Growth of Jobs

Apprentices in West Sussex

This goal is measured by tracking number of apprentices in West Sussex. It is our aim that there are a similar number of apprentices in the county in 2014/2015 to the number in the county last year.Explore the data
apprentices
Final
5,600apprentices
Mar 2015 Target
Goal Period ended March 2015

        Data relates to the academic financial year which starts on 1 August and ends 31 July. The statistics are sourced from

        FE data library: apprenticeships. We are unable to present an accurate figure as the reporting on progress work has a three month lag.

        Half yearly figure for August 2014 to January 2015 is 2,550 (provisional)

        Nationally there has been a drop in the number of people starting an Apprenticeship, particularly the 16-18 year old starts and those starting Advanced and Higher Apprenticeships. The start reductions are reflected in both the South East region and West Sussex. Nationally we are aware the picture is changing and the number of Apprenticeship starts is increasing again; however at this time we do not have a local picture to indicate the trends we can share.

        The County Council has embraced apprenticeships and wishes other employers to do likewise. Any businesses new to Apprenticeships can access information and advice from the Apprenticemakers website. West Sussex also offers business to business advice to any employer new to Apprenticeships.

        Apprenticeships employers, to develop the skills they need for their future workforce enhancing productivity and increasing their competitiveness.

        Apprenticeships are a key way of boosting the local economy, through providing training alongside paid work opportunities

        Apprenticeships increase the skills available locally and the employability of those in our local communities. There are many apprenticeships advertised with different employers across the county.

      Data Governance

      describes the quality of the data itself. Governance issues generally indicate that the data source is considered incomplete or unreliable.

      Model Health

      describes the quality of the predictive model. If the model health is poor, the trend prediction should not be trusted.

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