ATHE Level 5 Extended Diploma in Computing and Artificial Intelligence
Introduction
This qualification is a 120-credit qualification aimed at learners building on Level 4 and pursuing a data analysis and Artificial Intelligence route through computing. Level 5 topics grow from Level 4 topics, and Level 5 Advanced Statistics and Artificial Intelligence units enable the learner to fully explore their AI interests. The Advanced Statistics unit underpins two units exploring natural language processing and
machine learning algorithms.
Grading
Graded with Pass, Merit and Distinction.
ATHE Level 5 Extended Diploma in Computing and Artificial Intelligence (120 credits)
Pass 360 – 431
Merit 432 – 539
Distinction 540
Advanced learner loans available in the UK – to check if funding is available see the latest Qualification Catalogue here.
For the progression routes visit our progression routes page.
Delivery Mode
This qualification can be delivered either in the classroom, via distance learning or blended.
Qualification Specification
To view the specification, please click here.
Additional
This qualification is eligible for UCAS points. To find out how much points your qualification is worth, please visit the UCAS Tariff Calculator here.
Typical Age
These qualifications are designed for learners who are typically aged 18+.
Qualifications
The entry profile for learners is likely to include at least one of the following:
- Prior study in computing or related subjects at Level 4 or above, or a Level 4 qualification, for example, an ATHE Level 4 Diploma/Extended Diploma in Computing; a Higher Technical Qualification (HTQ) in a computing-related subject
- Other equivalent international qualifications
Language
For those whom English is not their first language we recommend the following standards of proficiency in English language skills or an approved equivalent for this qualification:
- IELTs 5.5
- Common European Framework of Reference (CEFR) B2
- Cambridge English Advanced (CAE) 162 or above
- Pearson Test of English (PTE) Academic 42-49
Learners are required to achieve all five mandatory units plus at least three units from units 5 – 8.
| Unit Name | Unit Aims | Credits | Mandatory |
|---|---|---|---|
| Computing Projects for Digital Transformation | This unit enables learners to begin to think about how digital transformation may benefit their organisation, their job role, or the day-to-day activities within their organisation or their team. This understanding should be digital pathway neutral as learners at Level 5 will be expected to understand and contribute to plans for a digital transformation, regardless of their role. | 15 | Yes |
| Professional Development and Business Communication | This unit builds on the professional practice content first introduced in Level 4’s Unit 11 (Synoptic Project and Professional Best Practice) where learners considered team behaviours that contribute to effective working, written and oral communication and wider considerations such as ethical practice and an understanding of relevant legislation. Because the IT industry is fast moving, practitioners should understand that working in this sector will necessitate continuous professional development (CPD), often including the updating of technical skills as well as the development of professional skills as practitioners are promoted. | 15 | Yes |
| Advanced Statistics for Data Science | This unit introduces statistical and probability concepts for understanding statistical models. It enables learners to numerically evaluate the performance of algorithmic models and equips them with practical knowledge of statistical modelling techniques such as regression, Bayes classifiers and support vector machines. The learners will undertake a practical activity in applying one of these algorithms to data. | 15 | Yes |
| Information Systems | When working in industry, IT practitioners must be able to see the bigger picture, drawing on all of their knowledge and previous experiences to find the best and most viable solutions to the problem. This is particularly important when working with non-technical managers who may not understand the technologies that they need to be able to perform their role. Practitioners must be able to share their knowledge and understanding in an appropriate way to promote the use of data and information systems to support modern business and enterprise. This unit enables learners to examine a wide range of information systems that they will find in industry. | 15 | Yes |
| Advanced Project | This unit is designed to enable learners from any pathway to resolve a business problem or show how a business opportunity could be pursued using appropriate tools and technologies. The project should be a suitable match to their study pathway and should make use of the knowledge and skills gained when studying the other units making up their qualification. | 15 | Yes |
| Optional Units | |||
| Unit 5 Advanced Database Practice | This unit will teach the learners relational theory concepts such as normalisation, foreign keys, ensuring consistency, three value logic, indexes for efficiency, database optimisation etc. As an advanced course, it will presume that the learners have some familiarity with basic SQL extraction and manipulation techniques, such as those taught in L4 Unit 8. It will teach how to create various database objects and their benefits, e.g. tables, dynamic/materialised views, triggers, stored procedures, user defined functions etc. | 15 | No |
| Unit 6 Programming for Data Engineering | This unit introduces various tools and languages used for data engineering. It presumes that the learners have a basic understanding of programming at the level normally taught in L3 or L4 computing programming syllabi. It will use the SciPy ecosystem of module in Python (mostly pandas and matplotlib) to perform efficient programmatic data loading and data manipulation using a variety of functions provided by the modules. | 15 | No |
| Unit 7 Foundations of Artificial Intelligence for Data and Language | This unit introduces the learners to the processes of how machines learn from data. It covers the basics of natural language processing as well as applying these techniques to a dataset. Learners will also investigate how generative AI models work, their strengths and weaknesses, and how such models can be used as components in computer systems. | 15 | No |
| Unit 8 Machine Learning in Practice: Structure, Strategy, and Evaluation | This unit introduces the learners to a wide range of modern machine learning algorithms as well as applying these techniques to a dataset. The focus is on practical implementations as well as evaluating their performance. | 15 | No |