What is the Data Science Competency Framework?
The data science field is in a period of growth and evolution. This growth can be hard for organisations to keep up with, especially because the requirements of the data science workforce haven't been uniformly defined or established.
D2D CRC created the Data Science Competency Framework (DSCF) to support the development and growth of this workforce. The intention is that the framework helps define and standardise the skills and competencies required in the data science profession, and provide a tool to measure against these benchmarks.
The DSCF has a supporting tool, the Development Planning Tool (DPT), which is a web application allowing users to identify and plan their professional development pathway. Trial the DPT here.
Who is the DSCF for?
This framework has been developed by data science professionals for data science professionals - people with an inherent understanding of what is required to successfully fulfil these types of roles, as well as grow into new opportunities as they develop.
The DSCF avoids technical jargon so it can also be used by those who are not in the data science field, such as human resource managers and team managers. This means they can understand the competencies of their workers and set expectations and benchmarks for those they manage.
What does the DSCF do?
The DSCF sets the skills and attributes of each data science job family generally, so it can be applied across organisations while still producing a standard for each individual to work towards and meet. It also shows relevant competencies to develop for career progression.
How does the DSCF work?
The DSCF is separated into three competency areas, which are then applied within different job families.
The three competency areas are:
- Data analytics solution life cycle: competencies related to processing and managing data projects.
- Technical: competencies relating specifically to big data, technologies and tools.
- Core: data science related competencies that often have organisational relevance
The three job families are:
- data scientist
- data engineer
- data analyst
These job families are then broken down into industry and Australian Public Service (APS) maturity levels, each with a different range of expertise.
Individuals or teams within these job families can use the framework to measure their performance against expectations that apply to their maturity level. The DSCF also reveals opportunities and pathways for professional development.
What is the Development Planning Tool?
The Development Planning Tool (DPT) is a web application, where users answer a series of questions and rate their competency against a variety of criteria. The tool shows the end user their results against the benchmark for their role and maturity level - it shows areas of success and gives suggestions for development.
Trial the DPT here.