Can you describe your journey into AI/ data science?
I overheard my brother-in-law talking about image classification with someone over the phone, and I thought it sounded cool! 🙂
Later, I became one of Udacity’s first code reviewers and mentors. Through this experience, I learned the importance of feedback when working with others. Also, I realized it’s often easier to get an advanced understanding of a topic when you’re teaching or guiding others through it.
I’ve been a data scientist for about five years. I’m a co-founder and CTO (pre-pandemic) at several entertainment companies. We’ve organized some of the biggest concerts in Africa. My focus in the past year has been on artificial intelligence, particularly on deep learning.
Can you share a point in your career where things got a bit difficult? What role does fear play in your data science journey?
Sometime in 2019, we organized an event called Loud Beach Festival, which did not go too well.
Due to many factors: wrong time and venue, bad organization, and because we were trying various things for the first time, it was a failure by any measure. Later, I rarely left home as I became incredibly anxious, withdrew from life, and mostly stayed in bed. I remember spending many hours in bed. The experience was either going to conquer me, or I was going to pass it. Only those two options existed. Using all my energy, I decided I had spent enough time moping about it. Instead, I forgave myself for my mistakes and started reflecting on my learnings. In the end, I realized that I needed to make those mistakes to learn the lessons that will help me improve. Mistakes are unavoidable and will always be. But our decision is what we take away from them.
“One of the basic rules of the universe is that nothing is perfect. Perfection simply doesn’t exist…Without imperfection, neither you nor I would exist” ― Stephen Hawking
Lessons learned after my biggest “failure”:
- Mistakes are guides, not failures, which teach me to do better next time
- Do not try to do everything yourself, ask for help, and trust in others
- Do not rush and become overbusy but regularly reflect and ask yourself: what am I missing or not seeing?
After the incident, I found out to identify a feeling of dread that’d come up whenever failure was on the horizon. You can call it intuition paired with a slight feeling of nervousness. I learned to use the fear to my advantage, which helps me be more careful in my data science endeavors.
To this day, I remember the following quote, which might also be helpful for you: You only really fail if you give up!
What projects did you participate with Omdena? How did the Omdena experience help you develop your skills?
I joined three Omdena projects.
Omdena team of 34 AI experts and data scientists collaborated with the World Food Programme Innovation Accelerator to build solutions to predict affected populations and create customized relief packages for disaster response.
Before diving into my motivation for joining Omdena to build real-world skills, I want to tell a brief story.
In 2020, Nigeria experienced a wave of protests against police brutality (EndSARS) and bad governance/corruption.
The closest protest spot to where I live was a toll gate, a few minutes’ drive from my home. It was a significant period. For the first time in my generation, people came out and really cared about making the country and the world better than the one we were born in. It was a beautiful time, at least until the moment the government sent soldiers to shoot at the unarmed protestors (I wasn’t there on the day as a curfew had been called earlier that day).
During this period, Nigeria was also going through a food security crisis. The protests at the time had inspired everyone to do more and better. Omdena had two challenges coming up at the time related to food and agriculture: Detecting Weed to Reduce Costs & Environmental Footprint and Improving Food Security and Crop Yield in Senegal.
I joined both, one as an active participant and the other as an observer.
Skills I acquired in the projects:
Making an impact
In my case, I joined these projects not only to improve my skills but also I wanted to make a difference. I think this is the most powerful realization, not only to make the world a better place but also to learn way faster because you are pulled by a deep purpose in your work.
Learning about data collection and preparation
We gathered a large amount of data for the project Internet Safety for Children: Using NLP to Predict the Risk Level of Online Games, Websites, and Applications.
Before that, I didn’t realize collecting that much data online was possible. Though, many people working towards a common goal make anything possible.
Focusing on a few key project tasks
In Omdena AI challenges, you can be overwhelmed because those are real projects. Even though I have some years of experience, at the start of every challenge, I’d also feel overwhelmed with the amount of information and the number of messages posted.
You have to realize that this is normal and there are ways to deal with it.
Here are my tips:
- Tune out the noise, focus on 1-2 things and go with this, and it will grow into something valuable for the project.
- Wait 1-2 weeks, and the initial “creative chaos” will turn into more focused action. This is the nature of bottom-up collaboration and solving tough problems.
Build your portfolio with real-world projects from Omdena
What would you do differently if you would start all over again in your career?
I’d spend less time on learning theory, and get my hands dirty rather quickly with hands-on projects.
Also, I would seek out mentorship/sponsorship. Having a person or persons to reach out to and ask questions is an invaluable resource for navigating a new area or field early on in one’s career. About five years ago, while starting in data science, at a tech conference, I met a data scientist and told him that I was spending some time learning Matlab. He advised me to ditch Matlab as it wasn’t really required outside of academia, and he was right. That was the last time I used it, and since then, I have never had.
Having someone guide you at the start is vital. The Omdena community has many experienced collaborators, who will be happy to help you along your journey and build your career path. Once you have completed a project, you will join the community and Slack channel, where you get access to all opportunities.
Any closing words?
I’d like to use this opportunity to thank Omdena. This amazing initiative allows people from diverse backgrounds to collaborate and learn. AI is quite possibly the most consequential innovation of our time, and its potential to address many of society’s problems is limitless.
Thanks, Sijuade, for the inspiration on turning fear into an opportunity to build a meaningful career in data science.