“Modern-day Gold Rush” for Data Scientists Leads to Innovation in Education

Recent research indicates that demand for data scientists is booming and is only likely to increase in the next decade as companies move towards digital transformation, and big data, machine learning and data analytics become entrenched in businesses globally.

An article published in TechTarget in 2019 found that data scientists entering the job market are more in demand than ever, while Algorithmia, in their 2020 State of Enterprise Machine Learning report, likened the need for data science to a “modern-day gold rush”.

Now, SAS is partnering with local universities to strengthen the output of graduates with the requisite training to fill this skills gap. SAS’s goal with its Viya for Learners (VFL) programme is to enable students to graduate with a level of practical SAS knowledge that makes them employer-ready after graduation.

For the first time, second-year students at the University of KwaZulu Natal’s School of Mathematics, Statistics and Computer Science are engaged in a brand-new Data Science course teaching them the skills of data mining using machine learning. The students are gaining hands-on experience in data mining using the SAS VFL programme, which is another first for South African University programmes.


The course focuses on different types of data classification, clustering and association analysis, showing the students how to interpret the output and compare models, based on the output that SAS VFL provides.

This introductory course is just the beginning, as the School of Mathematics, Statistics and Computer Science at UKZN is in the process of setting up a SAS Centre for Data Science for Business, which will offer training and qualifications in Data Science, from undergraduate level through to postgraduate level.

For Professor Delia North, Dean of the School of Mathematics, Statistics and Computer Science at UKZN, the partnership with SAS and the Viya for Learners platform offers an incredible opportunity for the University to produce world-class graduates who are likely to be snapped up by employers.

“If we don’t work with the most modern programmes, our graduates will not be work-ready. The world of work is changing at an incredible speed, and through our relationships with industry, such as our partnership with SAS, we’re confident that our graduates will be sought-after,” says Professor North.

Allowing a hands-on approach has paid dividends, says UKZN course lecturer Danielle Roberts.

“Having access to Viya for Learners really gives the students great insight into new methods and advances in the field of data science. With the hands-on experience, our students are able to reason and interpret what they’re seeing instead of just repeating class notes. Data science is a job of the future and Viya for Learners is the best interface to work with to help our students develop those skills,” she comments.

Danielle’s colleague, Nombuso Zondo, is working with the faculty’s post-graduate students. VFL has enabled her students to engage with different machine learning algorithms and understanding neutral networks through a hands-on approach.

“Our post-graduate students are excited about the outputs they are seeing. They have been really impressed with the speed at which the programme produces results for even complicated models using large data sets. We’ve experienced a very high level of engagement from our students in the programme,” comments Nombuso.

As the Dean of the faculty, Professor North’s challenge is how to keep up with the demand for work-ready students on a tight academic budget. “With the results we are able to produce, we hope that industry will meet us halfway and provide funding for our graduate programmes,” she comments.

“The support we get from SAS for the SAS Viya for Learners programme is appreciable, and we appreciate how they work with us. We know that data science is the future, and using SAS Viya for Learners, we have full confidence that we can achieve many things,” says Professor Temesgen Zewotir, research professor in Applied Statistics and Data Science.

Edited by Jenna Delport

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