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 and through this experience, I learned the importance of feedback when working with others. Also, I learned it’s oftentimes easier to get an advanced understanding of a topic about a topic when you’re teaching/guiding others through it.
I’ve been a data scientist for about 5 years. I’m a co-founder and CTO (pre-pandemic) at a number of entertainment companies. We’ve organized some of the biggest concerts in Africa. My focus on the past year and moving forward has been and is 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.
Pictures from the event
Due to a number of factors; wrong time and venue, bad organization, and because we were trying a number of things for the first time, it was a failure by any measure.
After the concert, I rarely left home. I became incredibly anxious, withdrew from life, and mostly stayed in bed. I remember spending many hours in bed. Eventually, I decided I was either going to allow that experience to conquer me, or I was going to move past it. Only those two options existed. Using all my energy, I decided that I had spent enough time moping about it. Instead, I forgave myself for the mistakes made and started to reflect on the learnings. In the end, I realized that I needed to make those mistakes to learn the lessons that will help me do better in the future. Mistakes are and will always be unavoidable but it is our decision 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
- Don´t rush and become overbusy but regularly reflect and ask yourself what am I missing or not seeing
After the incident, I developed and learned 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 and it helps me to be more careful, not only in my data science endeavors.
To this day, I remember the following quote, which might be helpful for you as well:
You only really fail if you give up!
How did the Omdena experience help you grow your skills? How can someone joining Omdena Projects make the best out of this experience?
I joined three Omdena projects, the first was disaster management in collaboration with the World Food Programme, one NLP project with Save the Children to fight online violence against children, and a Computer Vision weed detection project hosted by an impact startup.
Before diving into my motivation for joining Omdena to build real-world skills, I want to tell a brief story. Late last year, 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 minute’s drive from my home. It was a great period because, for the first time in my generation, people came out and really cared about making the country better and creating a better world than the one we were born into. It was a beautiful time, at least up 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, to do better. Omdena had two challenges coming up at the time related to food and agriculture, weed detection as well as a Senegal food insecurity Challenge. 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 because I wanted to make a difference. I think this is the most powerful realization, not only to make the world a better place but to also 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 Save The Children project. Before that, I didn’t realize it was possible to collect that much data online. Though, many people working towards a common goal makes 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 out there and the number of messages posted.
You have to realize that this is normal and that 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.
If you would start all over again in your career, what would you do differently?
I’d spend less time on learning and 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 to navigating a new area or field early on in one’s career. About five years ago while starting out in data science, at a tech conference, I met a data scientist and told him I was spending some time learning Matlab, he advised me to ditch Matlab as it wasn’t really required outside of academia. He was right. That was the last time I used it and have never had to since then.
Having someone guide you at the start is vital. The Omdena community has many experienced people, 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 is an amazing initiative allowing 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, thanks for the inspiration on turning fear into an opportunity to build a meaningful career in data science.
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