Overview

This topic offers an in-depth exploration of the key concepts, techniques, and tools necessary for data engineering, focusing on preparing data for analytics and decision-making processes. Students will gain hands-on experience in designing and implementing data pipelines and applying advanced data transformation, modelling, and analytics techniques. By leveraging modern data … For more content click the Read More button below.

Topic availabilities

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Tuition pattern

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Aims

This topic aims to:

  • Develop a strong foundation in data engineering principles.
  • Equip students with practical data pipeline design and implementation skills.
  • Foster expertise in data transformation, modelling, and analytics.
  • Instil a deep understanding of data quality, security, and privacy.
  • Introduce modern data platforms and architectural solutions.
  • Enable students to support data-driven decision-making and AI integration.

Learning outcomes

On completion of this topic you will be expected to be able to:
1.
Critically evaluate the fundamental concepts, tools, and technologies involved in the data engineering lifecycle, including data generation, storage, processing, and visualization
2.
Architect and implement robust data pipelines and storage solutions, integrating data from diverse sources and ensuring efficient and scalable data processing workflows
3.
Apply data transformation, modelling, and preprocessing techniques to ensure data quality, integrity, and suitability for advanced analytics and machine learning applications, while addressing security, privacy, and ethical considerations, applying best practices for compliance and data management
4.
Evaluate and apply modern data management architectures such as data lakes, data meshes, and DataOps, enabling scalable and efficient data processing and analysis
5.
Prepare and optimize data for AI and machine learning models, facilitating data-driven decision-making and the integration of data insights into business processes

Assessments

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Current students should refer to FLO for detailed assessment information, including due dates. Assessment information is accurate at the time of publishing.

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Requisites information

Pre-requisites:
Anti-requisites:

Assumed knowledge

Basic Knowledge of Statistics; Sound knowledge of at least one programming/scripting language, e.g., C++/Java/Matlab/R/Python