This course teaches candidates the integration of data science and data engineering into aspects of the data analysis field. Candidates will be introduced to big data theory at a practical level, as well as how to use existing machine learning assets and model clusters. This course does not teach candidates how to create new machine learning systems. Instead, it will teach them how to discover the required services to integrate pre-trained models and artificial intelligence systems into their datasets. Additionally, advanced implementations of query-based tools such as Power Bi and SQL databases will be explored.

This course is relevant because most modern cloud-based data analysis lifecycles utilise big data in their systems. Candidates must be able to identify appropriate pre-trained models and how to apply said models to their respective data projects. Candidates must be able to use these tools alongside previous theory-learned tools to assemble a complete process but must first understand critical data theory. Candidates can quickly integrate cutting-edge machine-learned data sets and artificial intelligence components into any required data projects.