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DataLike: Interview with Wuraola Oyewusi
Wuraola Oyewusi is a Data Scientist, Technical Instructor, and Pharmacist, a passionate professional committed to advancing artificial intelligence practice. She has held roles in AI research as a Researcher (data science and data curation) at the Imperial College London and previously led Research and Innovation at Data Science Nigeria.
Her research interest is in natural language processing and she has also been at the forefront of unstructured data application and open source access, especially in the area of health and language.
She is the author and instructor of:
- Hands-on natural language processing
- Advanced AI: NLP techniques for clinical datasets
- Hands-on data science and AI for healthcare
- Python for health sciences and healthcare
- Hands-on data annotation: Applied machine learning courses on LinkedIn learning
She contributed to the Springer AI in Medicine textbook and teaches tech in Yoruba on YouTube. You can follow her research publications and medium blog.
Can you share how you started working with data?
By chance, I was reading job descriptions, I found one about data analysis, I thought I could do many things on the list except SQL. So I decided to check out what it was all about.
What is the most challenging aspect of your day-to-day activities?
I currently work in research, so I will say active study and understanding of a lot of academic content.
And the most rewarding?
Finding interesting patterns. Having the license to be curious.
Could you share with us how you got started with the LinkedIn learning courses?
I applied using the instructor recruitment link. They are always recruiting. It might have also helped that I wrote a series of articles (I think you should write, it’s useful for your portfolio. I got my first data science job offer because someone read my article).
Can you share a project or experience that was particularly rewarding or memorable for you?
Hmmm, there are a lot of projects but since I have to share one, I will say deriving a scoring method to compare how people agree on a data labeling task from the text component of the data.
Then teaching Tech concepts including AI in Yoruba Language. I wrote all about my method here.
How does your background in pharmacy help your career?
My background is helpful for scale. The pharmacy curriculum is diverse and my confidence to experiment and document processes can be traced back to pharmacy labs. Also, ease and familiarity with clinical/healthcare-related terms in my work and research is invaluable! I can label my data, find the information I need, and also ask precise questions.
Could you share with us something you wished you could tell yourself now that you have more experience in data science?
I will say keep at it! You did right by yourself by going as deep as possible.
We thank Wuraola for her sharing her journey and story with us. You can keep up with her on Linkedin and X (Twitter).
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