FDSC AI with Applied Data Analytics

Standard Entry requirements for Full Time Students

The following admissions criteria will normally apply at level 4\;

Students applying for this programme will be a minimum age of 18 years old and are expected to hold a relevant Level 3 qualification in a related subject with a minimum of?48 UCAS points (A level, BTEC).

Programmes will require 4 GCSEs at A*- C or 4 or above.

Students are also required to have at least grade 4 or above in GCSE maths and English or equivalent.

All applicants must be interviewed by the curriculum, where applicants live substantially away from the college, this can be conducted over live video chat.

Non-Standard Entry Requirements for Full Time Mature students

Mature students (aged at least 21 by the start of the academic year of admission) or Applicants with non-standard qualifications and/or experience and whose qualifications and/or experience is deemed to be appropriate to gain entry onto a named foundation degree may be admitted onto a foundation degree programme.

Such admission is discretionary and will normally involve an interview. In appropriate cases such applicants may be asked to undertake assessment activity to assist the College in determining their suitability for the programme.

Applicants will be judged using the following criteria:
Evidence of ability for self-organisation. Evidence of ability to work independently. The motivation to learn. Interest in the subject area. Evidence of ability to work with others.

Programme will prefer 4 GCSEs at A*- C or 4 or above, including maths and English or to have demonstrated ability in maths and English within further studies (such as key/functional skills).

You will be required to contribute to trips and visits. We also recommend that students buy texts to support learning, reading list are provided in module handbooks.

You will meet with a member of curriculum who will ask a set of standard questions. This is also an opportunity to learn about your motivation for the course and career aspirations, and an opportunity for you to ask questions. A tour of the facilities will be available.

The aim of the FdSc AI with Applied Data Analytics is to provide students with relevant knowledge, skills and behaviours applicable to Level 4 and 5 computing students with regards to the comprehension, design and exploitation of computation and computer technologies. The programme aims are as follows:

Develop and apply transferable technical and analytical skills that enable students to contribute effectively within data and AI-driven industries. Enable students to gain hands-on, work-related experience in data analytics and artificial intelligence through practical labs, simulations, and employer-led projects. Demonstrate systematic understanding of the concepts, methods, and tools that underpin AI and applied analytics, including data acquisition, modelling, visualisation, and predictive analysis. Critically evaluate the ethical, legal, and societal implications of AI and data analytics, ensuring responsible and inclusive practices in line with professional and regulatory standards. Apply advanced computational and problem-solving techniques to derive insight, automate processes, and support evidence-based decision-making within organisations. Recognise and uphold professional, legal, and ethical standards relating to data protection, algorithmic accountability, and responsible innovation.

Level 4

Personal & Professional Development 1 – 10 credits This module challenges you to develop the skills you need to become successful in higher education including report writing, research skills and independent thought. It will also introduce the soft skills needed to work in the computing and IT industry, along with ethical codes of conduct and personal development.

You will: Learn the importance of Professional Practice within the computing and IT industry and how to manage relationships, promote teamwork and interact positively with clients. Be introduced to goal setting, performance monitoring and self-reflection so that you can evaluate your personal progress while working to develop your transferable and employability skills to a professional standard.

Work Related Learning 1 Ethics and legal considerations (AI and Analytics)– 10 credits This module introduces students to the ethical, legal, and regulatory frameworks that underpin AI (inc. Shadow AI) and data use in organisations. Topics include the General Data Protection Regulation (GDPR), DPA, Freedom of Information, intellectual property, data bias, data bias with AI, consent, algorithmic transparency, and the social impacts of automated decision-making.? Students will critically evaluate contemporary case studies where ethical issues have impacted business or public trust. They will also reflect on their own professional responsibilities as data practitioners and understand the importance of ethical design and inclusive data practices.

Data Visualisation and Insight Communication (10 Credits) Students will explore how to turn raw data into meaningful visual insights using tools such as Python libraries or visual dashboards. The module introduces charts, infographics and storytelling techniques to help students present findings clearly and accurately for different audiences.

Digital Research and Emerging Technologies (AI Focus) (10 Credits) This module develops research and inquiry skills by investigating current and emerging AI technologies. Students will review real-world applications, trends and innovations, while learning how to source and evaluate digital information relevant to AI and analytics.

Database Design and Data Management (20 Credits) Students will learn to design, build, and manage databases using both relational (SQL) and non-relational (NoSQL) models. The module emphasises structuring, storing, and retrieving data efficiently, providing a strong foundation for data analysis. Learners will be introduced to key concepts in data modelling, normalisation, and systems analysis, developing the skills needed to manage enterprise-level datasets across diverse database technologies.

Fundamentals of AI and Data Analytics (20 Credits) This module introduces learners to the core principles of Artificial Intelligence and data analytics, exploring how data is collected, processed, and used to develop intelligent systems. Students will examine key concepts such as machine learning, data visualisation, algorithms, ethics, and real-world applications across industry sectors. Through practical activities and guided exercises, learners will develop foundational skills in data handling, problem-solving, and AI tools. The module builds confidence in using analytical thinking to interpret data and understand how AI drives decision-making in modern organisations.

Introduction to Programming for AI and Data Analytics (Python) (20 Credits)
Python is the leading language for both AI and data analytics, and this module introduces students to its core principles at a foundational level. Through hands-on practice, learners will develop confidence with basic syntax, control structures, data types, and essential libraries such as Pandas and NumPy for data handling. The module also introduces simple AI concepts, such as basic automation, pattern recognition, and using pre-built machine learning libraries to show how Python underpins intelligent systems. This creates a practical starting point for students progressing into applied AI and advanced analytics.

Applied Mathematics for AI and Data Analytics (20 Credits) This module introduces the core mathematical concepts that underpin AI and data analytics. Students will develop confidence with statistics, probability, linear algebra and problem-solving techniques used in algorithms, modelling and data interpretation. The focus is on practical application rather than abstract theory, supporting progression into machine learning and analytical methods.

Level 5

Applied Data Analytics (20 Credits) Data analytics continues to be one of the most sought-after skillsets across industries, with organisations increasingly relying on data-driven insights to inform strategic decisions. This module provides students with the practical skills and knowledge required to analyse data effectively, communicate findings, and support evidence-based decision-making.Students will learn to manipulate, analyse, and visualise data using Python. They will explore techniques for data cleaning, trend analysis, and performance measurement, and will gain hands-on experience in creating interactive dashboards and generating impactful analytical reports.

AI and Predictive Modelling (20 Credits) This module introduces students to the core principles of AI technologies and predictive modelling, with a strong emphasis on real-world application in data-driven business environments. Students will learn how to develop and evaluate AI technologies that support forecasting, classification, and decision-making.? Students will explore both supervised learning (e.g. regression and classification) and unsupervised learning (e.g. clustering and dimensionality reduction). They will gain hands-on experience in preparing datasets, training models, interpreting outputs, and understanding model accuracy and limitations.? The module will highlight how predictive analytics is applied across industries to automate processes, identify trends, and support strategic planning.

PPD2 (Small Scale Project) (10 Credits) This module develops the practical and technical behaviors required to work on AI and data projects. Learners will build confidence in version control, documentation, testing, collaboration tools and basic project workflows used in industry. They will explore how technical teams plan, communicate and deliver data or AI solutions, while producing evidence of their contribution to a small-scale project or problem brief. The focus is on applied teamwork, technical professionalism and working to simple project standards rather than personal reflection.

Work Related Learning 2 for Data Analytics (10 Credits) Students will undertake a work-based or simulated project that reflects a real data challenge in industry. This module reinforces employability by focusing on project planning, risk management, stakeholder communication, and problem-solving using data. Students will also document processes and outcomes in a professional portfolio format.

Agentic AI in Practice: Automating Business Processes (30 Credits) This module explores the transformative potential of agentic artificial intelligence (AI) in automating complex business processes. Unlike traditional generative AI, which primarily assists or co-pilots tasks, agentic AI enables autonomous decision-making and execution, fundamentally altering operational workflows. Through real-world case studies, students will examine how agentic AI is applied across various industries, including software development, customer service, and enterprise operations and finance. Data analytics will be a foundational component, enabling students to understand how AI agents interpret, respond to, and act upon data.

Advanced Data Visualisation and Storytelling with Data (30 Credits) This module equips students with the skills to present complex data clearly, persuasively, and ethically. It covers advanced data visualisation principles including accessibility, cognitive load, interactivity, and aesthetics. Students will develop interactive dashboards and reports using tools such as Power BI, learning how to tailor their message to diverse audiences including non-technical stakeholders.? Students will explore narrative techniques, colour theory, layout best practices, and user interface (UI) principles, culminating in a portfolio-based project that demonstrates effective data storytelling in a professional context.

Year 1 L4 Tuesday 09:00 - 17:00, Wednesday 09:00 - 17:00 Year 2 L5 Thursday 09:00 - 16:30, Friday 09:00 - 16:30

Any times indicated are based on current courses and may be subject to change in future years. Full timetables will be confirmed at the start of each course.

The nature of the programme is directly applicable to work situations and the theory is related to practical work-based situations, where possible. Each individual module will use different teaching and learning strategies depending upon the nature of the subject. The teaching and learning methods will include a combination of different approaches including but not limited to:

Lectures: The purpose of a lecture is to introduce the relevant theories/knowledge of the individual topic areas within a subject, e.g., Fundamentals of AI and Data Analytics

Workshops/Seminars: The purpose of the workshop/seminar is to develop the students understanding of the theories from the lectures by using case studies, role-plays and exercises, e.g. Data Visualisation and Insight Communication or Agentic AI in Practice (Automating Business Processes) that requires students to work with a client to determine the requirements for a working website, during workshops and seminars students will be taught industry standard design, build and testing techniques to enable them to deliver a working prototype that meets the clients' requirements and expectations.

Academic Tutorials: Students will be time tabled one hour per week for tutorial. The purpose of the tutorials is to develop study/academic skills, for example, research/information gathering. The development of these study/academic skills will assist in your learning throughout the programme, Students are advised to book at least one session with the named HE Academic tutor. Academic Tutors will also be invited to deliver a tutorial on academic skills during programme induction.

The department is currently building links with local employers within the sectors. This section will be updated when confirmed.

A work placement is a fundamental expectation of this foundation degree programme. You will be expected to source work experience which provides you with an opportunity to stretch your knowledge, understanding and practical skills beyond what you have learned in your modules. This will be a great opportunity for you to demonstrate your positive attitude, ability to use initiative and independent learning skills to future employers.

You will have the opportunity to gain professional advanced qualifications from the British Computer Society.

You could progress to a one year BSc (Hons) top-up degree at an accepting local or national university. We are also exploring the development of a one year BSc (Hons) top-up in house, which could be available by the end of this two year programme.