Data Science and Applied Artificial Intelligence MSc

Select the year

2026-27

2027-28

Start Dates

21 September 2026, 11 January 2027

Duration

1 year full-time

Recent Awards For Excellence

Computer Science & Information Systems - QS 2025

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About this course

Overview

Our Data Science and Applied Artificial Intelligence MSc is a conversion course designed for those who want to move into one of the most exciting and fast-growing areas of computing, but have no prior knowledge or experience in data science or artificial intelligence. It offers a clear and supportive route into the field, building your knowledge step by step from core concepts through to advanced, applied techniques.

You will learn how to work confidently with data, develop intelligent models and generate insights that support real-world decision making. The course covers key areas including statistical analysis, machine learning, data mining, big data technologies and data visualisation, helping you understand how organisations use data and AI to solve complex problems, improve services and drive innovation. Alongside technical skills and practical programming experience using Python, R, and MATLAB, you will develop the ability to critically assess emerging technologies, recognising where they add value and where their limitations lie in practical contexts.

A major project forms a central part of the course, allowing you to explore a data or AI-focused challenge aligned to your interests or career ambitions. The course also supports progression towards SAS certification, strengthening your professional profile and employability.

Career opportunities after the course *

Data Scientist

Data Engineer

Data Analyst

Machine Learning Engineer

Software Engineer

*Lightcast

Who can apply?

Entry Requirements

Entry requirements for this course are normally:

A BSc, BEng or BA Honours degree (2:2 or above) or equivalent professional qualification in any subject.
Other appropriate professional qualifications and/or experience will be considered on an individual basis.

If your first language is not English, you will need to meet the minimum requirements of an English Language qualification. The minimum for IELTS is 6.0 overall with no element lower than 5.5, or equivalent. Read more about the University’s entry requirements for students outside of the UK on our International Entry Requirements page.

What will you learn?

Course Details

With ever-increasing advancements in Internet-of-Things, Cyber-Physical Systems, and social media applications, vast and complex datasets are generated daily. Visually analysing these datasets enables the transformation of raw data into meaningful insights that support strategic decision-making. The key challenge lies in designing and interacting with effective visual artefacts and dashboards that simplify complex analytical problems without exposing underlying technical complexities. This module provides core knowledge and a deep understanding of the theories, principles, and best practices underpinning data visualisation and business intelligence. You will develop practical skills in designing, developing, and interpreting interactive visualisation dashboards and BI reports using state-of-the-art tools and techniques. You will engage in hands-on exercises addressing real-world big data challenges through visual analysis, focusing on transforming analytical outputs into actionable intelligence. Topics include exploratory data visualisation, visualisation dashboards, interactive 2D and 3D visualisation toolkits, and the application of visualisation methods within specific business and analytical domains.

In this module, you will explore how Artificial Intelligence (AI) is applied across real-world domains to solve complex problems and drive innovation. You will study key areas such as computer vision, natural language processing, large language models, chatbots, autonomous systems, and intelligent decision-making. You will learn how AI technologies are integrated into systems used in industry, public services, and research, while considering their capabilities, limitations, explainability, robustness, and ethical implications. Through practical examples and case studies, you will discover how AI is shaping sectors including healthcare, finance, transport, supply chains, and the creative industries. By the end of this module, you will understand how to connect theoretical concepts of AI with their real-world applications and appreciate the impact of AI on society and the future world of work.

Machine Learning (ML) techniques power many of today’s most transformative technologies, from intelligent assistants and recommender systems to autonomous vehicles and medical diagnostics. This module will introduce you to the key principles and algorithms of ML, both as independent systems and as integral components within broader AI frameworks. You will develop a strong conceptual and practical understanding of how machines can learn from data and experience to recognise patterns, classify information, make predictions, and optimise decisions. You will also explore how to select suitable learning methods for different problem types, analysing the strengths, limitations, and trade-offs between classical and modern approaches. Topics include data-driven learning methods such as tree-based models, ensemble learning, kernel methods, clustering techniques, artificial neural networks and their real-world applications. Through hands-on practical sessions, you will work with widely adopted machine learning tools and frameworks to develop a practical understanding of how core algorithms operate in practice, including data preparation, model training, performance evaluation, and bias analysis.

Data Mining involves using tools, methods, and statistical techniques to extract valuable insights from large datasets. As data volume grows, the potential to uncover meaningful patterns increases. In this module, you'll explore various data mining techniques and tools, focusing on data preparation, exploration, types, modelling, pattern mining, and the social impact of data mining. Essentially, this module emphasises the exploratory and interpretative analysis of complex, real-world datasets, using overlapping techniques for fundamentally different analytical purposes such as pattern discovery, insight generation, and decision support. You'll be expected to develop a deep understanding of real-world applications and research areas, enhancing your ability to contribute to discussions on data mining issues and advancements.

This module explores how statistical methods and predictive models drive intelligent, data-driven decision-making. You will develop a practical understanding of statistical reasoning, hypothesis testing, and uncertainty analysis, and learn how these foundations support real-world data science and AI applications. You will examine how traditional statistical approaches connect with modern predictive analytics, combining techniques from machine learning and data mining to interpret and forecast outcomes from complex datasets. Using industry-standard tools such as SAS, you will gain experience in building and evaluating predictive models across different domains, including business, healthcare, and finance. Case studies and applied projects will help you apply your skills to real data and understand how statistical thinking underpins advanced AI-enabled decision systems.

In this module, you will learn how to design and build scalable data systems that can handle real-world, large-scale datasets. You will explore how big data analytics and modern database technologies work together, compare different data models and architectures, and apply advanced techniques for query processing and optimisation. You will also gain practical experience in creating data pipelines that integrate machine learning, data visualisation, and domain-specific analytics.

This module enables you to independently undertake a project based on a problem of your own choosing. Designed to be integrative, the project serves as a culmination of the knowledge, skills, competencies and experiences you've gained throughout your studies, while also encouraging further development in these areas. As the project is student-led, you will take ownership of the project from start to finish — negotiating agreements, managing communications, and collaborating effectively with all stakeholders at each stage. In addition, the module offers a comprehensive overview of the research process, academic writing, and key professional considerations including legal, ethical and social issues. It also supports your career development by exploring employability pathways and professional growth.

Teaching and Assessment

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Where could this lead you?

Your Career

Graduates of Data Analytics MSc can progress into a range of data-focused and technical roles across sectors including technology, finance, healthcare, retail and government. Many pursue careers as Data Scientists, Data Analysts or Business Analysts, using data-driven insights to support decision-making and strategy. Opportunities also include roles such as Data Engineer or Software Engineer, building and maintaining the systems that enable effective data processing and analysis. These skills are increasingly in demand as organisations look to harness data to drive innovation and performance.

Source: Lightcast data extracted from the Graduate Career Explorer

98%
of the University's postgraduate students go on to work and/or further study within fifteen months of graduating.

* HESA Graduate Outcomes 2022/23, UK domiciled.

£38,500
The average salary of our postgraduates fifteen months after graduating.

* HESA Graduate Outcomes 2022/23, mean salary, UK domiciled, full-time UK employment as main activity.

The learning environment provided me with cutting-edge skills and the confidence to succeed in a competitive job market. After completing my MSc, I secured a role as a Senior Analyst with E.ON UK Plc. My time at Huddersfield was transformative, setting me on a path to a fulfilling career.

- Lucia Nnami
Data Analytics MSc Graduate

How much will it cost?

Fees and Finance

£8,225 per year

This information is for Home students applying to study at the University of Huddersfield in the academic year 2026/27.

Please note that tuition fees for subsequent years may rise in line with inflation (RPI-X) and/or Government policy.

For detailed information please visit https://www.hud.ac.uk/study/fees/

£18,700 per year

This information is for international students applying to study at the University of Huddersfield in the academic year 2026/27.

Please note that tuition fees for subsequent years may rise in line with inflation (RPI-X) and/or Government policy.

For detailed information please visit https://www.hud.ac.uk/international/fees-and-funding/

Scholarships and Bursaries

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What’s included in your fee?

We want you to understand exactly what your fees will cover and what additional costs you may need to budget for when you decide to become a student with us.

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If you have any questions about Fees and Finance, please email the Student Finance Team.

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What does the University offer?

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The Study Mill

A wide range of computing labs

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Why Hud

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Explore Huddersfield

Make Huddersfield your home. A Yorkshire town, surrounded by beautiful countryside, with easy access to neighbouring cities; Leeds, Manchester and Sheffield. You’ll find plenty of places to eat, drink, shop and socialise, there’s ambitious plans for future development. Our campus is situated next to the town centre - making your commute a breeze.

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Get involved in great opportunities helping you to make friends from all across the world and create lifelong memories. Join one of over 100 societies with the Students’ Union, experience new cultures through global events, earn whilst you learn and build your skills via our Global Professional Award.

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Since 2015, we’ve invested £198 million to create spaces where you can learn, collaborate and thrive. We’re continuously investing in our campus with your wellbeing in mind—adding features that make our buildings healthier, more supportive and better places for you to succeed.

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Data Science and Applied Artificial Intelligence MSc

Overview

Why choose Huddersfield for this course?

Accreditation and Professional Links

Entry Requirements