Data science (new) DATS


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

Guidance notes


Typically applied in the analysis of high volume, high velocity and high variety data (numbers, symbols, text, sound and image).

Activities may include - but not limited to...

  • integrating methods from mathematics, statistics, 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 clean data to make it fit for purpose
  • develop data science hypotheses and exploring data using models and analytics sandboxes
  • refine requirements, validate, train and evolve models over time
  • to discover deeper insights, make predictions, or generate recommendations.
  • advanced analytic techniques include those such as data/text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, neural networks.

Data science (new): Level 7


Directs the creation and review of a cross-functional, enterprise-wide approach and culture for generating value from data science and analytics.

Drives the identification, evaluation and adoption of data science and analytics capabilities to transform organisational performance. Leads the provision of the organisation’s data science and analytics capabilities.

Ensures that the strategic application of data science and analytics is embedded in the governance and leadership of the organisation. Aligns business strategies, enterprise transformation and data science and analytics strategies.

Data science (new): Level 6


Leads the introduction and use of data science and analytics to drive innovation and business value.

Develops organisational policies, standards, and guidelines for data science and analytics.

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

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

Data science (new): Level 5


Plans and drives all stages of the development of data science and analytics solutions. Provides expert advice to evaluate the problems to be solved and the need for data science solutions. Identifies what data sources to use or acquire.

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

Reviews the benefits and value of data science techniques and tools and recommends improvements.

Contributes to the development of policy, standards and guidelines for developing, evaluating, monitoring and deploying data science solutions.

Data science (new): Level 4


Investigates the described problem and dataset to make an assessment of the usefulness of data science and analytics solutions.

Applies a range of data science techniques and uses specialised programming languages.

Understands and applies rules and guidelines specific to the industry, and anticipates risks and other implications of modelling.

Selects, acquires and integrates data for analysis. Develops data hypotheses and methods and evaluates analytics models. Advises on the effectiveness of specific techniques, based on project findings and on knowledge of wider research.

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

Data science (new): Level 3


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

Selects from existing 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 conforming to agreed standards.

Data science (new): Level 2


Applies given data science techniques to data, under guidance.

Analyses and reports findings and remediates simple issues, using algorithms implemented in standard software frameworks and tools.