Olayinka Fadahunsi: Open Data Opportunities and Challenges in Africa
Olayinka Fadahunsi: Open Data Opportunities and Challenges in Africa

A conversation about using data science skills for social good and finding creative solutions to Africa’s most pressing problems.

It is our pleasure to introduce Olayinka Fadahunsi. Mr. Fadahunsi is a Data Scientist with Stanbic IBTC Bank in Lagos, Nigeria and focuses on predictive customer models in personal and business banking. A graduate from the University of Lagos with a degree in Electrical and Electronics Engineering, he also moonlights as a Data Scientist on Zindi, Africa’s first data science competition platform that is focused on solving the continent’s most pressing problems.

“Success with domain knowledge will facilitate the creation of useful features resulting in better models.”

Yinka Fadahunsi, Data Scientist.

As one of the top data scientists on Zindi, Mr. Fadahunsi is enthusiastic to use his data science skills to solve real-world challenges, especially those involving Africa. To date, he has competed in 80% of the Zindi data science competitions, winning multiple prizes for his innovative solutions. The ones we found most impressive were the models he built for the Farm Pin Crop Detection Challenge and the Wazihub Soil Moisture Prediction Challenge — both of which he ranked №1. His success is impressive as Mr. Fadahunsi’s knowledge in machine learning is largely self-taught.

The Farm Pin Crop Detection Challenge model monitors agricultural activities along the Orange River in South Africa, using the European Space Agency’s Sentinel-2 satellite imagery. This model helps to lower service costs for Farm Pin. For the Wazihub Soil Moisture Prediction Challenge, he built a machine learning (ML) model using incomplete and unclean data to predict soil humidity. This model helps farmers anticipate and schedule their water needs.

In this Q&A, Mr. Fadahunsi talks to us about his data science journey and using his skills for social good in Africa.

Congratulations on being ranked as one of the top data scientists on Zindi! Tell us about your data science journey. Your background is in electronics and electrical engineering. How did you become interested in data science? What resources did you use to learn data science skills?

My interest in data science came from an article written on Harvard Business Review in 2012, which highlighted data science as the “sexiest job of the 21st century.” At the time, I knew I enjoyed analysis and research, so a career in data science seemed fitting. Upon graduation from the University of Lagos, Nigeria, I was selected to join the graduate trainee program at Stanbic IBTC Bank. My experience in the 18-months graduate program cemented my passion for analysis and research. A few years down the line, the organization expanded data skills among its staff, which resulted in me attending a 6-months program in India and South Africa.

The first 3-months was an intense bootcamp at Tech Mahindra in Hyderabad, India. I was fortunate to have skilled facilitators such as professors and alumni of Harvard University who exposed me to concepts and skills like Hadoop, programming in java/R/Python, QlikView for storytelling, machine learning, among others’ data science skills. The latter 3-months were spent in South Africa with industry experts, which helped translate the theory to practical knowledge. This rounded program gave me the confidence and familiarity in tackling real-world problems both in the office and on competition sites like Zindi.

Getting involved in providing solutions to real-world problems has been the largest contributor to the development of my skills. I have learned the most by solving problems at work and on Zindi. I am self-driven, so I often find myself researching solutions to a problem and taking short courses online to bridge a skill gap. I recommend taking short courses on datacamp.com. Besides, open-source tools make it easy to find support online when stuck on a problem. For instance, I visit sites like stackoverflow.com daily to glean knowledge from experienced developers.

Your participation rate on Zindi is 80%. What is the appeal of these data challenge competitions for you? What do you think is the value of open data competitions?

2019 was the first time I put my goals on paper. One of the items on my checklist was to be the best data scientist in Africa by the end of 2019. Zindi offered a measure to my goal, as it provided rankings to data scientists based on competitions won. This was the foundation of my high participation rate. In addition, I enjoy writing code and solving data science problems, so I was naturally drawn to Zindi because it provided lots of data sets and problems. The next stop on my global quest is to be amongst the top 5 data scientists in the world.

Open competitions present value both to the data scientists and organizations hosting the problems. On the one hand, data scientists get to improve their skills and earn rewards in the form of cash and bragging rights. On the other hand, organizations obtain the best solutions via crowdsourcing.

You won first prize in the Farm Pin Crop Detection challenge on Zindi. Walk us through the process of competing in this challenge. How did using ML for Earth observation data compare to ML applications in other data domains?

The Farm Pin Crop Detection challenge was a unique, complex and exciting competition. It involved the analysis of satellite images, which was an unknown territory at the time for me. The key success factor in the competition was the understanding of the domain of satellite imagery for crop classification. The knowledge I gathered from research papers and other online sources resulted in my ability to create a plethora of features that helped in the development of a great model. Features around vegetation indices, perennial versus annual crops, harvesting and planting season features, etc. were created.

Sentinel satellite images have small pixel coverage, as such, traditional computer vision algorithms wouldn’t produce excellent results. The solution was to convert images to structured data represented by an aggregated list of pixel values. At this point, traditional ML algorithms utilized on structured data can be used. For example, Xgboost, Catboost and Light GBM.

How do you think data science, particularly ML and AI, fits into global development work?

Data is produced in every activity. ML and AI will continue to be relevant for many years. The limitation in today’s big data analytics climate, however, is the skill set required in gathering and storing large amounts of both structured and unstructured data. Once countries have matured to this capability, the world will be truly data-driven.

African tech hubs play an important part in innovating businesses on the continent with no signs of it slowing down. What are some of the opportunities and challenges facing data scientists in Africa?

There are opportunities for African data scientists to apply knowledge in areas of agriculture, financial inclusion, health diagnosis with computer vision, among other mission areas. With the advent of the internet, data scientists in Africa are only limited by their imagination. One current challenge is around access to low-cost infrastructure to perform deep learning tasks, especially when it involves large amounts of data.

Do you have any advice for your peers to engage in these data challenge competitions and work on problems that would address challenges in the continent?

First, both new entrants and experienced data scientists need to gain base knowledge in statistics, mathematics, programming in R/Python, ML, etc. Upon completion of this foundational knowledge, they should enroll in competitions and test their skills. Time should be invested in understanding the domain. Success with domain knowledge will facilitate the creation of useful features resulting in better models.
['Radiant Earth Foundation']
['Data Scientist Skills', 'Data Scientist', 'Social Good', 'Earth Observation', 'Africa']