The importance of data science burst into the public consciousness in 2020, as the world battled to come to grips with the scale of the pandemic. Suddenly, everyone was an amateur data scientist with theories and charts to back it up. But trained African data scientists have been hard to find.
Data science is a broad term that overlaps with different disciplines but broadly refers to the act of extracting value and actionable information from data, using a mix of traditional statistics and newer programming methodologies.
The term has been in use for several decades but has gained currency as enterprises have become awash in a sea of data, funneled into databases by a wide variety of new customer-facing applications, particularly mobile apps. Data science, though, can be applied to small data sets as well as the “big data” generated by mass-scale applications.
One of the newest data science programmes in sub-Saharan Africa is the School for Data Science and Computational Thinking at Stellenbosch University (SU) in South Africa, opened in July 2019 with the goal of “blazing new trails in what is still largely uncharted territory,” according to Wim Delva, who was acting director at the school at the time. The programme highlights a multidisciplinary approach embracing subjects including mathematics, computer science, mathematical statistics and AI.
Due to the popularity of the programme, SU is launching a new undergraduate degree course in data science with a true multi‐disciplinary nature in 2021.This programme is presented in four faculties: Economic and Management Sciences, Science, AgriSciences and Arts and Social Sciences.
There are eight focus areas in SU’s new BDatSci programme:
- Analytics and Optimisation (Economic and Management Sciences),
- Behavioural Economics (Economic and Management Sciences),
- Statistical Learning (Economic and Management Sciences),
- Applied Mathematics (Science),
- Computer Science (Science),
- Statistical Physics (Science),
- Statistical Genetics (AgriSciences), and
- Geoinformatics (Arts and Social Sciences).
The commitment of the university to this new program, and the enthusiasm with which it has been adopted, are a sure sign that data science in Africa is set to thrive in the coming years.
The need for robust data science education in Africa was underscored by South African Minister of Higher Education Blade Nzimande during his address at the official opening of the Stellenbosch programme when he remarked that, “Data science as an academic discipline was pushed by the need for teams of people to analyze the big data that corporations and governments are collecting. The task for both government and universities is to prepare the youth, and adults, for the skills of the future.”
New programmes arise throughout Africa
It’s not just South Africa where data science is playing a more prominent role in higher education. For example, the African Center of Excellence in Data Science in Rwanda at the University of Rwanda and the AI & Data Science Research Group at Makerere University in Uganda are offering specialised programmes at both the undergraduate and graduate level.
The Rwandan programme offers PhD and Masters programmes with the aim of providing a research hub, stimulating collaboration between academia, government and the private sector. In November of 2020, the centre became the first African institute to receive accreditation by the Data Science Council of America (DASCA) under the World Data Science Initiative. This prestigious move means that ACE-DS can begin to leverage WDSI member institutions for access to state-of-the-art technology, systems, content and curricula that can only improve the work they are already doing.
The Makerere program, on the other hand, is small, with space for 15 researchers, but its output and focus on developing-world issues is producing some interesting research, including computational prediction of famine and mobile monitoring of crop disease.
“Our focus has been around building computational/AI methods and tools to improve efficiency or to compensate for a lack of resources in health, agriculture, transportation and so on,” said Engineer Bainomugisha, the chair of the programme and associate professor of computer science at Makerere University. “For example, we are using artificial Intelligence and data science for human and plant disease detection, air quality monitoring and analysis and traffic analysis. Since 2009 we have been running a special track on AI and data science in our MSc programme which has helped build local capacity in AI and DS.”
Global players see Africa’s potential
There has been a steady stream of attention from global technology giants looking to lay down roots in Africa, and it’s heralding a new era of possibility for young African data scientists. In Lagos, Microsoft launched the first Africa Development Centres (ADC) to “serve as a premier centre of engineering for Microsoft, where world-class African talent can create solutions for local and global impact.”
In 2020, the global giant proved its further commitment by setting up the Microsoft Africa Research Institute (MARI), which is co-located in the ADC. Mari’s “mission is to understand how innovative technologies, like cloud and AI, are helping to solve local challenges, and how we can then use this understanding to influence product creation and unearth opportunities.”
This follows Google’s early commitment to AI with the centre it built in Accra and Facebook’s brand new office in Lagos, which is the first of its African centres with a permanent team of engineers addressing African issues.
Unfortunately, there have been some casualties along the way. Last year we reported on the Decision Science Accelerator, a subsidiary of Blue Label Telecoms that was developing a hybrid model that combined a corporate outreach programme with an accelerator lab that allows students or recent graduates to develop projects. In the wake of the pandemic, that accelerator has been shuttered.
But Wim Delva, who was spearheading the program, has launched a new venture called Wimmy that “develops and delivers analytical solutions to improve the health and wellbeing of communities.” He has put together a team of data scientists and health professionals, with backgrounds in statistics, epidemiology, public health, physics, mathematics and computer science.
Building pipelines to the corporate world
Meanwhile, there are an increasing number of alternatives to traditional four-year programmes. Cape Town’s Explore Data Science Academy is offering a one-year, full-time undergraduate program. The Explore program is built around high-intensity, project-based sprints with a strong emphasis on teamwork, communication and collaboration. The company wants to stay involved with education and is launching a ‘Data science in health’ competition for recently graduated and soon to be graduating students, with the prize will be a paid internship at Wimmy.
Explore has found a way to work with corporations to build a pipeline of talent by identifying gaps in a company’s data science capabilities, and then tailoring a skills programme to fill that gap and create an employment pipeline for that company.
For example, in a case study on its website, the Explore academy illustrates how it was given the task to build a data science programme for a large telecommunications organisation that needed to bring digital skills into its business. The academy created a programme and recruited 100 students for it.
The Explore academy includes a strong online component, particularly for students who can not study on campus. In general, data science lends itself particularly well to being taught through online learning, and data science programmes in Africa typically make extensive use of online resources. Thriving in a virtual classroom environment requires the same creative, solution-oriented mindset that characterises the best data scientists.
Rwanda seems to be the country where the most innovative data thinking is coming from. The African Institute for Mathematical Sciences (AIMS) is building a “trans-disciplinary science, technology, engineering, mathematics (STEM) workforce,” and trying to “leverage implementable localized science policy to deliver on the continent’s science, technology, innovation and agenda.” Recently, they partnered with Ishango, who run a fully funded data science fellowship program, to announce a partnership that will connect top African data scientists with international work experience opportunities. For three months, fellows will be fully funded to work remotely with top Afican data scientists on international projects.
Online courses can be cost-effective
Throughout Africa, there are a wide range of online courses that offer data science training at very little cost. For example, the University of Cape Town offers an eight-week online program with modules that include “Data Science with Python,” “Neural Networks” and “Hierarchical Clustering.” Kenya’s Ulearn Systems offers online data science courses on topics such as “Statistical Inference,” “Agile Project Management,” “Data Extraction.”
Many of the best programs on the continent, such as GetSmarter’s “Data Science with Python Online Course” offer a hybrid model of online courses, mixed with project-based group work and well-mentored internships that add depth and value to the experience.
DataScienceAfrica.org, meanwhile, aims to be an online hub for data science studies on the continent, offering resources, upcoming events and a directory of important players in the field. The site is well-organized but still feels a bit thin.
Some African issues are unique to continent
Data science is a global discipline and it would be a mistake to think that only Africans can solve African problems. Some problems are unique to Africa, however.
“I think the African data science challenge is more challenging for a number of reasons; the supply of people you can work with is thinner, the data itself is lower in quality and consistency, more erratic,” Delva said. “You need to be a deeper problem solver, in order to succeed.”
South Africa, for example, poses an unusual challenge due to the massive gap between rich and poor in that country. So even though people may be living in close proximity to each other, their lived experiences are often worlds apart. In other words, it would be difficult to create a successful application for a squatter camp without data that is specific to people in that camp.
Ultimately, data science needs to be thought about as an important component of a much broader chain. For real, lasting success, Delva said, big questions need to be asked of any application: Is it scalable when you start talking about billions of daily observations continent-wide? How will it produce revenue over the long term? Does it truly address the root cause of the problem for customers?
These questions explain why it’s so important for data science programmes on the continent to take a holistic view of the field and deliver a broad, inclusive curricula that provide a common language across all aspects of data science.
There is “a need for programs that help people to broaden their language and their skill set,” Delva said. “You don’t need to know everything but you need to understand how a UX person or a data engineer thinks and speaks and vice versa. If we understand what other people’s domains entail then we can build something that makes sense for all of us.”
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