Machine Learning and Data Mining
Internet of things with data science
School of Science, Engineering and Environment
Two and a half year
In a nutshell
Make discoveries in data and explore the value Internet of Things (IoT) can bring to businesses and organisations. With our Internet of Things with Data Science postgraduate degree, you can enhance your existing computer science knowledge, or gain new skills that can help you secure a role in a fastest-growing employment field in the digital economy.
Available with full and part-time study routes, the course provides you with specialist IoT and data science knowledge to apply in a wide range of sectors, including healthcare, commerce, professional services and the built environment. Start dates are available each January and September.
This course starts in September 2023.
Start your study journey
Register for our next Open Day to learn more about studying Internet of Things with Data Science and speak to the course team
- Explore the full data mining lifecycle, from data pre-processing and exploratory data analysis to the application
- Develop an advanced understanding, and context of IoT, the skills, challenges, System Design and methodologies
- Learn about security vulnerabilities and challenges, including attack surfaces (networking, social engineering) and modern issues (IoT, Cloud).
- Complete high-level academic and practical work developing, evaluating and critically assessing a robust, scalable and usable IoT solution
This is for you if...
You want to enhance your existing skills and qualifications for a career move into Internet of Things application
You want to take your data science skills to the next level, ready to lead IoT projects
You're a knowledge-seeker and want to explore the possibilities that the Internet of Things opens up
All about the course
The course is delivered through a range of highly-focused modules. The 180-credit MSc award comprises four taught modules, plus a research dissertation. The 120-credit PgDip award comprises four taught modules.
Flexibility is at the heart of our learning approach. You can choose to study this postgraduate course full-time or part-time on campus, with start dates in January and September:
- Full-time students will complete taught modules in each of the first two trimesters, and complete the 60-credit dissertation project in trimester three
- Part-time students will complete taught modules in years one and two
The course is designed to upskill and empower graduates and professionals with limited prior subject knowledge of IoT, so they are ready to take up exciting, data-driven career opportunities. Through our industry-focus, we invite speakers from major companies and employers to participate in course delivery, providing real-world case studies and projects.
Course topics include principles and design of IoT systems, advanced databases, big data tools and techniques, security and privacy, machine learning and data mining. We regularly review module content with our industry partners to ensure the knowledge and skill set you will develop reflects trends and needs within professional and business communities. Learn more about the current course modules in the section below.
The Internet of Things with Data Science postgraduate course is delivered by an academic team with extensive industry experience and research connections, and a track record of developing solutions through industry partnerships.
Course leader: Professor Mo Saraee
This module will help you to explore the full data mining lifecycle, from data pre-processing and exploratory data analysis to the application and evaluation of supervised and unsupervised machine learning algorithms.
Through practical, hands-on workshops using Python and Microsoft Azure Machine Learning, you will gain experience at using machine learning and data mining tools and techniques to extract insights from data.
Principles and Design of IoT Systems
This module will provide you with the history, advanced understanding, and context of IoT, the skills, challenges, System Design and methodologies the term implies. You will learn about the architectures to develop an IoT application using wearable sensors. You will experience all the stages in the design and implementation of a complex system, from its specification to the demonstration of a working prototype.
During this module, you will cover aspects of embedded systems programming, sensor data analytics using machine learning methods, user interface design, system integration and testing.
Security and Privacy in IoT
IoT devices and applications present new security vulnerabilities and challenges, and the data they generate has given rise to concerns over personal data and privacy. This industry-aligned module provides specialist cyber security knowledge, and a hands-on ethos, so you will gain the skills and knowledge ready to fight security and privacy issues and challenges in IoT systems.
In this module, you will learn about both attack and defence mechanisms and consider established attack surfaces (networking, social engineering) and modern issues (IoT, Cloud). You will also examine trade-offs between security and availability, and between privacy and audit; study threats to information security, technologies used to detect and combat them, and techniques and tools used to manage and investigate incidents.
The project module aims to provide you with an opportunity to integrate learning from the course modules, working under the direction of an academic supervisor to carry out high-level coordinated academic and practical work on researching a suitable problem and developing, evaluating and critically assessing a robust, scalable and usable solution.
Plus, one module from the following options
This module aims to provide you with a broad overview of the general field of database systems and to develop specialised knowledge in areas that demonstrate the interaction and synergy between ongoing research and practical deployment of this field of study.
Big Data Tools and Techniques
This module introduces you to the tools and techniques to build decision making systems for business organisations. You will learn how to gather large sets of data and information, and produce outputs and reports that enable organisations to make strategic decisions to improve their businesses and predict future trends.
You will learn to critically assess diverse issues regarding the use of statistics in real-world contexts, including:
- how to design, justify, apply and evaluate strategies for using data mining techniques in diverse business contexts
- how to devise strategies for making effective use of analytical software such as SAS Enterprise Miner
Please note that it may not be possible to deliver the full list of options every year as this will depend on factors such as how many students choose a particular option. Exact modules may also vary in order to keep content current. When accepting your offer of a place to study on this programme, you should be aware that not all optional modules will be running each year. Your tutor will be able to advise you as to the available options on or before the start of the programme. Whilst the University tries to ensure that you are able to undertake your preferred options, it cannot guarantee this.
What will I be doing?
Learning is delivered using a range of methods. You are taught through a combination of lectures, seminars, laboratory practicals and project work. Seminars enable you to discuss and develop your understanding of topics covered in lectures in smaller groups.
You will use industry-standard software and have access to computing suites and facilities in our new £65 million hub for engineering and technology.
When not attending lectures, seminars and laboratory or other timetabled sessions, we encourage you to continue learning independently through self-study. Typically, this will involve reading journal articles and books, working on individual and group projects, undertaking research in the library, preparing coursework assignments and presentations, and preparing for examinations.
Your independent learning is supported by a range of excellent facilities, including the library, the learning zone, and our engineering and computer laboratories.
Course projects and assignments apply your learning in authentic problem-solving settings. We will develop your independent learning skills and your ability to make decisions in uncertain situations. Professionalism and ethics are woven throughout, and identifying these issues is an integral part of all assignments and projects.
The final 60-credit project is an opportunity to demonstrate your ability to carry out research in the area of IoT and data science. You will have flexibility in choosing the area of your project to match the areas of the course, or your future career plans.
You will receive feedback on all practice assessments and on formal assessments undertaken by coursework. Feedback on examination performance is available upon request from the module leader. Feedback is intended to help you learn, and we encourage you to discuss it with your module tutor.
School of Science, Engineering and Environment
Rising to the challenge of a changing world, our postgraduate courses are designed to shape the next generation of urbanists, scientists, engineers, consultants and leaders.
Shaped by industry, and delivered by supportive programme teams, you can develop the skills to take your career potential further.
INDUSTRY COLLABORATION AND RESEARCH
When you start this degree with Salford, you are also joining a community making a difference in industry, our local region and in our wider society.
Many of our academics and technicians who support your course also deliver collaborative, interdisciplinary, high-impact work in a range of local and global computing and informatics issues and challenges.
Discover how you are part of something bigger.
What about after uni?
Employment prospects for graduates of the MSc Internet of Things with Data Science are strong, given current and growing levels of demand in fields across health, finance, energy and transport, neuroscience companies and central government.
Demand for data science enabled IoT engineers outstrips supply and there is continued demand for qualified, talented graduates across many global industries. Equipped with this qualification, you will have a skill set and technical knowledge relevant for the IoT and Data Science job market.
Typical roles to consider once you graduate include IoT Product Manager, Supply Chain Transformation Manager, IoT Data Analyst, Big Data Engineer, and Business Transformation Manager.
You might also choose to take your subject interest further with postgraduate research. The Salford Innovation and Research Centre (SIRC) is home to Informatics PhD and Research Master’s opportunities in knowledge discovery and semantic web, software engineering, big data, data mining and analytics, cyber security, information visualisation and virtual environments.
Explore our Doctoral School to learn more about research training, support and opportunities.
What you need to know
This course is ideal for mathematics, computing or science graduates, and experienced professionals, eager to build skills in the growing IoT field.
ENGLISH LANGUAGE REQUIREMENTS
All of our courses are taught and assessed in English. If English is not your first language, you must meet our minimum English language entry requirements. An IELTS score of 6.0 (no element below 5.5) is proof of this, and we also accept a range of equivalent qualifications.
Read more about our English language requirements, including information about pathways that can help you gain entry on to our degree courses.
The minimum requirement is a second class division honours degree or equivalent in any discipline, with previous demonstrable mathematical aptitude e.g. (A-level or BTEC Mathematics).
Accreditation of Prior Learning (APL)
We welcome applications from students who may not have formal/traditional entry criteria but who have relevant experience or the ability to pursue the course successfully.
The Accreditation of Prior Learning (APL) process could help you to make your work and life experience count. The APL process can be used for entry onto courses or to give you exemptions from parts of your course.
Two forms of APL may be used for entry: the Accreditation of Prior Certificated Learning (APCL) or the Accreditation of Prior Experiential Learning (APEL).
|Type of study||Year||Fees|
|Full-time home||2023/24||£9,090per year|
|Full-time international||2023/24||£15,750per year|
|Part-time||2023/24||£1,515 per 30 credits|
Having your own laptop (16GB of RAM and an Ethernet port) is not essential, but it will give you more flexibility in where and how you engage with the software you will need to use during your studies (software is provided as part of the course).
You should consider further costs which may include books, stationery, printing, binding and general subsistence on trips and visits.
International student scholarships
If you are a high-achieving international student, you may be eligible for one of our scholarships. We offer a range of scholarships worth between £3,000-£5,000.
Learn more about our latest international scholarships.
All set? Let's apply
Course starts in September 2023.