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The University of Southampton
Southampton Statistical Sciences Research Institute

S3RI Seminar - Data Science Education at School and at University in UK – Key skills and industry expectations, Professor Berthold Lausen (University of Essex) Seminar

Origin:
Mathematical Sciences
S3RI Seminar
Time:
14:00 - 15:00
Date:
3 May 2018
Venue:
Room 8031, Lecture Theatre 8C, Building 54, Mathematical Sciences, University of Southampton, Highfield Campus, SO17 1BJ

For more information regarding this seminar, please email Dr Helen Ogden at H.E.Ogden@southampton.ac.uk .

Event details

The landscape for school mathematics in the United Kingdom is very different now compared to even just five years ago, see Lee et al (2016). The study of mathematics is compulsory in England up to the age of 16 when students complete their GCSE (General Certificate of Secondary Education) examinations. Annually over 550,000 students take GCSE Mathematics(*). Post 16 the most commonly studied qualifications are A levels (Advanced Levels), where two potential mathematics qualifications are available to students, Mathematics and Further Mathematics. Further Mathematics is an additional A level only open to students who are already studying a Mathematics A level. In 2005 a total of 52897(**) students completed A level Mathematics, but this number had grown to 95244(**) by the summer of 2017, making it the most popular A level in the UK. Similarly, the numbers for Further Mathematics have also increased over the same period, raising from 5933(**) in 2005 to 16172(**) in 2017.

Since 2015 there have been many changes in mathematics education with the compulsory 14-16 GCSE examinations being made more difficult with, for example, a greater emphasis on problem solving. Similarly, the post-compulsory 16+ A level examinations have also been reformed, however this time the examinations have not been made more difficult, rather the curriculum has become more standardised. For example all students now have to complete a statistics component as part of A level Mathematics, something that was not true under the pre-2017 structure. Another new compulsory requirement is that students must work with prescribed large data sets during their A level Mathematics studies, and that the use of technology must permeate across their course.  The new style A level has been introduced for the 2017/18 academic year so the first large cohort of students studying under the system will be arriving at Universities for the 2019/20 academic year. These students will be those who have studied both the new linear GCSE Mathematics and new A level mathematics qualifications.

During the period of recent changes in the UK school system we have seen the emergence of undergraduate (BSc) and postgraduate taught (MSc) qualifications in Data Science, for example in 2014 the Department of Mathematical Sciences and the School of Computer Science and Electronic Engineering at the University of Essex have introduced a BSc in Data Science and Analytics and an MSc in Data Science. The curriculum of these courses covers compulsory modules from computer science and mathematical sciences, introducing students to a range of mathematics and statistical topics as well as computing skills such as programming, software engineering, databases, data mining, web development, and artificial intelligence.

In this talk we will discuss the changes to the school curriculum in the UK and the impact they have on preparing people to study data science at university any beyond. We will also review the curricula of data science related university degrees, consider how their content matches industry expectations and discuss plans for further developments.

(*) Source: Government Department for Education data
(**) Source: Joint Council for Qualifications Examination Entry data

Lee, S et al (2016) Understanding the UK Mathematics Curriculum Pre-Higher Education – A Guide for Academic Members of Staff (2016 edition) SIGMA. ISBN: 978-1-911217-05 -3

Speaker information

Professor Berthold Lausen , University of Essex. Research interests: biostatistics, classification, clinical research, computational statistics, data analysis, data science, epidemiology, public health, systems biology.

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