The global skills and competency framework for the digital world

Data science DATS

Applying mathematics, statistics, data mining and predictive modelling techniques to gain insights, predict behaviours and generate value from data.

Revision notes

Updates for SFIA 9

  • Theme(s) influencing the updates for this skill: Continued refinement for data and analytics skills.
  • Level 7 has been moved to a new skill - Data analytics
  • Content and/or readability changes have been made to levels 2, 4, 5, and 6.
  • You can move to SFIA 9 when you are ready - SFIA 8 skill descriptions will still be available to use.
  • Previous SFIA assessments or skills mapping may be impacted by this change.

Guidance notes

Data science is typically used for analysing high volume, high velocity and high variety data (numbers, symbols, text, sound and image).

Activities may include, but are not limited to:

  • integrating methods from mathematics, statistics and probability modelling using specialised programming languages, tools and techniques
  • sourcing and preparing data for analysis
  • identifying, validating and exploiting internal and external data sets generated from a diverse range of processes
  • developing forward-looking, predictive, real-time, model-based insights to create value and drive effective decision-making
  • finding, selecting, acquiring and ingesting data sources
  • integrating and cleansing data to make it fit for purpose
  • developing hypotheses and exploring data using models and analytics sandboxes
  • refining requirements, validating, training and evolving models over time to discover deeper insights, make predictions or generate recommendations
  • using advanced analytic techniques including, but not limited to: data/text mining, machine learning, pattern matching, forecasting, visualisation, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing and neural networks.

Understanding the responsibility levels of this skill

Where lower levels are not defined...
  • Specific tasks and responsibilities are not defined because the skill requires a higher level of autonomy, influence, and complexity in decision-making than is typically expected at these levels. You can use the essence statements to understand the generic responsibilities associated with these levels.
Where higher levels are not defined...
  • Responsibilities and accountabilities are not defined because these higher levels involve strategic leadership and broader organisational influence that goes beyond the scope of this specific skill. See the essence statements.

Developing skills and demonstrating responsibilities related to this skill

The defined levels show the incremental progression in skills and responsibilities.

Where lower levels are not defined...

You can develop your knowledge and support others who do have responsibility in this area by:

  • Learning key concepts and principles related to this skill and its impact on your role
  • Performing related skills (see the related SFIA skills)
  • Supporting others who are performing higher level tasks and activities
Where higher levels are not defined...
  • You can progress by developing related skills which are better suited to higher levels of organisational leadership.

Show/hide extra descriptions and levels.

Levels of responsibility for this skill

2 3 4 5 6

Data science: Level 2

Level 2 - Assist: Essence of the level: Provides assistance to others, works under routine supervision, and uses their discretion to address routine problems. Actively learns through training and on-the-job experiences.

Under routine supervision, applies specified data science techniques to data.

Analyses and reports findings and addresses simple issues, using algorithms included within standard software frameworks and tools.

Data science: Level 3

Level 3 - Apply: Essence of the level: Performs varied tasks, sometimes complex and non-routine, using standard methods and procedures. Works under general direction, exercises discretion, and manages own work within deadlines. Proactively enhances skills and impact in the workplace.

Applies standard data science techniques to new problems and datasets using specialised programming techniques.

Identifies and selects appropriate data sources and prepares data to be used by data science models.

Evaluates the outcomes and performance of data science models. Identifies and implements opportunities to train and improve models and the data they use.

Publishes and reports on model outputs to meet customer needs and conform to agreed standards.

Data science: Level 4

Level 4 - Enable: Essence of the level: Performs diverse complex activities, supports and guides others, delegates tasks when appropriate, works autonomously under general direction, and contributes expertise to deliver team objectives.

Investigates problems and datasets to assess the usefulness of data science solutions.

Applies diverse data science techniques and specialised programming languages. Understands and applies rules and guidelines specific to the industry and business, and anticipates risks and other implications of modelling.

Selects, acquires and integrates data for analysis. Formulates hypotheses and evaluates data science models. Advises on the effectiveness of specific techniques based on analysis findings and research.

Contributes to the development, evaluation, monitoring and deployment of data science solutions.

Data science: Level 5

Level 5 - Ensure, advise: Essence of the level: Provides authoritative guidance in their field and works under broad direction. Accountable for delivering significant work outcomes, from analysis through execution to evaluation.

Plans, coordinates and drives all stages of the development of data science solutions.

Provides expert advice to evaluate the problems to be solved and the need for data science solutions. Identifies and justifies what data sources to use or acquire.

Specifies and applies appropriate data science techniques and specialised programming languages.

Critically reviews the benefits and value of data science techniques and tools and recommends improvements. Contributes to developing policy, standards and guidelines for developing, evaluating, monitoring and deploying data science solutions.

Data science: Level 6

Level 6 - Initiate, influence: Essence of the level: Has significant organisational influence, makes high-level decisions, shapes policies, demonstrates leadership, promotes organisational collaboration, and accepts accountability in key areas.

Champions and leads the introduction and use of data science to drive innovation and business value.

Develops and drives adoption of and adherence to organisational policies, standards, guidelines and methods for data science.

Sets direction and leads in the introduction and use of data science techniques, methodologies and tools. Leads the development of organisational capabilities for data science.

Plans and leads strategic, large and complex data science initiatives to generate insights, create value and drive decision-making.

Keywords
Split Level removed