People producing statistics should be appropriately skilled, trained and supported in their roles and professional development.
T5.1 Those involved in producing and releasing statistics and data should demonstrate sound judgement. They should act professionally, work collaboratively, and behave responsibly.
T5.2 The roles and responsibilities of those involved in the production of statistics and data should be clearly defined with supporting guidance provided to help staff carry out their roles.
T5.3 Suitably skilled staff should be recruited using a relevant professional competency framework as appropriate and with consideration given to future organisational needs.
T5.4 All staff involved in the production of statistics and data should be provided with training on secure data handling and quality management.
T5.5 Staff should be provided with the time and resources required to develop their skills, knowledge and competencies.
T5.6 Staff should seek statistical advice and guidance from the Chief Statistician/Head of Profession for Statistics.
The Department for Transport (DfT) have been up-skilling their analysts to facilitate the adoption of data science methods in the department. To help with this, DfT have established weekly Coffee & Coding sessions and bespoke R coding workshops, building on successful models used in the Department for Education and Business Enterprise Industry and Skills.
Coffee & Coding sessions aim to nurture and encourage a vibrant, supportive and inclusive coding community. They provide a regular opportunity for people to share coding skills, knowledge and advice, and to network and get to know each other. The format is usually a presentation followed by a Code Surgery. Presentations usually demonstrate a tool or technique and/or a show and tell of new work done within the department. Code Surgery’s allow people to raise coding queries or ideas with the coding community; there is no such thing as a silly question and it is understood that the quest for knowledge necessarily includes failure.
The R workshops are a suite of sessions designed to train DfT’s statisticians in the basics of R coding. They are mainly based around the use of tidyverse R libraries to maintain regular standards, and include topics such as data wrangling with dplyr, graphing with ggplot2, and report automation with rmarkdown. DfT’s first cohort graduated in late 2018 and the second is due to start in early 2019.
DfT run a mentorship programme (akin to the GDS Data Science Accelerator) to provide support to those taking on data science projects using a new tool or method. DfT expect that eventually there will be enough coders in the department that asking for statistical coding advice will be as easy to source as advice on using Excel.
A big part of DfT’s approach is to encourage people to share knowledge, so that pioneers trying methods for the first time generate resources for others to use and adapt. GitHub has become central to this process – DfT use it to share code and host any materials from DfT’s weekly coding meetings and to signpost to useful resources online. DfT have also developed coding standards, that specify DfT’s minimum requirements for ‘good code’, whilst not burdening the developer with lots of extra work. For example, DfT require that the master version of a script is not edited without going through a code review and encourage the use of automated testing (Continuous Integration) tools. The document is community edited, so standards can evolve as change as needed.
DfT encourage analysts to use similar variants of code and to follow a style guide. For data analysis, R and Python have proved popular language choices, but there are also style differences within R and Python. For this reason, DfT have default suggested packages in DfT’s coding standards and approach the R workshops with a consistent coding style, encouraging developers to use the Tidyverse syntax style. This means that a relatively new coder only has to learn this syntax style to be able to interpret typical code across the department.
DfT collaborate closely with their Digital Services team to ensure that the core functions of the software development tools work, making sure analysts can install packages for Python and R, use Git to version control their code, and use dependency management tools like packrat.
This example shows how DfT staff are provided with the time and resources required to develop new coding skills, knowledge and competencies to meet DfT’s future organisational needs and how DfT is developing new quality strategies and standards.
|The Government Statistical Service (GSS) website contains links to relevant news, comments from the National Statistician, the Policy Store, job vacancies, conferences, and learning and development opportunities.||GSS website||GSS|
|The Government Statistician Group (GSG) Guide to Continuing Professional Development (CPD) is applicable to both Statisticians and Data Scientists within the Statistics profession and is intended to help you through|
the process of identifying and meeting your learning and development needs.
|GSG Guide to Continuous Professional Development (PDF, 0.12MB)||GSS|
|The Government Statistician Group (GSG) Competency Framework sets out what members of the statistical profession are expected to achieve in addition to the Civil Service Competency Framework (CSCF). It can be used in performance management discussions, objective setting at the start of each performance year, or when moving posts.||GSG Competency Framework||GSS|
|The Royal Statistical Society (RSS) encourages all its members, and requires its professionally qualified members, to follow a process of continuous professional review through CPD. The Society provides guidelines on minimum targets at which to aim.||RSS Continuing Professional Development policy||RSS|
|The United Nations Economic Commission for Europe’s (UNECE) guide on Making Data Meaningful is intended as a practical tool to help managers, statisticians and media relations officers use text, and visualisations to bring statistics to life for non-statisticians. It contains suggestions, guidelines and examples.||A UNECE guide to Making Data Meaningful||UNECE|
|Guidance on statistics for policy professionals, developed by the Good Practice Team. It aims to help policy staff work effectively with statisticians and other analysts, by introducing some key ideas and concepts to help you ask the right questions when working with statistical evidence.||Guidance on statistics for policy professionals||GSS|