Course name: Undergraduate Diploma in Level 4 Diploma in Artificial Intelligence
No. of units:
DAI402 to DAI406
Qualification:
Level 7 Diploma in Accounting and Finance - 610/3934/2
Course option
£3,500 /£3,500
About This Course
The aim of the QUALIFI Level 4 Diploma in Artificial Intelligence is to to equip students with understanding of AI concepts and applications at an entry level. The diploma covers a range of topics, including the history and principles of AI, machine learning, data science, deep learning, AI ethics, and practical AI applications. Students will gain hands-on experience with popular AI tools and develop programming skills in languages like Python.
The Diploma emphasises ethical considerations and responsible AI development. It typically includes project work to apply AI knowledge to real-world challenges and prepares students for careers in AI development, data science, machine learning engineering, or further academic pursuits in the field. Assessment methods include exams, assignments, and project evaluations to ensure an understanding of AI principles and their practical application.
Successful completion of the QUALIFI Level 4 Diploma in Artificial Intelligence provides learners with the opportunity to progress to further study or employment.
The aim of the QUALIFI Level 4 Diploma in Artificial Intelligence is to to equip students with understanding of AI concepts and applications at an entry level. The diploma covers a range of topics, including the history and principles of AI, machine learning, data science, deep learning, AI ethics, and practical AI applications. Students will gain hands-on experience with popular AI tools and develop programming skills in languages like Python.
The Diploma emphasises ethical considerations and responsible AI development. It typically includes project work to apply AI knowledge to real-world challenges and prepares students for careers in AI development, data science, machine learning engineering, or further academic pursuits in the field. Assessment methods include exams, assignments, and project evaluations to ensure an understanding of AI principles and their practical
application.
Successful completion of the QUALIFI Level 4 Diploma in Artificial Intelligence provides learners with the opportunity to progress to further study or employment. Learning Outcomes of the QUALIFI Level 4 Diploma in Artificial Intelligence The overall learning outcomes of the qualification are for learners to:
1. Demonstrate a fundamental understanding of artificial intelligence concepts, theories, and principles.
2. Apply machine learning algorithms and techniques to solve real-world problems.
3. Analyse and process data for AI applications, including data cleaning, transformation, and feature engineering.
4. Develop basic AI solutions using programming languages such as Python.
5. Evaluate the ethical and legal considerations associated with AI technologies.
6. Distinguish various impacts of AI on society, economy, and industry.
Structure of the Qualification
4.1 Units, Credits and Total Qualification Time (TQT)
The units have been designed from a learning time perspective and are expressed in terms of Total
Qualification Time (TQT). TQT is an estimate of the total amount of time that could reasonably be
expected to be required for a learner to achieve and demonstrate the achievement of the level of
attainment necessary for the award of a qualification. TQT includes undertaking each of the
activities of guided learning, directed learning and invigilated assessment. 120 credits equate to
1200 hours of TQT.
Examples of activities that can contribute to Total Qualification Time include:
- guided learning.
- independent and unsupervised research/learning.
- unsupervised compilation of a portfolio of work experience.
- unsupervised e-learning.
- unsupervised e-assessment.
- unsupervised coursework.
- watching a prerecorded podcast or webinar.
- unsupervised work-based learning.
Entry Criteria
The qualifications have been designed to be accessible without artificial barriers that restrict access and progression. Entry to the qualifications will be through centre interview and applicants will be expected to hold the following:
- Qualifi Level 4 Diploma in Artificial Intelligence
- learners who possess qualifications at Level 3 and/or;
- learners who have work experience in the governmental and non-governmental sector and demonstrate ambition with clear career
goals.Qualifi Level 5 Diploma in Artificial Intelligence
- learners who possess qualifications at Level 4 and/or;
- learners who have work experience in the governmental and non-governmental
sector and demonstrate ambition with clear career goals.
In certain circumstances, learners with considerable experience but no formal qualifications may be considered, subject to interview and demonstrate their ability to cope with the qualification’s demands.
Recognition of Prior Learning
Recognition of Prior Learning (RPL) is a method of assessment (leading to the award of credit) that considers whether learners can demonstrate that they can meet the assessment requirements for a unit through knowledge, understanding or skills they already possess and so do not need to develop through a course of learning
Examples of activities that can contribute to Total Qualification Time include:
guided learning.
- independent and unsupervised research/learning.
- unsupervised compilation of a portfolio of work experience.
- unsupervised e-learning.
- unsupervised e-assessment.
- unsupervised coursework.
- watching a prerecorded podcast or webinar.
- unsupervised work-based learning.
Guided Learning Hours (GLH) are defined as the time when a tutor is present to give specific guidance towards the learning aim being studied on a programme. This definition includes lectures, tutorials and supervised study in, for example, open learning centres and learning workshops.
Guided learning includes any supervised assessment activity; this includes invigilated examination and observed assessment and observed work-based practice.
Recognition of Prior Learning
Recognition of Prior Learning (RPL) is a method of assessment (leading to the award of credit) that considers whether learners can demonstrate that they can meet the assessment requirements for a unit. through knowledge, understanding or skills they already possess, and so do not need to develop through a course of learning.
Examples of University Progression
- University of Sunderland – On Campus
- Anglia Ruskin University
- Coventry University
Progression routes:
- QUALIFI Level 5 Diploma in Artificial Intelligence
- Employment in an associated profession.
Awarding Body
Qualification
Qualification Numbers:
Level 7 Diploma in Accounting and Finance - 610/3934/2
Qualification number (RQF):
Course Details
Unit DAI401: Introduction to Artificial Intelligence and Applications
Unit code: R/651/0599
RQF Level: 4
Unit Aim:
This unit will provide students with a fundamental understanding of Artificial Intelligence
(AI) and is an introductory unit for the Diploma in Artificial Intelligence Application. Students
will gain knowledge of the evolving field of AI and explore the basic theoretical foundation
of AI as it is applied in industry.
Indicative Content
– AI principles and foundation, its technologies and impacts on society
– Trending AI application and their benefits in industries such as Education, Marketing
and Small Businesses
– AI models and its purposes (Neural network, supervised, unsupervised, reinforced
learning )
– AI technologies including data science, machine learning, natural language processing,
computer vision, speech/image recognition and robotics
– Implementation of AI and its challenges
Unit code: F/651/0600
RQF level: 4
Unit Aim
In this unit students will explore the essential mathematical principles that form the bedrock
of modern machine learning. The unit covers core concepts such as linear algebra, calculus,
probability, and statistics, providing a mathematical foundation for understanding machine
learning algorithms and techniques. Students will develop the skills needed to translate
problems into mathematical models and communicate solutions effectively. Students will
quantify business solutions to a given complex dataset
This unit introduces students to the key concepts and characteristics of big data technologies, data mining, visualization, real-world applications, and ethical implications in the field of big data analytics. Students will explore big data and its crucial role in today’s data-driven landscape and the structured to provide understanding and practical expertise in managing and analyzing extensive datasets. Students will develop an understanding of big data fundamentals and gain hands-on experience of using cutting-edge technologies. The unit aims to equip students with the necessary skills and knowledge to excel in the rapidly evolving field of big data analytics. Students will apply their knowledge and skills to a comprehensive dataset and present their data analysis.
Indicative Content
– Fundamentals of Big Data and Technologies
– Hadoop and NoSQL, MapReduce, Spark databases
– Data Mining and Processing
– Classification or clustering
– Data Visualization and its applications in industries such as retail eCommerce, Public
Relation, and Human Resources
– Tableau or PowerBI
– Ethical Implications in optimizing big data
In this unit learners will develop an understanding of ethical considerations and principles in relation to the development, deployment, and impact of AI. Students will discuss various industrial case studies to appreciate the ethical challenges associated with AI systems. They will identify dilemmas and express their views on possible conflicting outcomes. They will gain knowledge and skills to address ethical issues and explore factors to consider in making informed and responsible decisions. Students will have the opportunity to compare and analyse the various AI regulatory landscapes as advocated by global governmental agencies.
Indicative Content
– Ethical implications of AI technologies and AI ethical framework
– Privacy, fairness, transparency, and accountability.
– Challenges of bias AI algorithms and its implication on fairness
– Methods to examine and mitigate ethical issues in AI systems
– Existing regulatory landscape in governing AI systems
All unit grading is shown on the qualification transcript.
QUALIFI Level 4 Artificial Intelligence
Pass mark is 40% for each unit.
Pass mark is 40% fo reach unit.
Fail – 0-39%
Pass – 40%-59%
Merit – 60% – 69%
Distinction 70%+
Learners’ assessments will be marked internally by the approved centre and will be subject to external moderation by QUALIFI prior to certification.
• Introduction to Artificial Intelligence and Applications
• Mathematical Foundations for Machine Learning
• Data Science Using Python
• Big Data Management
• Introduction to Deep Learning
• Artificial Intelligence Ethics
External Quality Assurance Arrangements All centres are required to complete an approval process to be recognised as an approved centre. Centres must have the ability to support learners. Centres must commit to working with QUALIFI and its team of External Quality Assurers (EQAs). Approved centres are required to have in place qualified and experienced tutors.
All tutors are required to undertake regular continued professional development (CPD). Approved centres will be monitored by QUALIFI External Quality Assurers (EQAs) to ensure compliance with QUALIFI requirements and to ensure that learners are provided with appropriate learning opportunities, guidance and formative assessment. QUALIFI’s guidance relating to invigilation, preventing plagiarism and collusion will apply to centres. Unless otherwise agreed, QUALIFI:
• sets all assessments.
• moderate’s assessments prior to certification.
• awards the final mark and issues certificates.
Formative Assessment
Formative assessment is an integral part of the assessment process, involving both the tutor/assessor and the learner about their progress during the course of study. Formative assessment takes place prior to summative assessment and focuses on helping the learner to reflect on their learning and improve their performance and does not confirm achievement of grades at this stage.
Summative Assessment
Summative assessment is used to evaluate learner competence and progression at the end of a unit or component. Summative assessment should take place when the assessor deems that the learner is at a stage where competence can be demonstrated. Learners should be made aware that summative assessment outcomes are subject to confirmation by the Internal Verifier and External Quality Assurer (EQA) and thus is provisional and can be overridden. Assessors should annotate on the learner work where the evidence supports their decisions against the assessment criteria. Learners will need to be familiar with the assessment and grading criteria so that they can understand the quality of what is required.
- Demonstrate a fundamental understanding of artificial intelligence concepts, theories, and principles.
- Apply machine learning algorithms and techniques to solve real-world problems.
- Analyze and process data for AI applications, including data cleaning, transformation, and feature engineering.
- Develop basic AI solutions using programming languages such as Python.
- Evaluate the ethical and legal considerations associated with AI technologies.
- Distinguish various impacts of AI on society, economy, and industry.
Units
Mandatory
- Introduction to Artificial Intelligence and Applications
- Mathematical Foundations for Machine Learning
- Data Science Using Python
- Big Data Management
- Introduction to Deep Learning
- Artificial Intelligence Ethics
provide career path support to learners who wish to develop their management skills,
enterprise capabilities and opportunities in their chosen sector
- improve learner understanding of any given business environments and organisations and how they are managed and developed
- develop skills and abilities in learners to support their professional development.
On completion of this course students have the opportunity to complete an Degree programme from a range of UK universities. With level diploma, you are qualifying for university year 3 for degree.
University of Gloucestershire
Anglia Ruskin University
University of Bolton
University of Sunderland
Westcliff University
Northampton University
University of Derby
And More:
To enrol onto the level 4 programme, you must be either.
a) a university graduate who is over 18 years old, or
b) a non-university graduate over 24-year-old, and with at least five years of managerial experience.
All course material, including online modules and written assignments.
Personal tutor support with 1-2-1 Zoom sessions
Dedicated student support
Access to an online social learning forum
Assignment marking and feedback.
FREE TOTUM student discount card
FREE laptop*
FREE access to Our Hubs
The fee for the level 4 Diploma in Artificial Intelligence course is £3,500.
Students can make payment using one of the following methods:
• Credit or debit card.
• Bank transfer.
• Interest free monthly instalments
• PayPal
Choose your course option
Request a Course Prospectus
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