Artificial Intelligence and Data Science MSc



Learn to drive real, impactful solutions to AI concepts across diverse industries with this hands-on course

Overview

The MSc Artificial Intelligence and Data Science at Edinburgh Napier University is distinctively practical, setting it apart from the more theory-driven programmes offered by other institutions. This course equips professionals with hands-on, industry-relevant skills in data engineering and AI, ensuring you can immediately apply your knowledge to real-world challenges. Instead of focusing solely on theory, the programme emphasises practical applications, such as big data systems, machine learning solutions and cutting-edge tasks, which are crucial for implementing AI effectively in any organisation. 

Through our strong research and knowledge-transfer partnerships with industry, you will tackle real AI problems directly related to your work environments, enhancing productivity and innovation from day one. This applied approach, combined with a commitment to ethical AI, ensures that graduates don’t just understand AI concepts but can use them to drive impactful, responsible solutions within diverse sectors, making this course uniquely career-focused and practically valuable. 

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Mode of Study:

Full-time

 

Duration:

1 years

Start date:

JanSep

Course details

Students choosing Edinburgh Napier's MSc Artificial Intelligence and Data Science gain critical skills to excel in data-driven careers across diverse industries. The course offers hands-on training in data analytics, machine learning, and visualisation, preparing you to tackle complex data challenges with confidence. For those aiming to transition into data science roles or advance in their current careers, the course provides a strong foundation in both theoretical and practical aspects. Industry partnerships, real-world projects, and flexible study options (full-time, part-time, and distance learning) equip students with adaptable, job-ready expertise that aligns with current and future career demands in a data-centric world. 

You will gain the skills to analyse complex datasets, build predictive models, and derive actionable insights to support decision-making in various sectors like healthcare, finance, business, and technology.

You will learn to manage large data systems, use advanced machine learning algorithms, and develop data visualisation tools that communicate insights effectively. The course also emphasises data ethics and security, essential for navigating today’s regulatory landscape. With a focus on practical, hands-on experience through industry-aligned projects, you will be prepared for roles such as data scientist, data analyst, machine learning engineer, and data consultant. 

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    How you’ll be taught

    This programme provides a well-rounded learning experience through a combination of lectures, practical labs, group discussions, supervision, and workshops.

    Lectures cover foundational and advanced topics in data science, delivering essential theoretical knowledge.

    Practical labs offer students the opportunity to apply this knowledge in real-world scenarios, using industry-standard tools and technologies to build their practical skills.

    Group discussions encourage collaborative learning, allowing students to exchange ideas and perspectives on complex data science challenges.

    Students receive personalised support through supervision, guiding them in both academic and research tasks.

    Additionally, interactive workshops delve into specialised areas like data analytics, machine learning, and data visualisation, providing hands-on skill development.

    This diverse teaching approach equips students with both the theoretical understanding and practical experience needed for successful careers in data science. 
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    Assessments

    Assignments in the MSc Artificial Intelligence and Data Science programme are designed to reflect the types of challenges data scientists face in the industry, combining technical and analytical skills. Examples include: 

    • Data Analysis Projects: Students may be assigned large datasets to clean, analyse, and interpret, drawing insights that address specific business or research questions. These projects test data preprocessing and exploratory data analysis skills. 
    • Machine Learning Modelling: Students work on developing, training, and evaluating machine learning models, such as regression or classification models, using real-world data. They may compare model performance, tune parameters, and interpret model outputs to optimise results.
    • Data Visualisation and Reporting: Assignments often involve creating visualisations that effectively communicate data findings to stakeholders, assessing students’ ability to translate complex data into clear, actionable insights.
    • Case Studies: Based on industry scenarios, case studies challenge students to propose data-driven solutions, integrate analytics within business contexts, and maybe consider ethical implications. 
    • Dissertation Project: The final assignment is an in-depth research project where students select a data science problem of their choice, conduct independent research, apply advanced methodologies, and document their work in a comprehensive report, showcasing their full range of skills. 

    These assignments help students build a portfolio of work that demonstrates their expertise to potential employers.

     
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    Facilities

    The programme incorporates several practical elements designed to build students' hands-on skills and prepare them for real-world data science roles. Practical labs are a core component, where students work with industry-standard tools (e.g. OpenRefine, Weka and SPSS) and programming languages (like Python) to analyse data, develop machine learning models, and create visualisations. These labs allow students to directly apply theoretical concepts from lectures in a controlled, supportive environment. 

    Students in the MSc Artificial Intelligence and Data Science programme benefit from a variety of specialised facilities that enhance their learning experience.  Computer labs offer modern software and hardware resources essential for tasks in data processing, statistical analysis, machine learning, and data visualization. Students might also work in the Robot Lab, where they can explore data science applications within robotics. The library resources provide a comprehensive collection of relevant journals, books, and databases, supporting their studies with up-to-date academic and industry information. These facilities provide students with both theoretical knowledge and practical experience, ensuring they’re well-prepared for data-driven careers. 

     

Modules

Modules that you will study* as part of this course

Artificial Intelligence ( SET11125 )

The module is partly based on the first three sections of “Artificial Intelligence: A Modern Approach” (3rd edition) by Russell and Norvig. The indicative content from the book is as follows: Introduction: What is AI? History of AI and the state of the art. Agents : An introduction to agents, their behaviors and structure Searching: Problem solving by searching, heuristics, local search and optimisation and adversarial search Constraint satisfaction problems: defining and solving CSPs Logic: Propositional logic, first-order logic, knowledge representation In addition to this the module will feature introductions to other AI techniques including neural networks, machine learning and nature inspired methods.

Further information

Business Intelligence and Reporting for Enterprises ( CSI11100 )

The module aims to present a balanced approach to the subject area by addressing both the theory and practice of Business Intelligence and reporting in enterprise systems. The aim of this module is to develop a deep understanding of enterprise information systems and their role in business processes alongside practical skills in business intelligence reporting and dashboard design. It includes:- Evaluation of the impact that Enterprise Information Systems can have within organisation business processes. - As an assessment of the impact that they have on the efficiency and effectiveness of organisations and supply chains. - Fundamentals of business intelligence, such as data warehousing and data mining. - The role of performance dashboards in performance management and measurement in organisations. - The sourcing and extraction of data and the analysis and development of intelligence. - Understanding of current legislation on data privacy and ethical issues relevant to business intelligence in organisations.The module will use a range of tools to give students an appreciation of the nature of large scale integrated information systems and to practice data analytical techniques.

Further information

Computational Intelligence ( SET11127 )

In this module you will:- develop an awareness of the use of modern computational intelligence techniques;- develop an understanding of a number of techniques such as evolutionary algorithms, neural networks, co-evolution, classifier systems and fuzzy logic;- gain practical experience of implementing and evaluating computational intelligence algorithms.

Further information

Data Analytics ( SET11122 )

The aim of this module is to enable you to develop a deep understanding of the fundamentals of data analytics, and to give you opportunities to practise a set of popular data analytical tools. Topics covered include:*Data Pre-processing – data quality, data cleaning, data preparation*Data Analytics – techniques of analysing data, such as classification, association, clustering and visualisation, including a variety of machine learning methods that are widely used in data mining* Post processing – data visualisation, interpretation, evaluationThis module will use tools such as OpenRefine, Weka and Tableau for standard and structured data The Benchmark Statement for Computing specifies the range of skills and knowledge that should be incorporated in computing courses. This module encompasses cognitive skills in Computational Thinking, Modelling and Methods and Tools and practical skills in deployment and use of tools and critical evaluation in addition to providing useful generic skills for employment.

Further information

Data Management & Processing ( SET11126 )

This module will explore and develop data management and processing solutions that will work on dirty, complex, real-world data. This module will examine the key concepts of data warehousing, data cleaning, and data processing in the context of business requirements and focus on how to combine these steps into a coherent data processing pipeline.First, modern tools and techniques in data management will be examined, with the emphasis on good practice and professional approaches of storing and handling data. Next, the module will examine ways of cleaning noisy real-world data in order to make it suitable for data processing. Finally, data processing and collation techniques such as Machine or Deep Learning will be applied to the data to extract structure and elicit comprehension of the data. Throughout the module, advantages and disadvantages of using local and cloud approaches will be explored, alongside discussing common parallel approaches to facilitate faster solutions.In short, the goal of this module is to allow students to understand a data processing pipeline from raw data to final delivery. It will cover:• Data warehousing and storage techniques• Data cleaning techniques• A discussion of cloud approaches• Data processing and collation techniques• An introduction to parallel data pipeline approaches

Further information

Masters Dissertation ( SOC11101 )

The work for this module comprises the completion of an individual research project. Each student is assigned a personal Supervisor, and an Internal Examiner who monitors progress and feedback, inputs advice, examines the dissertation and takes the lead at the viva. There are two preliminary deliverables prior to the submission of the final dissertation: (1) Project proposal (2) Initial Report including time plan and dissertation outline

Further information

Scripting for Natural Language Processing ( SET11128 )

Conversational AI has attracted increasing attention in academia and industry in the past five years. There have been over 100 companies working on voice-AI or Conversational AI-based products, including the world’s most valuable brands (e.g. Apple, Google, Amazon, Huawei, etc.) and start-ups (e.g. Emotech, Alana). On the other hand, there are global competitions organised by Amazon, which focus on state-of-the-art technologies in diverse conversational AI tasks, e.g. social conversation and task-oriented conversation. This module can help students learn about conversational AI, related NLP and ML/DL methodologies, and state-of-the-art research topics. It also can provide students with the experience of team-level project work on realistic human daily problems, which offers a good insight into academic and industrial perspectives. The module covers the following topics: • Data types and formats: numerical and time series, graph, textual, unstructured, • Data sources and interfaces: open data, APIs, social media, web-based • NoSQL databases such as document (MongoDB), graph and key-value pair • Techniques for dealing with large data sets, including Map Reduce • Developing Data-Driven Applications in Python The Benchmark Statement for Computing specifies the range of skills and knowledge that should be incorporated in computing courses. This module encompasses cognitive skills in Computational Thinking, Modelling and Methods and Tools, Requirements Analysis and practical skills in specification, development and testing and the deployment and use of tools and critical evaluation, and providing useful generic skills for employment.

Further information

* These are indicative only and reflect the course structure in the current academic year. Some changes may occur between now and the time that you study.

Disclaimer

Study modules mentioned above are indicative only. Some changes may occur between now and the time that you study.

Full information is available in our disclaimer.

Entry requirements

The entry requirement for this course is a Bachelor (Honours) Degree at a 2:2 or above in any discipline. However, 30% of the modules studied in the degree must be Computing related. If you do not have 30% computing modules studied in the degree but have a minimum of 2 years programming/computing related work experience, you may still be considered.

Alternatively, other qualifications or experience demonstrating through our recognition of prior learning processes that you have appropriate knowledge and skills at SCQF level 10 may be considered.

We may also consider lesser qualifications if you have sufficient professional work experience within a relevant industry. There will also be an interview for this course if necessary, undertaken by the programme leader, to assess suitability of applicants from a non-standard background.

Recognised Prior Learning

Your application will be considered on an individual basis, taking into consideration your previous study and experience. If you can demonstrate that you have appropriate knowledge and skills at SCQF level 10 you may be able to enter this programme. You will be asked to reflect on your previous experience and demonstrate skills, knowledge and understanding equivalent to that achieved on successful completion of a bachelor’s degree with honours. We will provide you with support in your reflective process through interviews and guidance on presenting your evidence.

 

If your first language isn't English, you'll normally need to undertake an approved English language test and our minimum English language requirements will apply.

This may not apply if you have completed all your school qualifications in English, or your undergraduate degree was taught and examined in English (within two years of starting your postgraduate course). Check our country pages to find out if this applies to you.

We welcome applications from students studying a wide range of international qualifications.
Entry requirements by country

Please note that international students are unable to enrol onto the following courses:
  • BM Midwifery/MM Midwifery
  • MSc Nursing courses
  • All Graduate Apprenticeship courses

See who can apply for more information on Graduate Apprenticeship courses.

We’re committed to admitting students who have the potential to succeed and benefit from our programmes of study. 

Our admissions policies will help you understand our admissions procedures, and how we use the information you provide us in your application to inform the decisions we make.

Undergraduate admissions policies
Postgraduate admissions policies

Fees & funding

The course fees you'll pay and the funding available to you will depend on a number of factors including your nationality, location, personal circumstances and the course you are studying. We also have a number of bursaries and scholarships available to our students.

Tuition fees
Students from 2025/26 2026/27
Scotland, England, Wales, Northern Ireland, and Republic of Ireland £7,650 £tba
Overseas and EU £21,430 £tba
Tuition fees are subject to an annual review and may increase from one year to the next. For more information on this and other tuition fee matters, please see our Fees and Funding links above.
The University offers a 20% discount on Postgraduate Taught Masters programmes to its alumni. The discount applies to all full-time, part-time and online programmes. The discount can only be applied to year one of a full-time Postgraduate degree, any additional years are exempt from the discount. For part time Postgraduate degrees the discount will apply to years one, two and three only and any additional years will be exempt from the discount. Please read our full T&C here
Please note that the tuition fees liable to be paid by EU nationals commencing their studies from 1 August 2021 will be the Overseas fee rate. The University offers a range of attractive Tuition Fee bursaries to students resident in specific countries. More information on these can be found here.


Careers

The programme equips graduates with versatile skills, opening doors to diverse career paths across various industries. Graduates may pursue roles such as Data Scientist, analysing data to uncover insights; Machine Learning Engineer, building predictive models; or Data Analyst, creating reports to support business decisions. Other options include Data Engineer, maintaining data pipelines; Business Intelligence Analyst, providing strategic insights; and Quantitative Analyst, applying statistical techniques in finance. Additional roles include AI Researcher, Big Data Engineer, and Data Consultant, offering specialized expertise to drive data-driven decisions. With applications in technology, healthcare, finance, and beyond, these roles position graduates for impactful careers in a data-driven world. 

 
Merchiston campus