Modules
Year 1 (Level 4)
Computer Network and Network Operating Systems (30 credits)
This module is an introduction to networking and networking operating systems where apprentices will identify key components of a network system and their operating system. Apprentices will gather basic understanding of networking protocols and be able to set up and troubleshoot hardware and software problems.
Software Development Principles (30 credits)
Developing high-quality software requires more than just coding skills. This practical module explores a wide range of software engineering techniques and industry practices, designed to promote the production of high-quality, efficient, reliable, and secure software.
Apprentices are introduced to algorithms with focus on Sorting (e.g., bubble, quick, heap), Searching (e.g., breadth first and depth first) and Merging (e.g., Simple and K-way).
Data Analytics and Database Systems (30 credits)
Apprentices will be able to identify organisational information requirements and be able to model data solutions using conceptual data modelling techniques.
Apprentices will develop skills in designing and implementing a database solution using industry-standard database management systems. Apprentices will also be able to perform database administration tasks, be able to manage data effectively and will learn about the key concepts of data quality and data security. They will also understand how organisations can use data analytics to support them in making decisions by using data dashboards as an example.
Mathematics and Statistics for Data Science (30 credits)
This module introduces the fundamental mathematical and statistical concepts essential for data science. Apprentices will gain a solid foundation in linear algebra, calculus, probability, and statistical methods, which are crucial for understanding and applying data science techniques.
The module is delivered in a practical environment with exposure to a range of real practical applications where mathematical and statistical techniques are used and deployed in Data Science areas. Specific software such as (but not limited to) MATLAB, SimuLink, Python and R Studio will be used to test and evaluate models and techniques.