Employability Skills: Time Series Forecasting at Swansea University

Steve Cook, Swansea University
s.cook@swansea.ac.uk
Published October 2019

Introduction

Relative to the delivery of modules this type I have encountered elsewhere, the approach adopted in this optional module spends less time on formal proof/theorem issues to focus attention upon the application of methods and the evaluation and interpretation of the results they provide. This is not at the expense of detailed analysis as this appears instead within an empirical context via repeated data-based application. The emphasis on the development of critical practical skills is reflected in assessment where practical, data-based coursework exercises are supplemented by ‘response to output’ and ‘working with data’ questioning in the formal examination. Again, this assessment is very practical compared to others I have encountered elsewhere and includes both formal reporting and discussion of methods, results, motivation, and so on, in non-technical language.  

The module requires repeated application of statistical techniques to both real world data and, on occasion, artificial data generated to possess certain features to test the understanding of students. For example, a particular single topic is delivered via the use of 5 data sets which are all used for multiple exercises to illustrate the different issues discussed. The module is driven by learning-by-doing and assessment-by-doing.

Specific features of the module which (hopefully) develop employability skills include the following related items:

Developing the ability to manage time and work independently: Online materials blending lecture type material, workshop exercises and interactive data-based tools are made available for use outside of the classroom via the Economics Network Ideas Bank[1]. These materials allow the introduction of flipping to the delivery of sessions. Students are directed to these materials to both work independently to support their studies and generate questions/issues to address for class activities. Using materials to place some responsibility on students to work outside of the classroom and bring back questions, ideas etc. features in the final paragraph of this section.

Developing additional data manipulation and IT skills: Movement between software packages is a feature of the module. After considering the different options available for analysis before generating and evaluating results, students are frequently then asked to transfer elements of the inputs and outputs of this work to Excel to replicate findings. This additional step to test understanding by reproducing results, manipulating data and switching between software packages reinforces understanding and provides an additional challenge for students.

Task driven exercises requiring the achievement of specified aim/targets: To develop further the ability to apply knowledge within a practical context, students are regularly asked to work towards specified outcomes or targets. For example, after studying and applying a particular method, students are frequently provided with data and asked to work towards (replicate) published results. Hopefully this generates skills in working towards/achieving specified aims/targets.

Additional responsibility via the use of student-led sessions: Student-led hours are built into the delivery of the module. Students are asked to reflect upon their studies, consider where any uncertainties might appear in their knowledge of the topics covered and then provide suggestions for both the form and content of some sessions within the module. Students are therefore shouldered with the responsibility of evaluating their position and how elements of the delivery might be tailored to best suit their needs.

Skills developed by this activity

The main skills that this activity explicitly helps students to develop:

Communication 

 

Writing for academic audience 

X

Writing for non-academic audience 

X

Presentation to academic audience 

 

Presentation to non-academic audience 

 

Application to real world 

 

Applying economics to real world context 

X

Solving policy or commercial problems 

 

Simplifying complex ideas/information to make them accessible to wide audience 

X

Data analysis 

 

Sourcing and organising quantitative data 

X

Analysing and interpreting quantitative data 

X

Fluency with excel 

X

Fluency with statistical/econometric packages 

X

Collaboration 

 

Team-working with economists 

X

Collaboration with non-economists 

 

Wider employability skills 

 

Flexibility 

X

Creativity and imagination 

X

Independent thinking 

X

Can do attitude 

X

Reliability 

X

Resilience 

X

Commercial awareness 

 

Time management 

X

Project management/organisational skills 

X


[1] Ideas Bank, Economics Network, https://www.economicsnetwork.ac.uk/showcase

Contributor profiles: