A global teaching resource for post-COVID-19 academia
During the COVID-19 pandemic, governments used data science modelling to justify actions around lockdowns, and then again, in due course, when they eased restrictions. These actions affected billions of citizens’ lives and livelihoods. The importance of analytical calculation and competence was brought home, often brutally, to households and institutions everywhere. Suddenly, it seemed that everyone knew about analytics.
At the same time, industries from retail through to insurance faced huge changes. They, too, drew heavily on analytics to predict customer demand, understand changing customer behaviour and compete effectively in an upside-down world.
The result was an unprecedented demand for analytical skills.
This has had a huge impact on the higher education sector – the main training ground for the data scientists of the future. It is not unreasonable to say that 2020 has irrevocably changed the nature and priorities of higher education. In particular, it has shone a spotlight on the teaching methods used in data science courses. Universities and employers alike have had to rethink which methods are most effective for teaching students the skills that they need to survive in the real world, where data are not always – or even mostly – clean and complete, and life is often messy.
Introducing analytical case studies
Higher education institutions generally agree that one of the most effective tools for teaching analytics is using case studies. These are analytical challenges based on real-world data. Case studies have long been used in business schools for more general learning and have a very good reputation. Case study clearinghouses have been set up to ensure the quality of these teaching resources, and there is considerable competition to get cases accepted for publication. The best cases are studied around the world by tens of thousands of business students every year.
Businesses see case studies as ways to enhance their reputation. They often commission case studies about their organisations, using them to highlight successes or to raise brand awareness with an influential demographic.
The academic library
However, until recently, data privacy and protection issues have limited the number of quantitative case studies developed for academic use. It is generally hard to get hold of real-world data that are both sufficiently useful for students and do not disclose confidential or business-sensitive information. These issues cannot be minimised or diluted, but it is past time that we stopped hiding behind regulations and recognised that data science students must have case studies if they are to learn the skills they need for work.
To address the shortfall in quantitative case studies, SAS has actively sought out new analytical cases with accompanying data to create a new academic case library for use by higher education institutions.
SAS analytical experts commission and curate new cases for the library. They can therefore ensure the library reflects a range of common analytical techniques applied across different industries. The experts also tag the cases to show their suitability for different levels of academic attainment. Where new content is developed for existing cases, it is only added to the library after careful review.
A step change for teaching
Academics write the SAS analytical case studies to ensure that they are suitable for academic use. They also follow a globally recognised format for academic case studies. All the studies are data-rich and based on real-world problems. They are also free to download and use. The studies include step-by-step demos, analytical games, and ideas for datathons and hackathons to encourage students. They also contain links to free SAS software and training materials.
The library groups case studies by industry and analytical topic to make it easier to navigate. The topics are wide-ranging across both sectors and analytical topics. There are studies from retail (for example, Insight Toys Ltd and Utkonos Retail), government (traffic accidents in the Netherlands), travel and tourism, and manufacturing, to name just a few. The analytical topics range from text mining – for example, from hotel reviews – to customer satisfaction prediction in retail and use of analytical tools like SAS Viya.
I believe that these case studies will be a step change in the teaching of analytics. But please don’t take my word for it. Why not go and have a look for yourself?
To access the SAS Case Library, click here: http://www.sas.com/case-library