Building the Confidence and Skills to Develop End-to-End AI Products

Ephantus Achebi from Kenya completed several Omdena challenges which helped him synthesize data science knowledge to build full-scale AI and data products, which he can showcase to employers when interviewing for jobs.

What is your background?

I am a mathematics and computer science graduate at Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya. My domain knowledge is in the insurance business, human resources, and entrepreneurship.

What is your achievement?

My previous Omdena challenge in June and August 2022 was about building an AI-powered Chatbot to offer 24/7 support services for the university website (Dedan Kimathi University of Technology, Kenya). The challenge helped me apply data science knowledge, build a data product (Chatbot), and collaborate with other talented techies.

How did the Omdena experience help in your work career?

I had invested over three years learning data science tools and approaches, but I had a limited, real practical experience. The Omdena challenge helped me synthesize the data science knowledge learned from books, online tutorials, and YouTube videos. Then put it into practice. It was a great opportunity to solve a real problem and collaborate with other data science enthusiasts. The challenge helped me improve coding skills having gotten a chance to read and review code from other collaborators. Now, I am able to confidently demonstrate my data science expertise to employers, and I am eager to apply the same in building commercial data products.

How did the Omdena challenge change your worldview? What was the biggest obstacle you overcame?

The challenge enabled me to review the world of data science as not needing a lot of resources (computation power, big data, etc.) for one to build a data product; you only need IT knowledge and tools, a laptop, and access to the internet.

My biggest obstacle was being able to demonstrate my techie skills to employers, and the Omdena experience has helped me overcome this having gained practical experience in building a data product.

Would you like to share any additional thoughts for future Omdena collaborators?

I found Omdena by constantly reading data science subjects and looking for people with similar interests. Fraternizing with data science enthusiasts makes me sink deep and deeper into the data science domain. Currently, I am engaged in another Omdena challenge that is driving me deep into computer vision, Using Image Analysis to Estimate the Density of Blood Cells.

My encouragement to future collaborators is to persistently mingle and engage fellow data scientists because help will always be within reach.