Nick Fisher, University of Sydney & ValueMetrics Australia, is coordinator of IDSSP, an international cooperative venture to create a framework for teaching Data Science in high schools, and to teach their teachers how to deliver the curriculum. He explains:
What is Data Science? Twenty years ago, the term ‘Data Science’ hardly existed at all, although there is strong evidence that the subject itself has been in existence for over half a century (cf. David Donoho’s excellent article “50 Years of Data Science” in the Journal of Computational and Graphical Statistics, 26:4, 745-766).
These days, it is impossible to avoid. If there was ever a STEM bandwagon, this is it. University departments and schools are being re-badged, all manner of Data Science courses for students at almost any stage of learning have sprung up, a myriad journals and websites have sprouted, and jobs advertising Data Science positions vastly outnumber the amount of suitably qualified people entering the work-force. In the US alone, there is evidence of a shortfall in the hundreds of thousands.
Yet there does not appear to be widespread common understanding of what the term actually means, let alone what might be taught in a course purporting to be a general introduction to the subject.
So what is Data Science, when should we learn it, and who’s going to teach it?
At its simplest, Data Science is a life skill — the science of learning from data — and something that every child needs to know about to help them cope with the vagaries of life.
So, there is a pressing need to equip school students with this skill. And of course, that means there is a similarly pressing need to teach teachers how to teach it.
The International Data Science in Schools Project (IDSSP: www.idssp.org) was set up early in 2018 to tackle this issue.
IDSSP is an international collaborative activity involving leading computer scientists, statistical scientists, curriculum experts and teachers from Australia, Canada, England, Germany, Holland, New Zealand and the USA and supported by national and international professional societies, groups and companies.
The project has two objectives.
Firstly, to ensure that school students acquire a sufficient understanding and appreciation of how data can be acquired and used to make decisions so that they can make informed judgments in their daily lives, as students and then as adults. In particular, we envisage future generations of lawyers, journalists, historians, and many others, leaving school with a basic understanding of how to work with data to make decisions in the presence of uncertainty, and how to interpret quantitative information presented to them in the course of their professional and personal activities.
Secondly, it aims to instil in more scientifically able school students sufficient interest and enthusiasm for Data Science that they will seek to pursue tertiary studies with a view to making a career in the area.
In both cases, we want to teach people how to learn from data.
Our goal is to provide the content for a pre-calculus course in Data Science that is fun to learn and fun to teach. A total of some 240 hours of instruction is envisaged. As a parallel development we aim to devise a program will enable teachers from a wide variety of backgrounds — either mathematics teachers or from any other discipline that involves data — to learn to present such a course well.
Phase 1 of work has now been completed and made freely available. This was an entirely voluntary effort. Two curriculum frameworks (see http://www.idssp.org/pages/framework.html) have been created to support development of a pre-calculus course in Data Science that is rigorous, engaging and accessible to all students, and a joy to teach.
It is envisaged the material will be used not just in schools, but also as a valuable source of information for Data Science courses in community colleges and universities and for private study.
Now we are pursuing Phase 2:
• to develop the resources to support courses based on the curriculum frameworks; and
• to devise and implement a course aimed at prospective teachers of Data Science.
The deliverables would include:
• Excellent course materials and resources to support delivery of pre-calculus Introductory Data Science courses in a variety of modes, so that it would be fun to teach Data Science and fun to learn it.
• A course and assessment process to teach teachers from a variety of backgrounds how to teach Data Science well.
As with Phase 1, all materials will be made publicly available.
It is intended that Phase 2 be carried out professionally: contributors will be recompensed for their time, professional production of high quality materials, and with contracted project management.