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Data Science and Applied Artificial Intelligence MSc (Distance Learning)

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Start Dates

7 September 2026, 18 January 2027, 10 May 2027

Duration

2-3 years part-time


Recent Awards For Excellence

Computer Science & Information Systems - QS 2025
Find out more about these awards
About this course

Overview

Why choose Huddersfield for this course?

  • Develop in-demand skills across three key areas: data science, artificial intelligence and applied computing.
  • No prior computer science background required; you'll be supported in developing skills from beginner level.
  • Taught 100% online, part-time allowing you to study while working.

Accreditation and Professional Links

Recognised connections to give you an extra edge when you graduate. Read More

Our 100% online 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 AI. 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, SAS 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.

Learn more about Distance Learning at Huddersfield.

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 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 with a minimum score of 6.0 in writing and a minimum of 5.5 in any single component or Duolingo English certificate, score 105 or above. 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. The module emphasises extracting actionable insights in data science challenges. 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

Discover what to expect from your tutor contact time, assessment methods, and feedback process.

Technology and System Requirements

As a Distance Learning student, you must provide and have access to certain IT equipment and facilities to access your Virtual Learning Environment (VLE) and to fully participate on your course.

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 our postgraduate students go on to work and/or further study within fifteen months of graduating.

* HESA Graduate Outcomes 2022/23, UK domiciled.

£38,500
Average salary of our postgraduate students fifteen months after graduating.

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

The course content more than met my expectations. I learnt a lot and my job as a Data Analyst Intern went hand in hand with what I was studying.
Read More

- Michelle Joynson
Data Analytics MSc Graduate

How much will it cost?

Fees and Finance

£9,900

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

Modules credits can range from 15 to 60, dependent on the content of the module. Read more about total credits required for a range of degrees, to allow you to calculate the potential total cost.

Please note that tuition fees for subsequent years may rise in line with inflation (RPI-X) and/or Government policy and/or to ensure our distance learning fees are competitive.

Tuition fees will cover the cost of your study at the University. Read more about what is and is not covered by Tuition Fees including compliance for Goods and Services Tax for International Students studying an online course. 

For detailed information please visit www.hud.ac.uk/distance-learning/fees-and-finance/

£9,900

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

Modules credits can range from 15 to 60, dependent on the content of the module. Read more about total credits required for a range of degrees, to allow you to calculate the potential total cost.

Please note that tuition fees for subsequent years may rise in line with inflation (RPI-X) and/or Government policy and/or to ensure our distance learning fees are competitive.

Tuition fees will cover the cost of your study at the University. Read more about what is and is not covered by Tuition Fees including compliance for Goods and Services Tax for International Students studying an online course. 

For detailed information please visit www.hud.ac.uk/distance-learning/fees-and-finance/ 

Flexible payments

Learn how to pay your fees including flexible instalment options

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Funding options

Explore ways to fund your course with exclusive discounts and government loans

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

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Why Hud

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