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Graduate Statistician

Graduate Statistician (GradStat) membership is for those with a statistics-based degree or a degree with significant statistics content – including theory, methods and practice. It's for those who want to further their statistical career towards CStat and demonstrate to employers their commitment to best practice and ethical conduct.

In addition to the benefits enjoyed by fellows, GradStats also get:

  • Use of GradStat post-nominal designation and digital badge
  • Access to the Society’s GradStat mentoring scheme
  • The opportunity to apply for a mid-term assessment where the Society provides feedback on your development
  • Networking opportunities through the Professional Statisticians’ Network
  • Further discounts on fees for Society conferences, events and courses.

How to become a GradStat:
You will need to join as a fellow first.

Join now

Then you must meet meet all of the criteria within one of the routes below – for full details, download the notes for guidance (PDF):

The standard route

  • You have graduated with an RSS accredited degree, meeting all conditions. See list of accredited degrees  
  • You have qualified with a non-accredited degree, but you can evidence you meet the RSS GradStat criteria set out further down the page.

The competency route

  • You hold a non-accredited level 6 or 7 degree (or equivalent)
  • have undertaken sufficient statistical training either as part of RSS Quality Marked courses or course that meet this standard (See standards here)
  • have at least two years’ work experience within a statistical role.
  • You will be asked a series of competency-based questions as part of your application.
  • Able to supply evidence of your continued professional development (CPD) of the past year, totalling 60 hours of learning.

Requirements

Graduates must have a good knowledge of:

  • The frequentist and Bayesian methods for conducting data analyses
  • Their logical foundations, including relevant probability theory
  • The principles of systematic data collection, management and curation.

They can use this knowledge, together with software and programming skills, to:

  • Build, assess and refine models appropriate for describing and understanding a wide variety of processes or problems
  • Draw appropriate inferences from them
  • Effectively communicate both substantive results and the nature of the uncertainty inherent in them, to expert or lay audiences.

They are aware of the implications of their work for the rights of individuals, are trustworthy, maintain the highest ethical standards and work for the public good.