South Africa is quickly becoming a hub for data science talent, with the University of Stellenbosch leading the way. Data science is not just about algorithms and models. It’s about solving real-world problems. Few understand this better than Professor Kanshukan Rajaratnam, whose career spans both academia and industry. As the Director of the School for Data Science and Computational Thinking at Stellenbosch University, he has seen firsthand how business challenges shape research and how education prepares future data scientists.
In this conversation, he shares his insights on leadership, learning, and South Africa’s unique approach to data science.
1. Your career spans both academia and industry. How has working in business influenced the way you approach education and research?
My doctoral studies and my subsequent research topics in the immediate subsequent years were directly answering questions that I faced while working in industry. My research focus was in decision making in the context of loan portfolio and many of the research questions were from the perspective of a loan portfolio manager trying to maximize profits and other metrics (such as market share).
My experience in industry was also instrumental in generating research questions from problems faced by practitioners. Perhaps most importantly, working in industry gave me leadership skills that helps me in my work up to today. In industry, one is faced with diverse stakeholders and one must learn to interact and engage with a diverse group of people.
2. Beyond technical expertise, what’s one way you help students think differently about data science?
It is important to get one’s hands dirty in data science. There are many opportunities to solve problems such as through hackathons and through platforms like Zindi. Potential data scientists must get experience in building models and solving problems through these means.
Additionally, one should not be a data scientist in isolation but be able to work in diverse teams, particularly with domain experts. Each domain area has nuances that one doesn’t readily know of as a data scientist, but experts in the domain area have experience in identifying and dealing with such nuances.
3. You’ve led major academic and strategic initiatives. What’s one leadership lesson you’ve learned that shaped the way you work today?
I am not sure if it has shaped the way I work today but it is one that I am working on. When one is responsible for a strategic initiative, it is important not to just focus on the initiative but also on stakeholders. It is easy to create success without taking everyone on the ride. It is important to take all stakeholders on the ride in achieving strategic goals.
4. You’ve worked at top universities across the world. What’s something unique about the way South Africa approaches data science and education?
I can only really speak about Stellenbosch University. SU is an innovative university and quickly identified that data science is a diverse team sport. The university created teaching and research pathways that is truly interdisciplinary and transdisciplinary.
Take for example the BDatSc degree. It is the only university with a named degree in data science. This specific degree allows students to specialise in subjects from four different faculties. As students undertake their major, they interact with students from other faculties, and hence learning from and with diverse set of student. It develops the skills mentioned in (2), which is to work in diverse teams.
South Africa has incredible talent and the country provides world-class education at a fraction of the cost of many other globally ranked universities. Unfortunately, the cost is still out of bounds for many of those with incredible talent.
5. Many young South Africans are interested in data science but don’t know where to start. If you were beginning your journey today, what would be your first step?
There are a lot of free resources available online. I would learn basic coding and undertake an online basic data science course. I would then use this to play with data and solve problems. Of course, doing such activities should not come at the expense of school grades. Degrees such as BDatSc has high requirements. So, it is important to get those good grades if one wants to study degrees such as BDatSc.
While at university, I would test my knowledge through hackathons and through platform such as Zindi as well as engage with industry through internships, job shadowing opportunities, etc.
Both at school and at university, it is important to develop language and soft skills. Spoken and written language skills are important to convey the results of data science models and projects. Additionally; team work, presentation skills, and such soft skills are central to being a data scientist. It is important to develop these while at school and university.
As Professor Rajaratnam emphasizes, data science is not just about mastering technical skills, but it’s about applying them in real-world scenarios, collaborating across disciplines, and continuously learning. Whether through hackathons, internships, or university programs like BDatSc, aspiring data scientists must embrace both knowledge and experience.
South Africa’s data science landscape is evolving, and those who take initiative will be well-positioned to thrive in this exciting field.
A huge thanks to Professor Rajaratnam for sharing his thoughts! What stood out to you about this interview? Feel free to share your thoughts below!
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