Traditional
Regarding my traditional education, I entered the University of Nairobi in 2012 and left in 2016 with a Bachelor of Commerce, Marketing option degree. During this time, my interest in entrepreneurship and the informal sector was sparked.
I then pursued a professional certification in marketing, CIM, in 2018, where I specialized in Digital Marketing. It was there where I saw the important role that digital platforms and the Internet play in helping businesses succeed, and I chose to further specialize in research and analytics.
I returned to the University of Nairobi in 2019, but this time studying a Master of Science in Social Statistics degree. It is here where I gained a solid statistical foundation, strengthening my research and analytics capabilities.
Non-Traditional
During my time in university, I got to travel a lot and learn more about the informal sector in 3 different countries in addition to Kenya. Something that struck me at the time was that a lot of research was not usually led by local communities, and there was little public data available for local businesses to leverage.
Out of university and back home in 2018, I first came across the term data science and saw that it involved use of a programming language called Python. Someone I met at an event also pointed me to some data science meetups in Nairobi. They were AI Kenya, Master Cohorts and Women in Machine Learning and Data Science Kenya. These forums exposed me to in-person hackathons, learning programs and open-source resources.
During late 2018, I learned Python using CodeAcademy while guided by the open-source Python learning path given by Andela at the time. Basic Python knowledge enabled me to participate in a continental program that trained aspiring data scientists in partnership with Microsoft. The Microsoft Professional Program in Data Science was a wonderful bridge that enabled me to transition from an Arts degree and enter into STEM.
2020 was a year that I got to plug into virtual communities and virtual global events, such as Data Science for Social Good Community, Data Umbrella Scikit-Learn sprint and AI for Good UK community. While in these global spaces, I applied for and got into two programs.
The first was the 2021 Delta Analytics Teaching Fellowship where I got to learn teaching techniques for technical (Data Science, Machine Learning, AI) concepts. Sometime later, I got into Bootcamp 33, a 3-month program organised by Prospect 33 that trained me to apply data science in the context of the banking industry.
In these two places, I was able to get technical mentorship around:
data science in production,
teaching mixed audiences,
presenting on a livestream,
leveraging graph databases to detect financial fraud, and
automating data annotation where the text data has code-switching.