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AQMEN is an established provider of training, capacity building and knowledge exchange activities in the area of statistical methods and data analysis. AQMEN is based in the School of Social and Political Science at the University of Edinburgh. Established in 2009, AQMEN has delivered over 150 training courses and events to over 2000 delegates (both academic and non-academic) from across the UK. Our training covers a broad range of topics including data preparation, data analytics, data visualisation, applying different software packages and using different data sets. Our analytical training ranges from introductory methods through to statistical modelling techniques. We offer a programme of training open to academic staff and students, and individuals from public, private and third sector organisations. We are also able to work directly with groups and organisations to develop and deliver bespoke training and consultancy.

Find out more about us

老王vnp新版-老王vnp2.2.29最新版-老王加速npv下载-老王vqn2024官网ios苹果安卓下载-老王vnp

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老王vnp新版-老王vnp2.2.29最新版-老王加速npv下载-老王vqn2024官网ios苹果安卓下载-老王vnp

AQMEN provide training in statistical methods and data analysis for social researchers at all levels of study/career.

Find out more about training for social research

老王vnp新版-老王vnp2.2.29最新版-老王加速npv下载-老王vqn2024官网ios苹果安卓下载-老王vnp

A workforce with the appropriate analytical skills is critical for organisations that intend to work effectively as the big data revolution unfolds. AQMEN offer a programme of training and events in statistical methods and data analytics that are suitable for individuals from business and industry to attend. We are also able to work directly with organisations to develop and deliver bespoke training and consultancy.

Find out more about training for business and industry
Tablet, pen and charts
Statistical
Modelling
Tablet, pen and charts
Data
Wrangling
Bar chart
Analytical
Methods
Bar chart
Statistical Software
and Tools
Abstract network
Accessing New
Forms of Data
Abstract network
Data
Linkage
Pencils on table
Data
Visualisation
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Secondary
Data Analysis
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