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IPS215 David Y. et al.
2. Methodology
The SQA Higher Statistics Award [8] was the result of meetings with Deans
of Science and Engineering in Scotland which highlighted a lack of data
analysis and problem solving skills in higher education. Further consultations
were done with several Higher Education institutions to ensure the course
content and learning outcomes would be appropriate and relevant and a
working group was tasked to generate ideas.
Meetings with various stakeholders i.e. schools, colleges, universities and
employers, helped to identify the key statistical skills to be included in the
course curriculum. It was clear that the course should be light on mathematical
theory with the emphasis being on the application of data science skills. In
addition, the course should be available to pupils of all ability levels across the
school curriculum.
Course content was agreed and resources and materials designed. These
included support notes, course specification and assessments, teaching and
learning resources, access to real life data and computer software training and
support. Once developed, the award was promoted at CFE implementation
events and CPD training for teaching staff was offered at the Universities of
Strathclyde and Edinburgh. These events highlighted a need for additional
resources, training and support which lead to further development and
revision of the course.

3. Results
The overall aim of the Higher Statistics Award is to develop knowledge,
skills and understanding in statistical methods and techniques applied to a
variety of real-life contexts from across the curriculum, some of which may be
new to the learner. Candidates who complete this qualification will be able to:
 use and apply statistical skills in real-life contexts
 identify and perform an appropriate statistical analysis on given data
set(s) using a statistical software package
 communicate the results of a statistical analysis, clearly and concisely,
in the context of the problem being addressed

3.1 Course content
Content was devised in collaboration with industry and education to
ensure fundamental data science skills covering a wide range of applications
would be covered within the course. There are three main topics: an
introduction to statistics, correlation and regression and hypothesis testing.
1. Introduction to Statistics: includes types of data; summarising data
graphically and numerically using appropriate descriptive statistics;
sampling and data distributions (specifically the normal distribution)

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