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Go from complexity to clarity

100% Online

15 months (part-time)

120 credits

4 starts per year

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Accelerate your career by making data-driven decisions for your organisation

The online Postgraduate Diploma in AI and Data Analytics is designed for learners who want to build advanced, career-ready skills without committing to the full MSc. The programme extends beyond the foundations in the PGCert, with added further modules and project-based assessments that focus on real-world datasets, modern AI tools, and applied machine learning. Deepen your expertise across the full data and AI lifecycle, from data collection and preparation to analysis, visualisation, intelligent decision-making, and project delivery, and prepare for careers in data analytics, data science, AI, and related fields.

Programme benefits

Curriculum combining data analytics with modern AI and ML
Hands-on learning that prioritises projects over exams
Real-world data from industry-relevant datasets
Career-ready advanced portfolio of projects

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Gain a deep understanding of cutting-edge AI techniques and practical skills

8 courses

120 credits

The 120‑credit curriculum is built around practical, project‑based learning that helps you turn complex data into meaningful, decision‑ready insights. Throughout the programme, you’ll work with real‑world datasets and modern AI tools to build a portfolio that supports advancement into specialised AI and data roles.

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Core Artificial Intelligence and Data Analytics PGDip modules

Research methods 15 Credits

Focusing on the design and evaluation of empirical research, this module introduces students to the methodologies and analytical approaches used to investigate questions in artificial intelligence, data analytics, and technology-driven environments. Students will explore the scientific method, research design, ethical considerations, and critical academic practice, while developing the ability to formulate research questions, collect and analyse evidence, and evaluate findings. Particular attention is given to both hypothesis-driven research and exploratory data analysis, enabling students to select appropriate methods and justify methodological choices in a range of applied contexts. The module prepares students to undertake independent research and communicate evidence-based findings to academic and professional audiences.

Programming and algorithms 15 Credits

Focusing on the principles of programming and algorithmic problem-solving, this module develops students’ ability to design, implement, and evaluate computational solutions. Students will learn how data structures, control flow mechanisms, functions, and object-oriented programming techniques can be used to address complex problems, including search, sorting, and data processing tasks. The module also introduces the theoretical foundations of algorithm design and analysis, enabling students to assess the efficiency and scalability of different approaches. Through the application of software engineering principles, students will develop robust, maintainable, and effective code suitable for real-world computing environments.

Data programming 15 Credits

Developing practical programming skills for language and data analysis, this module introduces students to the tools and techniques required to acquire, process, and explore data using Python. Students will learn how to collect, clean, validate, and manipulate datasets through structured and reproducible workflows, applying programming concepts to real analytical challenges. The module emphasises the use of data structures, libraries, and visualisation techniques to support exploratory analysis and evidence-based reasoning. Through hands-on engagement with data, students will develop the technical competence and critical awareness required to conduct responsible data analysis and prepare for advanced study in machine learning, natural language processing, and computational linguistics.

Artificial intelligence 15 Credits

Examining the latest developments in artificial intelligence, this module explores how modern AI systems are designed, trained, and applied to address complex real-world challenges. Students will investigate contemporary approaches including machine learning, deep learning, generative AI, and intelligent agents, evaluating their capabilities, limitations, and practical applications across a range of domains. The module combines technical understanding with critical reflection, encouraging students to assess the ethical, societal, and organisational implications of AI technologies while developing evidence-based solutions to real-world problems.

Machine learning 15 Credits

Developing the foundations of modern data-driven intelligence, this module introduces students to the core concepts, algorithms, and workflows that underpin machine learning systems. Students will explore supervised and unsupervised learning techniques, learning how different algorithms can be applied to tasks such as classification, regression, clustering, and dimensionality reduction. The module examines approaches to model evaluation, validation, and performance assessment, enabling students to critically interpret results and identify the strengths and limitations of different methods. Alongside technical development, students will consider issues of fairness, bias, interpretability, and responsible AI, developing a critical understanding of the opportunities and challenges associated with deploying machine learning systems in real-world contexts.

Natural language processing 15 Credits

Exploring the computational analysis of human language, this module develops students’ understanding of the methods, techniques, and tools used to process, analyse, and extract meaning from text. Students will investigate the core stages of the natural language processing pipeline, including tokenisation, text normalisation, parsing, named entity recognition, classification, and information extraction. The module examines both rule-based and statistical approaches to language processing, enabling students to evaluate their suitability for different linguistic tasks and applications. Through practical engagement with widely used NLP tools and libraries, students will develop the skills required to analyse language data and implement solutions to real-world language problems. The module also encourages critical reflection on the ethical and societal implications of NLP technologies, including issues of bias, privacy, intellectual property, and responsible innovation.

Data mining 15 Credits

Focusing on the extraction of knowledge from complex and large-scale datasets, this module examines advanced data mining techniques for identifying patterns, anomalies, relationships, and trends in contemporary data environments. Students will explore methods for analysing streaming, graph-based, and spatio-temporal data, developing an understanding of the theoretical, computational, and practical challenges associated with these data types. The module critically evaluates state-of-the-art approaches to knowledge discovery and considers their application in domains characterised by rapidly evolving and highly interconnected data. Through the study of advanced mining algorithms and analytical frameworks, students will develop the expertise required to interpret and model complex data structures.

Big data analytics 15 Credits

Focusing on the design and operation of scalable data systems, this module examines the architectures, tools, and technologies used to support big data processing in enterprise and research environments. Students will explore the characteristics of big data and the challenges they present for conventional data management systems, before investigating distributed computing paradigms such as MapReduce, the Hadoop ecosystem, cloud-based platforms, and NoSQL databases. The module develops practical skills in deploying and managing distributed data environments while critically evaluating alternative technologies for data storage, access, and processing. Through the study of real-world big data solutions, students will develop the knowledge required to support large-scale analytics and data-driven decision-making.

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Career Outlook

The online AI and Data Analytics Postgraduate Diploma prepares you for careers across technology, finance, healthcare, government, business, and other data-driven sectors, with opportunities in data analytics, data science, machine learning, AI consulting, and research.

Explore potential career paths

Senior Data Analyst £74,451 per year¹

Senior Consultant Artificial Intelligence £73,459 per year²

Value of a Diploma (PGDip)

A Postgraduate Diploma (PGDip) is a flexible alternative to a master’s if you want to advance your career without committing to a longer programme. You’ll gain advanced, industry-relevant knowledge in a streamlined format, allowing you to focus on the skills that matter most. You’ll be able to:

Study relevant modules only.
Graduate sooner with a Level 7 qualification.
Reduce your overall tuition costs.
Apply practical skills immediately.
Progress to your MSc when you’re ready.

Programme outcomes

By the end of the Postgraduate Diploma in AI and Data Analytics, you will be able to:

Develop an understanding of the concepts, methods, and tools used in artificial intelligence and data analytics
Apply core AI, machine learning, and data analytics techniques to well-defined, real-world problems
Work with diverse datasets to uncover insights and support data-driven decision-making
Use programming languages, tools, and frameworks to build and adapt data-driven applications
Recognise ethical, privacy, bias, and regulatory considerations when working with AI systems and data
Compare different approaches to AI and data challenges and select effective solutions
Communicate technical findings effectively to both technical and non-technical audiences
Contribute to planning and delivering research and data analytics projects using professional project management approaches

Entry criteria

Application Deadline 05/02/27

Start Date 01/03/27

This programme is designed for curious problem-solvers looking to combine technical expertise with critical thinking to shape the future of artificial intelligence and data analytics.

Online application	Apply Now
Degree	Typically, applicants hold a 2:1 bachelor’s degree (or equivalent) in a STEM or relevant quantitative subject. Applicants with substantial professional experience may also be considered.
Language criteria	If English is not your first language, you’ll usually need an IELTS overall score of 6.5 (with no band below 6.0). In some cases, this may be waived based on prior study, supporting access for a global cohort.

Admissions

Tuition and funding

£9,600 Total cost

120 Total credits

The estimated total cost of this programme is £9,600 GBP for the full PGDip. You can pay in full or instalments of £1,200 for each module. All costs are listed to help you make an informed decision.

Programme fees are reviewed annually and are subject to change each academic year. The fees listed above are for the academic year 2026-27.

Fees and funding

We are excited to be offering discounts. Review our tuition and funding pages for more information.

Funding options

Meet your faculty

Our expert faculty offer extensive knowledge and diverse perspectives, bringing industry insights and real-world expertise directly into the classroom to enhance your learning experience.

Academics with advanced qualifications and specialised expertise
Real-world experience integrated into coursework and discussion
A learning environment that blends academic rigour with structured support
Smaller study groups that enable individual attention and feedback
Ongoing mentorship and support at each stage of your studies at Goldsmiths

Dr Basel Barakat, Programme Convenor, AI and Data Analytics

Dr Basel Barakat

Dr Barakat is a lecturer in Computing at Goldsmiths, specialising in AI, machine learning, IoT systems and real-time communication networks. He holds a PhD in real-time communication systems and works in applied research on sustainable energy, ethical AI, and healthcare forecasting. His recent projects include AI-powered carbon-intensity prediction for electric-vehicle charging and an NHS-funded machine-learning initiative to enhance the prescription process. Dr Barakat has published extensively, and he teaches across AI and data science modules while supervising postgraduate research.

Dr Devakunchari Ramalingam, Programme Convenor, AI and Data Analytics

Dr Devakunchari Ramalingam

Dr Devakunchari is an academic and researcher in AI and data analytics whose work focuses on big data analytics for online social networks. She has extensive experience with leading big data platforms, including Apache Hadoop and Apache Spark, and works with tools such as Apache Flume, Hive, Pig, and NoSQL databases like HBase and MongoDB. Her expertise spans data mining, cloud databases, machine learning, and social network analysis, and she brings this practical, research-led perspective directly into her teaching.

Related programmes

Develop advanced AI and data analytics expertise through hands‑on, project‑based learning that prepares you to solve complex real‑world problems.

Gain foundational knowledge in AI and data analytics, and develop practical skills you can apply immediately in your current role. 

Sources

ERI Economic Research Institute (2026). Data Analyst in the United Kingdom (2026). [online] SalaryExpert. Available at: https://www.salaryexpert.com/salary/job/data-analyst/united-kingdom [Accessed 6 July 2026].
ERI Economic Research Institute (2026). Consultant Artificial Intelligence in the United Kingdom (2026). [online] SalaryExpert. Available at: https://www.salaryexpert.com/salary/job/consultant-artificial-intelligence/united-kingdom [Accessed 6 July 2026].

Artificial Intelligence and Data Analytics PGDip (Online)