Intermediate

Data ingestion and management:
Establish data connectors to ingest data from external databases and leverage APIs as a way of integrating a variety of data types on a regular basis either through basic coding or point-and-click tools. They can also begin establishing automated data ingestion pipelines and review the quality of these. They can apply schema to new datasets and are able to create more advanced links between objects as well as begin configuring actions.

Data manipulation and coding:
Comfortable using both ‘point-and-click’ tools as well as more advanced coding techniques in applications such as code workbook and code repositories. For example, train and test simple machine learning models and output predictions all within the FDP environment, automate data processing workflows, manage the deployment and management of code using code repositories (e.g. branches, commits, pull requests to merge)

Data visualisation:
Operationalise workflows to enable interactive dashboards where visualisations vary dynamically (e.g. through dynamic filters or application of actions/scenario actions.
Embed a range of visuals across different applications (e.g. workshop, carbon workspace) to support visualising a range of outputs within a single environment. Can include more advanced visualisation features such as time series analyses which have their own unique data dependencies.