While trying to break into tech, there’s the usual script: a computer science degree, maybe a bootcamp, a portfolio of GitHub projects. But Wuraola never read that script. She was a practicing pharmacist, happily employed, when she stumbled on a data analyst job description. She didn’t know what SQL was, but she looked it up. Then she found Python. Then data science. And suddenly, she wasn’t just reading job listings, she was rewriting her own career story.

What followed was an intense, self-taught journey, one she treated like school. She studied obsessively, leaned on the generosity of the internet, and carved out her niche in natural language processing (NLP). Today, she’s a PhD researcher working on NLP for cancer care, a LinkedIn Learning instructor with over 190,000 students, and the creator of an unexpected hit: a Yoruba-language video series on tech and AI, watched in over 25 countries.

It didn’t start as a grand plan. It just happened. This is Wura’s story.

We caught up with Wura to talk about how it all started.

It actually happened by chance. I was working as a pharmacist. I wasn’t unhappy or looking to escape my job. I was just curious. I used to read job descriptions for fun, just to see what else was out there. One day, I came across a role for a data analyst, and it stopped me. As I read through the requirements, I thought, “I already do most of this.”

Then I saw something called SQL. I had never heard of it before, so I Googled it. When I understood what it was, a structured way to retrieve data, it immediately made sense to me. I took a free course online, and from there, I just kept going. I found Python. Then machine learning. Then data science. And I realized, this wasn’t just a mere tool, this was an entire ecosystem I wanted to be part of.

I didn’t consider myself a “tech person” in the traditional sense. I wasn’t into design or visuals. But I loved the idea of structure, systems, and especially language. While computer vision was the hot stuff at the time, I found myself drawn to text. To meaning. To the way words work. I didn’t predict that Natural Language Processing (NLP) would explode. I just found it genuinely interesting. So I went all in. I created a schedule, studied every day, and treated it like school. I wasn't just watching tutorials. I was reading textbooks, trying to understand context. And because I’m good at finding resources online, I made the most of everything I could find. It wasn’t about chasing hype or going viral. I just wanted to master something new, and I knew that meant showing up every single day.

When I was starting out, I wasn’t in Lagos or part of any tech community. I was in Kano, coding every day. No meetups, no events. I didn’t even realize it was the “early days” of the ecosystem in Nigeria. I was just trying to figure out something cool.

The internet became my community. I was always online, reading, taking courses, connecting with others on twitter. That’s how I found people into the same things. Eventually, I started writing about applying machine learning to healthcare, which made sense because of my pharmacy background. One day, someone reached saying, “We’re building exactly what you just wrote about” and offered me a job. I didn’t take it, but it changed everything. It was tangible evidence I had acquired a valuable skillset.

I studied a lot. I had a system and treated it like school. People think you need some grand reason to switch careers, but sometimes you just want better pay or more options, and that’s okay. Whatever your reason, you have to commit. This kind of change doesn’t come from doing two hours a week. It takes real time. I usually say: if you’re giving something ten hours a day for six months, it’s almost impossible not to become good at it.

Honestly, it’s mostly just because I can. I speak Yoruba a lot. I mean, a lot. I do AI, I understand Yoruba well, and I thought: why not just talk about one using the other? So, I made a video and people decided to watch. It’s easier to say something like, “Oh, there was a big plan and strategy,” because that sounds touching. But honestly, it was just something I did, and people chose to be part of it.

I made a very short video, put it on LinkedIn and Twitter, not even Facebook, and suddenly, people were watching. And then it became a thing. I think I ended up making about 47 videos in that series. It was not like I sat down and planned to make 40. I was just randomly goofing around and giving it a shot. So yeah, that’s how it started.

The people watching were really diverse. Some wanted to understand Yoruba better, some were trying to get the tech, and some just found it entertaining. And they were watching from all over. When I shared it on LinkedIn, a few people said it didn’t belong there, that it was “Yoruba stuff” and should’ve been on Facebook. I wasn't surprised. I understand why they would assume it didn't belong there. It felt like some people assumed the audience I was speaking to only existed on Facebook. But I happen to be on LinkedIn and Twitter, that’s where I shared it, and that’s where it lived. I never even put it on Facebook.

One thing about putting stuff out there is that you have to be ready for people to ignore it completely. But also, be ready for it to take off, and that’s what happened. Since I document a lot, I ended up writing a paper about it, what I did, how people responded, and who the audience was. The content reached viewers in over 25 countries on YouTube.

Because of how I process things, I’m usually prepared for both decisions and consequences, so most things don’t catch me off guard. But one of the most interesting, not necessarily challenging, aspects of the whole project was figuring out how to add subtitles. Initially, I hadn’t thought much of it. But then I realised, to properly subtitle a five-minute video, it could take up to 40 minutes. That was a learning curve. Luckily, because I speak Yoruba fluently and I’m a native speaker, it was easier for me to handle. I also had to come up with entirely new words to describe certain technical concepts. And because I understand the workings of AI systems and concepts, I am to do that  well qualified type of work.

One example was when I coined a term for artificial intelligence, Ọgbọ́n Àpinlẹ̀rọ. I remember getting emails from people who didn’t agree with the term. Some felt like it made AI sound less sophisticated, like I was simplifying something that was supposed to sound futuristic. But that was exactly the point. AI is not refined wisdom, it’s simulated knowledge. And I wanted the word to reflect that clearly.

There’s always a balance to strike between being technically accurate and being accessible. Some Yoruba words might technically describe AI better, but if they’re clunky, they won’t stick.

And all of this links to a bigger issue. Language preservation. If our languages don’t evolve to describe new realities, they’ll die. If Yoruba doesn’t have ways to explain emerging technologies, or if it’s not spoken online, it’ll fade. It’s that simple. And that was part of the quiet mission behind the project: keep the language alive by using it in new, modern contexts.

Interestingly, when the Yoruba AI content started, it wasn’t from a conscious goal to preserve the language. That was a relevant second order effect. Digitization in any form is relevant to language use and preservation.

It’s also worth saying: this isn’t my day job. I work full-time in research, so this project has always been a side effort.. I’m not trying to make a career in content or traditional media. It’s one of those things I did because I could, and it resonated. But that also means I can’t run it like a media business. I don’t have the capacity to do that.

Still, I’ve found it powerful that something so simple could reach so many people and raise these kinds of questions. If Yoruba, or any indigenous language, is going to survive, it has to evolve with the times.

LinkedIn Learning has been a blessing. I remember tweeting back in 2018, “I want to teach this stuff,” even though I had no idea how it would happen.

I wanted to break it down, make it easier to grasp. So, I applied when LinkedIn Learning had an open call. I didn’t have any insider access or anything.

The experience has been amazing. I’ve had support from a great team, and the feedback from learners is inspiring. People don’t owe you their time, so when they show up, learn, and ask for more, that means everything. I’m really incredibly grateful and glad I took the leap.

I’ve had a lot of proud moments but honestly, it’s been a mix. I’ve had some highs, and some lows. One of the highlights for me is getting to teach the series on LinkedIn Learning. I always knew I wanted to teach, even before I knew how it would happen. And now, getting to create content that people actually show up for, especially on topics like NLP? That means a lot.

Thinking about where I started, just learning online, figuring things out on my own, it feels full circle. I didn’t imagine AI would be such a big part of my story, but here we are.

So yeah, just being here, doing work that feels relevant, and contributing to it in my own way, that’s something I’m incredibly proud of. And if I can say one last thing, it’s this: if there’s something you want to try, just do it. Don’t wait to become a master before starting. Don’t go halfway. Life is global now. The internet is changing everything. There’s space in this world for people who show up and go all in.

 I’ve had a lot of proud moments from creating my courses in Data, AI and Healthcare analytics to finding out I was betting on the right things when I transitioned careers, being part of interesting research and definitely now my work in Tech and AI in Yoruba. I do think mastery of one's craft is a big deal and the internet is a game changer. There’s space in this world for people who show up and go all in.

You can check out Wura's website here. wuraolaoyewusi.com