Accreditation in data science: A recap
Introduction
I wrote last year about the process of going through accreditation as an Advanced Data Science Professional (ADSP) and what it entailed. In the past year another accreditation has come out, the Data Science Professional (DSP) for those at earlier stages in their career, and it has been revealed that ADSP will become a chartership once permission has been granted by the Privy Council.
I’ve started co-leading a mentoring group at work for those considering applying for either of the accreditations so I thought it was worthwhile recapping what these are, what is required to apply and how to apply.
Background
In 2019 Prof Andrew Blake authored a report on the dynamics of data science skills. Following this in 2020, instead of creating a new learned society, the Alliance for Data Science Professionals (AfDSP) was formed with input from the following professional bodies:
- British Computer Society
- Institute of Mathematics and its Applications
- Operational Research Society
- Royal Statistical Society
The Alan Turing Institute and the National Physical Laboratory also provided support. All bodies worked together to form the standards for each of the accreditations available today.
Two accreditation levels
As mentioned, there are two different levels of accreditation:
- DSP for those in the earlier stages of their career
- ADSP for those with more experience, including of technical leadership within their organisation.
Here’s a summary of the differences:
| Data Science Professional | Advanced Data Science Professional | |
|---|---|---|
| Relevant work experience | Two years | Five years |
| Continuous Professional Development evidence | Two years | Two years |
| Responsibility | Have personal responsibility for their own work Have responsibility for activities of a section or team |
Are fully accountable for their own work and that of others, including ethical considerations |
| Decision making | Have decision-making authority at the Section or team level | Have significant decision-making authority within their given area of expertise |
| Complexity | Act as an advisor/consultant and departmental level | Act as an advisor/consultant at a strategic level |
| Business impact | Apply technical skills in delivering outcomes Understands how their individual practice impacts other departments |
Undertake a range of complex work activities that have a significant impact Consider the impact across the business and more widely, of actions undertaken based on their decisions |
Five skill areas
Evidence must be demonstrated against each of the skill areas, which can appear daunting but it’s worth noting that the guidance says “it is not essential that an applicant meets all the criteria … but that on balance, the totality of their evidence for each section meets the required level”.
The areas are:
| Skill area | Detail |
|---|---|
| A. Data privacy and stewardship | Ensuring the protection of personal and sensitive data |
| Managing sensitive data | |
| Data stewardship and standards | |
| B. Definition, acquisition, engineering, architecture, storage and curation | Data collection and management |
| Data engineering | |
| Deployment | |
| C. Problem definition and communication with stakeholders | Problem definition |
| Relationship management | |
| D. Problem solving, analysis, statistical modelling, visualisation | Identifying and applying technical solutions and project management approaches |
| Data preparation and feature modelling | |
| Data Analysis and model building | |
| E. Evaluation and Reflection (cross-cutting consideration to be evidenced throughout) | Project evaluation |
| Ethical behaviour | |
| Sustainability and best practices | |
| Reflective practice and ongoing development |
Knowledge levels
For each of the accreditation levels, different levels of knowledge or experience are required for the skills as follows:
- Data Science Professional
- Applied - Skill area E plus two others
- Limited – two remaining skill areas
- Advanced Data Science Professional
- Deep – Skill area E plus two others
- General – two remaining skill areas
| Level | Description |
|---|---|
| Limited | Has knowledge and understanding of facts, procedures and ideas in the field of work. Can interpret relevant information and ideas. Is aware of a range of information that is relevant to the area of work. |
| General | Has factual, procedural and theoretical knowledge and understanding. Can interpret and evaluate relevant information and ideas. Is aware of the area of work. Is aware of different perspectives or approaches within the area of work. |
| Applied | Has practical, theoretical or technical knowledge and understanding of the field of work, enabling the applicant to address problems that are well defined but complex and non-routine. Can analyse, interpret and evaluate relevant information and ideas. Is aware of the nature of the approximate scope of the area of work. Has an informed awareness of different perspectives or approaches within the area of work. |
| Deep | Has advanced practical, conceptual or technological knowledge and understanding of the field of work, enabling the applicant to create ways forward in contexts where there are many interacting factors. Understands different perspectives, approaches or schools of thought and the theories that underpin them. Can critically analyse, interpret and evaluate complex information, concepts and ideas |
Application process
So you can apply through your chosen professional body, which is likely to be the body that best aligns with your background (for me that was the Royal Statistical Society). Each body has a slightly different process (for example, some require downloading of a form from their website, others have an online form). However all bodies will be judging applications against the framework above.
Summary
I’ve outlined the requirements of data science accreditation above, including the two different levels that can be applied for and the different knowledge levels that apply for each as well as the five skill areas that every application needs to demonstrate.
While it does take some effort to consider your experience with respect to these requirements and apply for accreditation, I’ve personally found it helpful in being able to demonstrate my professional competence through it. Good luck!