, it's, it's good to have you and the good thing about this partner session is that they are all ladies And so in, if you are a woman your Lydia, and you think that you might not be good enough or, you know, Maybe you are not a fast. learner, or you have one or two limitations or the other. I think this can inspire you because it's a very excellent people prolificing. what they do. And they've literally seen a lot of, you know, the the ups and downs in tech and of some mounted it. All right. So very quickly, we are going to start. By looking probable, I would like to probe a bit into, you know, your background and careers. and how you got here, right? So can we start with Jumoke? Can you just tell us about your background? How did you get here, right? You didn't just suddenly become a data.

Analytics lead. What's as a journey being like Thank you. and so, as already my bio, I started microbiology. And then after school, I decided to transition to take And then I started majorly with online courses coursera on my own. But then, I moved to boot camps. I started with Africa's boot camp. Where I learned data science and machine learning. Um, people often feel like it's so easy to transfer to things, I don't For some people, maybe easy for some people. they don't really find that easy. So for me, after the major boots come that attended I stayed back and I kept learning.

From online courses data car data course, coursera Anywhere. I can see free resources. I basically started learning with free resources actually. And then, so at the point where I started, you know, job hunting, I after like one or two months, three months, I've not gotten a job. I decided to volunteer and then I start with games concerts organized by Mr. Zion here. And then also Ppts.

So I volunteer. They just so I could have like some tech experience and something on my Siri. And then fortunately, I was able to secure and internship or so I started as an intern after four months. you read my performance and I was converted to a full staff. And then I basically learned on the job. I mean, I've been learning on my own before then. So I learned so much more on the job. So I said on the job from like two years more and then at some point I became the lead and then I just kept of Skilling or Skilling or Skilling. So at some point I started I got tired of just doing, you know, that's I started looking out for international opportunities like Kagwex Mentorship like how to treat you open. source contribution. like just I explorers and all of that. So, there's been my journey to from learning on my own to put calms to volunteer volunteering then starting as an intern.

I mean, and then learning on the job to upskilling. Then I think one of the things that also up to my career was LinkedIn. optimization. So at some point when I got A bit confidence in my data analytics skills. I say I put in myself out there and sharing my learnings my progress and at some point I say attracting recruiters, I know. So that's been my journey. That's an amazing one. Thank you very much for that.

And I like the emphasis on, you know, putting yourself out there, optimizing your LinkedIn profile just making sure that you show up, and it's like telling everybody I'm here. I'm here. All right, so, I'll pass the question the same question to fortress as she tells us, you know, a bit about I mean, tell us a bit about your background and your current trajectory points in this point. Okay. hi everybody. My first introduction to Cyber Security was Well, I was in my internship. I think, in my 200 level, I wasn't even my major internship. I was interning. We keep EMG. and then I was like, with your tech department. Or I had the opportunity to.

So there was a separate department for Cyber security and like I was just seeing it for my father like, oh, what was on there was going on and on it. I think I had some interactions with some of them and I just go interested. So when I go back to school after I internship, I found myself wanting to learn more about it. you know. Nice. They're taking courses on Coursera as well. And you know, just leaning by myself building my knowledge and yeah I don't think even after that.

I did my internship. I didn't do this internships. Notifico. I still continue taking those courses myself. And then fast forward to 500 level. When I was graduating, someone, I think doing as at the time I was taking my final paper my final exam, someone sent me a link like and everybody. one thing is also putting yourself out there because everybody knew as I think that oh, she likes cyber security. And so if friends sent me Link and I was like, Oh, there's a cyber security program.

Or Africans from different countries Nigeria Kenya Ethiopia, South Africa and all of that. and I I didn't think it has anything. Listen. actually kind of forced me to apply. I apply and surprisingly out of over like, 200, 300 applicants, I was choosing and I think About 15 of us. We're chosen from different countries and I was one from Nigeria and that was the start of my cyber security. notice that? Well, yeah, I said having like hands-on experience. I did like, he who several security program and from there of hearts jobs and they, I really started doing, you know, hands-on stuff beauty in my knowledge, really seeing how we find out, and it's been about three years now, and it has been a great journey. I've had several rules, several experiences work to save our teams and it has been doing great. Oh amazing. Amazing. I'm tempted to ask some follow-up questions or To you fortress.

So, let's move on to Nancy. I feel like Nancy. I talked extensively about you know how she started. Excuse me. So I don't want to, you know, force to go back there. Instead, I want to ask you this one question. When us, when the journey when you started a journey were there times when you felt like giving up when things were so challenge and it was like, Why is, why is this whole thing just so, And how did you like, how did you situation back then deal with you? How did you ensure that you still kept going ahead? And you refuse to not give up in the journey because I know that nothing particular has been.

One of those persons who has had a very unconventional way of actually getting into into the into the tech career and that's taking some dedication and commitment. So, I wanted to just speak more to it. Eduted. last year. so, at times when things actually difficult or just ask myself and be like, actually I'm not just go back to my pharmacy and stay there. but then I think what keeps me going is the euphoria gets when I eventually overcome those challenges and the fact that I have people around me, I always see that I'm a product of people have met, a lot of people that always have my back men that have been in this space for years. and then sometimes even reach out to me and they're like, how is it going? How is everything going? So the encouragement, the fact that these people are always there for me to reach out if I'm having any challenges, the Father, always, come to me and ask me if things are going well, that's actually what keeps me going.

So one thing I'll say is As you're starting out start connecting with people have that support space and I'm sure you're going to go far. Yeah. amazing. Thank you very much for that. I'll come back to Fortress. and I want to ask you about For cyber security in particular, what do you consider to be the essential skills for people are trying to break in to the field and also for those who already here already, who I've started, you know, careers and cyber security or even cloud in general? What do you consider to be like the key things that can help them? stay relevant. so, As a beginner, right? In cyber security.

First of all, is the knowledge, right? You know, that you so important and it's after building that knowledge, you now start thinking about skills. So some areas where you need to build knowledge and skills. So, for knowledge, first cyber security, basics, whatever security, all about how is it? How is it? How is he applied? What are the different areas, those things are so important, the basic concepts, right? And then we and then Yeah, you're not talking about skills. Number one is networking, right? You really need to know your networking skills in cyber security if you if you desire to have you a career in cyber security. This is because Most attacks come through the network, right? Most in fact, most most components of tech Security. They rely on networking. So you need to really understand that. And then also operation OS. you different types of OS, right? You need to know. Okay. how do these things work? Does that some very very important parts of cybersecurity.

But then the knowledge is also important. so don't push your skew and I don't know. I think they both go hand in hand but she knowledge. the basics of several security concepts and then when you find that you now know narrow it down to these. cues that you should have. Um, yeah. What? generally networking Oasis. Does that? it was important thing. Amazing. Thank you. very much. What's a keynote speaker, is they talked about about building conceptual knowledge and then you know, building your skill on top of that, you can very dangerous.

If you've spent years in your career by just winging it and then a time comes where you now need a particular foundational of fundamental knowledge and you just mess up at that point, it can be very embarrassing. Trust me. Now I want us to talk about technical challenges in a different rules out. How old? Our allows to start with more care on this. Obviously, you've experienced several technical challenges. Can you highlight some of this most significant ones that you value. You've experienced of what? What was it? did you? Why did you actually overcome this challenges in your in your room? Okay.

So I'll just mention if you that can remember. So when I first Actually, because we're in a very funny, very blessed country. and what you learn online. Whenever you always eventually makes my start working. I mean, the data you learn with online we are mostly cleaned data and everything is structured putting guitar views for you and I was going to start working. I started working with the company that didn't even have, like database was not even accessible and all And it was just funny and I mean, and people have mentioned to the diary are going to lend it analysis.

Analytics that I might even need to learn Python or both in this country. You most likely have to use Python because there are some situations that will come up and then you need to maybe web scrape or or something or just well, what I'll just if I say is one instance. So recently, I took up a project meaning that it data analyst and I mean they specified by the time I got to The editor was MongoDB. and MongoDB is not the evolutional database that I can easily query.

And then there wasn't even a proper data team. no data engineer. So, he means that had to be data engineer. How to look for how to get my data from Mongo Divot. Suppose Grace, which is the English national database. so that I can query it connect to Power BI and all of that. So, I found myself reaching out to NCAA, please. They want to Dominic City engineer. all of you sudden and then so she she gave me like options to this do this duties. You want to minimize cause disease if you want to minimize time, do this and all and then thankfully, I I know how to use Python. so I had to use, you know, some Python packages. I find Mongo and all to move the guitar from Mongo, Db2, because great, so that I can now do my data. and I'll list. Stores. And then, as you are getting done with that, this happens saying they want to be the recommendous system and I'm like I mean did analysts nothing machine learning engineer. So this are some of the technical challenges that have encountered in my career.

Like people often feel like because they did that professional. I supposed to know everything. So you might find yourself through and then there's no data engineer in the thing to help you. Day out of computer engineer or they wanted to go for that to the building machine learning models and So, as I did that, as a data professional in my need to know one or two things about every other field. One or two things about everywhere, everybody for you to be very, very relevant in your even beyond data data space.

You might want to learn about software, not so much. So to engineering, well, at least a little bit about social engineer because you also know how your date That's all data points. You're interacting with is being collected. With the point is not being collected. I know that this is possible. It's been done somewhere here. Maybe you want to know about click sessions and oh, and you need to know. So this are some of the technical challenges having contact in my career. Amazing amazing. So it's like you are constantly being pressed as a little professional to know more than just a specific nature working on.

Otherwise, you can literally lose your job. if you cannot delivery. So, let's move on to Nancy. And this question is about data, overload, right? How do you deal with data overload in particular? You have a all of a sudden, This is spiking, you know, in data and you need to manage your pipelines. Well, I'm asking this question because a data engineer, you would typically do with, you know, such times when you have a lot of data to handle, How do you handle those challenges when they arise? so, in handling data overload, first of all, you might have is Sues. That's why one thing when you're building your pipeline you need to even think ahead, right? You think of things like scalability and when they are changes in the data volume and data velocity. So in case is where there is data overload.

That means in the AT that point, you need to go back to your pipeline. so that because when there's data overload, for example, here might be in when the data is moves to the warehouse. And then when the whole transformation happens, so you need to look at the tools that you've used and see if he's actually handling that much data. that you have at that point. if it's actually handling it. Well, that's one then at that point when you see that and if it is a hand it will and that's when another thing you need to look at it. The server resources, the server where these tools are hosted at the Resour.

Enough. Because if there is that data overload and the resources are not enough, your server might even shut down. So that's where you wanna have to reach out to if you're not the one in charge of the infrastructure. you have to reach out to the infrastructure manager to help to upskill the resources of the server. Just so that your server won't shut down.

And then your data injection stops. Yeah. That's one way to do that. Okay, that's amazing. Thank you. very much for that. I mean, that's that happens. a lot and if you don't know what you're doing, you can just break the open plan and you be in trouble. Now, I want to go back again to fortress and you have dealt with a lot of teams, You've walked across functionally across, you know, with diversity of you know across the diversity of teams How do you? How do you collaborate cross-functionally across disciplines? I'm sure you work with software engineers.

Devops, you know, people from different technical backgrounds, right? How do you ensure that a smooth collaboration and you are as much as possible? making the right level of impact. But for you, in terms of your own rule and for the organization, Okay, so Yeah. So cyber security is needed in like the difference first different areas of tech whatever it is that you're doing. You need to secure whether it is data, related network applications API, you know, security is needed.

And I don't even forget of this phrase, um, security. not after, or security with whatever you are building, right? So we try to emphasize that, although, it is not something that you really find everywhere. We try to emphasize that it is not after something has been. builts, or a product has been done. and all, then you now be thinking, Okay? how do we make it secure? It is. well, it is being built and while it is being done, so, So I for example, we have like Devops, right? But it's development and operations and oh and then we also have what we call devsecops right where we now. Okay how do we how do we include security into these things? So one way to do that, one way to collaborate is understanding The goals of each team. This is our goal from movie development and operations team security. This is our goal. This is what we want to do, right? So understanding goals, understanding what the other team wants to do and then working harmoniously with that for the good of the product, the users and the organization.

Yeah. Okay. Thank you. very much for that. Still, you know, still on your fortress. How do you balance data utility with data security in a hyper connected world? So, you know, we have issues around data privacy. But there are some teams that might just want to be, you know, there's this phrase. is it beautiful fast? and break things or something? like that ship fast and break things.

Some people want to move very fast and data privacy is a concern but that teams who want to utilize data to to solve certain product problems or to infuse into products, right? How do you balance this need for using the data available versus the need for securing the data? Okay, I think the answer is in the CIA Triad CIA CIA. Asian study means confidentiality integrity and availability, right? And confidentiality is ensuring that data is not cannot be seen or be used by someone that should not have the access, right? The best. whoever is not supposed to access. It should not be able to access it. That's confidentiality integrity is ensuring that data has not been tampered with, nobody has touched that data. Anybody has Nobody like that has not been authorized. has tampered without it and I believe it is ensuring that data is available at all times to all the people that have the required. authorization. So based on this question is like a combination of all these things? because if data is only available but it's not confidential, it is, doesn't have integrity then security has been compromised, So while we try to ensure availability which is also a very important part of the CIA chart.

We also ensure that it is secure. it is confidential and there is integrity right? And of course, it's not. It's not always so easy and straightforward. some people just want to have it. They don't care if you secure or not. if something that we always try to emphasize especially through security consciousness. and awareness, and providing tools that make like these things easier for the users here, Amazing, I think that's a good one. So essentially it's like you ensure that you are shipping the organization. Culture. Such that teams are way of the need to have all these things.

Infused, and then you make all the tools that they need to use available. So they don't have to start stressing about or an external integration or how do we, you know, infuse this into the solution. Okay. so let's talk a bit about, you know, barriers like, you know, ethical barriers. We'll talk about it from a data lands are from a cyber security lens. So I'll start with you. Okay? What ethical barriers are you, you know, have you seen play out when it comes to data you know, things around ethics misuse abuse and all that. And how do you see? do you think that this can be overcome? Okay. So the some of the ethical concerns when it Comes around, first of all. Consent. and transparency. in the sense that before you collect data, you need to get The person to give you. You go ahead. There was recently an issue around NH NHS using millions of people's data without their consent. And it was really An Asian tug of war.

So, before you collect data, you need to take a consent from the person and you have to be very, very transparent about it. What are using the data for? Is very, very important. Oh, you're using the data? You need to Be. Be privacy. privacy conscious right? On, and you also need to think of security as well. Because these people have trusted you with their data.

They don't want to go online to say their data just some So you have to be careful of privacy security. ensure that whatever platform you're using the data or something. They It's not prone to security, breaches or something. Yeah. so I think that's a major or ethical. of guidelines that have been put in place to ensure like a proper use of data while collecting it were analyzing it and then okay. another thing is also the place of You have to be sure that while. there was recently this issue of Ai classifying gorillas as Africans and something. So you really need to Take notes of. you know, being by us. When you're using the data you have to. if you're something that I have to make sure they are using it particular, something. mentored way you know, there's inclusion and yeah in the data. so amazing. Thank you very much for those examples. I would like Nancy to also answer that question to Ethical barriers. What what have you seen and How are you, You know, our view plead your role in addressing or solving some of those challenges? Okay, in addition to watch more Casey, I'm going to emphasize on the data privacy parts because I've had a conversation with someone where he mentioned that.

The data engineers, they just put data to the warehouse. Anyhow. and then he's left for data analysis to, to their modeling and get the data. they need. Now in case is where they sensitive information. Like the username, the phone numbers of customers or email addresses. That particular information is not supposed to be seen by everyone. So in cases where the times, you just pushes data to the warehouse and anybody that has access to warehouse, they can do whatever they want. Then that's actually an issue in terms of the it's because I mean, you need to look at the particular data that setting eyes. should not see and look for use techniques that you can use to gather data. so that you're wrong. I don't see techniques like encryption or just limiting it. so that seeing because by the time this the time that it starts building the Dashboards.

And visualizations. They might not take into cognizance the fact that this particular team should not see this data for example, supply, this is supply chain, operation team, they have nothing to do with customers email addresses, and phone numbers, compared to things. like customer retention. So in terms of that, you need to get the data. so that it's only goes to the right eyes. So yeah, amazing. Thank you. very much for that. That's very key. and sharing that only those who need to access the particular database or particular data source are the ones that made to access that.

And that's very, very key. All right. Moving on. I would pass this question to Fortress to talk about Cloud technologies and adoption, right? So you do a lot of cloud technologies. We deal a lot with that. How do you for start? You know, the adoption of Cloud Technologies. We still have a lot of traditional organizations today that still have you know? Traditional servers that have diverse limitations. And so, you know, I just want you to just speak to, you know, how audio ensure that or how do, how do you try to? to foster that need for organizations to adopt cloud technologies? Okay, so Yeah, that's a very good question because I think so. years back in my organization, we're trying to look for how to make people move from.

Like from On-prem tomorrow, like SAS and to the cloud. and all of that, right? Our clients, our customers, and it was, it's okay a long time, right? But I think one thing is emphasizing the benefits of the cloud, right? And cloud technologies. what does Cloud have to offer as opposed to On-prem? Where you have your On-prem servers and all? and when you look at it, you really see the difference right? right? Number one is capability. If you have your environment in the cloud, it is easier to scale as as needed as operations. demand. Right. For example, I set up something I set up like I said of a I set up a maybe service or something on the cloud, right? And I noticed that okay, my users are getting more day by day by day by day by day, and Is able to scale by itself or maybe doing a particular day in the morning is where I have more users using using this systems that I have set up, and then in the afternoon, probably like very, very few people.

It's able to scale up is able to scale able to set that up using the cloud. Well, it's not. it's not actually forward as we don't, pray scalability is one benefit Another benefit is security right. The cloud also has like, efficient security their security from the cloud providers, and there's also security that you would put in, right? but they also have security. There's availability, right? So with cloud service providers, the offer SL is like service level agreements that talk about. Yeah, availability system and I believe it's like 99.99. right? So, yes, always I believe it's your watching. Cloud. then cost efficiency. It's also very cost effective because for example, now I need to do something and like like compare going to buy these servers and putting it on PREM having somewhere, you put it in a conducive environment and all of that as opposed to just going to the cloud and setting it up Accessibility is another benefit, right? You can assess it from anywhere in the world that I travel one in.

Why can't I say the servants where I come back or I have to tell this person to come here and do something for me on So, these are benefits and so high. like just highlighting these benefits has actually like really helped us with with. like, we got to convince in people. so adopt cloud and all of that. Yeah. amazing. Still on you. For trust. What? I mean, we're in the AI era. Right? What what's impact? would you say AI and automation in general has had on Not your cybersecurity, but even on your own role, right? Do you think it has enhanced and helped or do you think that's in that some things and pose more challenges that you now have to start looking for ways around solving those challenges? Okay, so automation and ai right automation on its own.

I think has Like really helped, right? Because with automation, even in my own in my particular role, I think I've been doing like a lot of that this past few months, where I sort of, I set up a system where anything happens in a customers environment, Right? GT tally. when your solution if there's any malicious activity immediately, the customers get an A lot, right? As opposed to it will happen. I need to just be like in the dashboard and everything they may be once in the day because it was going on in my environment.

And oh boy, if it's something that you need to pursue. it's arriving immediately and customer care. Is immediately. I'm like, Okay, this is happening. So automation has been really, really good in for my role as a security as you ai. I would say like it has the good and bad sides, right? Because AI is ai, is like Is an additional risk surface. How do I put an additional risk surface? Where we do with more use of AI? idea? Yeah. More risks associated with it right For example, last week or last weeks, I heard of I had of how hackers To user data.

Still users chats, and all of that. from an AI assistant, you know, using a critical vulnerability. that it had also deepfake impersonating people to get money from an organization from a CEO from, like, a financial Person in the, in the company, right? So that is the bad side. but it's also has a very, very great site where we are also talking about this automation, you know, monitoring systems, monitoring environments to quickly. get alerted of malicious activity. So yeah both of them, good. Amazing. Amazing. I mean, we all have our stories about here. but at least let's just let's just hope that, you know, we not just hope, I think with there's need for practice steps to take in terms of ensuring that we can call the downsides or negative side effects of AI mobile.

Our role here, I can actually be All right, still on this. AI and automation. I would like this question to be taken by Mitchum. Okay. What's in fact, as automation and AI had on your role and as they've been downsize that you've identified So for me, I haven't really been able to do so much automation, right? Well, I used a lot. I had work. Asylum. When it comes to Debuggie, I started to be a lot of facts a lot, right? So he helps me. You write scripts at all. Yeah. As also really been really been it's not being really utilized in Data analytics. I mean, people are not building AI boats that can help you analyze data.

So you just upload your CSV and then just prompt the AI to, and to analyze the data for you. although that I might just basically scrap the script. The surface it may not give you in depth analysis or May not give you. Maybe. like, maybe domain or okay? For example. I, I am Nigerian. I've worked in an e-commerce sector for three years. If I give an e-commerce data sense to an AI analyze, I mean it's just going to give me the surface. It's not going to report the insights in the same way I will. Because I have some, some Nigerian knowledge. I have some knowledge, you getting to regarding to Ecommerce in Nigeria, that it does not have that. I might not have been trained on, right? So I've really I've tried my hands on using AI to analyze data, even some platforms, like Fabia now.

Now integrate ai, like, for example, you can use, there's this feature on power, bi I will just, you know, prompt the AI only particular question and then you rest the data analysis expression for you and then even suggests right there. Stuff like that in power bi. there's a forecast future week. I used to predict maybe you have revenue. You guys. to know? podcast revenue of July, maybe 20, I mean yes to come. I know so AI visually useful of personally for me. I use AI a lot, but Julie is just issue. I have with. Yeah, even me personally, this people might tend to get really over dependent on Yeah, I mean and I guess it went way you come across. I need you and before. you even think of writing code yourself, you want to go to church activity first? It is really really bad. Yeah. So well, yeah, it's really been beneficial to me in a busy.

Thank you very much. So I think you know what time is fast. So, I'll just ask this one last question and out. And this question to Nancy and Fortress. Where do you see? Tech careers going. So what do you think is the future of tech careers in just 30 seconds? the future of tech areas, but as data or tech in general, Okay, I'm going to stop on. Data engineering days. Because that's So yeah, I see Detention going to his going towards architecture because now I'm ready. Seeing job rules where they are Looking for a data engineer. That should know, both reading of pipelines, and then knowing cloud architecture. So they're not looking for those. that are building. just pipelines again. So if you are looking to go into the engineering, then you should just know that at some point, which might have to choose was that area? So yeah.

Okay. so, fortress over here, so, I still think that most tech areas as we have it would still be very relevant in years to come. but I think, for example, with my future as security, they would be more adoption of AI because we already seen more AI more use of AI. We are going to see more through its coming because of that and therefore cyber security. also needs to get most sophisticated with more tools and the adoption of like good AI to fight against like the bad courses of AI and all of that. So yeah. all right. Thank you very much and I think that's a wrap for panel Session. I was thinking we're gonna put us together for amazing panelists. It's been a great time talking with you, ladies. And I really appreciate the insights.

Yes. So much on park from that I'm sure would even still need to go over the session again for us to digest, some of the things that I've been talked about in this session. Thank you. Once again, it's been great. Having you our Now. and over to Mr. Knee. Yeah, please, let's get ready for pictures, please. Tolu, are you here? And I just want to say we thank you to all of you. Really appreciate you. Jumoke care. Precious. Fortress. So she was all that changed her. Okay. So what really appreciate you guys and I want to say these we have a boarding, we want to reach out to more people. How can we duplicate the knowledge, guys have to a lot of people. I know it's gonna include us having spending time amount of time. it's going to require some resources money, you know, logistics and all of that. But we sincerely want to do this. and, you know, I'm working with my brother here to ensure this is workable one or half a lot of people.

Selected people people that, you know, when you invest your time, you'll be proud. we did this. I'm a product of that. I'll be calling done that. And I've seen some of my proteins doing very well in terms of very well. those stories inspire us. In fact, I was speaking with our elderly father here. Who's going to spending on some of us have had 44 years as a chartered accountant and, you know, He's wild at what you people have been able to achieve. I mean, it's struggle and pushing on and I mean your resume is look very interesting to me. have KPMG, right? I'm correct. It's amazing. A lot of our plus to them. I am pitching us. Can we collaborate? Can we partner? Where we make? Greatness common? You know, train people in these different areas. We certified them. We are committed and we can have an arrangement where we look at the areas that can make it easier for us to all do this. Please, we are committed. Please. can you also thank you so much people. Thank you so much.

Let's get out today. Thank you. so much. We appreciate our. We will get across to you as soon as possible and in design something fast but we're gonna design it together. Thank you so much. Please are out of our plaza, please? really take pictures. Right. Still look. Okay. so this here. All right. We're wrapping up. first, we Stretched. The time we ought to have been true before. Now, you know, And this was funny. Thank you. so much. I hope that this two days have been amazing. So, we will set for that information, but I'm sure. remote feelings still, are will still continue. And then for Saturday, we are having a business dinner for PM. Please don't miss it for anything under heaven and it's Okay. I said, I Miss you. We have a graph in early.

As found on YouTube