Over the last decade, there has been a huge increase in the amount of data generated from various fields, and the volume, diversity, and complexity of this data continues to increase dramatically. Organisations of all sizes are now facing a key challenge - how to make sense of this data and how to use it to inform business decisions.
The MSc Data Science and Applied Analytics programme aims to develop graduates who understand relevant approaches to designing data science tools, their implementation and evaluation, analytical aspects of big data, and their meaning and importance to both businesses and the public sector.
Develop the deep data skills needed to thrive in a digital economy
A focus on the design and deployment of data science tools
In-depth case studies of data science applications
Develop practical skills for data science through hands-on learning
Complete a research project in a specialist area
It has becoming increasingly important to equip computing students with the deep data skills needed to thrive in a digital economy. Knowledge in data analytics, artificial intelligence, and machine learning are already in demand in organisations around the world and graduates with those skills are leading the way in transforming the way industry and society operates.
Students on this programme can develop an understanding of the design and deployment of data science tools and the core data-related components of computing, analysis, and engineering that enable this. This will involve a mixture of taught content such as programming and data science, in-depth case-studies of data science applications, and technology development such as fast, reliable, and interpretable data analysis and engineering. Additionally, a significant focus will be on developing practical skills for data science through hands-on learning.
This module explores current methodologies in the field of big data analytics and modelling, covering a range of aspects in collecting, transforming, processing, analysing and make inferences out of large amounts of data, which can either be signals or visual data.
The aim is to offer students a deeper understanding and to allow an exposure to the latest developments in big data analytics, equipping them with knowledge in practical depth. The module will also provide training in programming skills (e.g. python), tools and methods (e.g. Apache Spark, Spark Machine/Deep Learning, distributed analytics, etc.) that are necessary for the implementation of big data analytics systems.
The module will also cover applications of big data analytics in various fields, such as Cybersecurity, Internet of Things, and Computer Vision, allowing students the chance to establish a full awareness to the technology advance in this rapidly evolving field.
This module aims to equip students with the essential knowledge required for data analysis in Python programming language. Students can learn both basic programming skills and advanced features such as object-oriented programming and tools/libraries in Python (e.g pandas, matplotlib, numpy, scipy, keras, sklearn) for implementing data analysis tasks. They are also introduced to useful frameworks and best practices, such as virtual environments and version control.
This module introduces cutting-edge topics in data science research areas, including both theory and practical applications. The module will follow a research seminar format, involving input from colleagues across the School of Computer Science and other Schools at Lincoln. Additionally, guest lectures from industry representatives and leading international researchers will be offered. Students will further benefit from opportunities to discuss potential research topics that they can explore to build and enhance their research and critical thinking skills.
This module aims to equip students with knowledge in data engineering, including concepts, ecosystem, and lifecycle. Students can learn about database systems for data storage and processes, and tools used (SQL/streaming SQL for database query, MongoDB, etc) as a data engineer in order to gather, transform, load, process, query, and manage data, so that it can be leveraged by data consumers for operations and decision making.
Data science is frequently applied for analysing structured data modalities, most common of which are image and text data. This module introduces the basic set of tools and techniques used to extract innovative and actionable insights from different data types. Students can learn about the most commonly performed analysis tasks as well as practice performing data analysis on a choice of public and in-house datasets.
This module provides an introduction to current data mining techniques and aims to equip students with knowledge about approaches to a broad range of data analytics situations, preparing them for application in real-world settings, as well as advanced in-depth study in the field of data mining. Students can develop a comprehensive understanding of the field of data mining and its application to real-world problems and data sets. Methodologies discussed include classification and clustering, for a range of modelling and prediction tasks, as well as advanced methods for specialised types of data (e.g. images) and techniques for implementing in the real world (e.g. dimensionality reduction). Lectures are accompanied by practical workshops, where students are given opportunities to manipulate data sets, learn, and demonstrate the concepts and skills conveyed.
This module covers the fundamental skills and background knowledge that students need to undertake a research project, including: surveying literature; selecting and justifying a research topic; planning of research; academic writing, data collection, handling and analysis; and presentation and reporting of research.
This module presents students with the opportunity to carry out a significant inquiry-driven research project, focusing on a topical area of interest that is aligned with their programme of study. This is primarily realised through the development of a dissertation and substantive research and/or software implementation output.
The research project is an individual piece of work, which enables students to apply and integrate elements of study from a range of modules, centred on a specific research question. The student will undertake work that is relevant to the ongoing research in either one of the established research centres within the School of Computer Science or through the development of a project concept in consultation with their allocated academic supervisor.
This module provides a foundation that will prepare students for learning and understanding advanced concepts in data science. Three key areas are introduced and/or reviewed in this module, designed for a potentially diverse cohort of students. A primary tenet of data science centres around the concept of modelling, particularly the use of models to represent and/or predict behaviours and/or responses of natural and artificial systems. Such models typically have a basis in mathematical or statistical constructs, which can be presented in a static (equation-based) or dynamic (simulation-based) context. The syllabus for this module is divided into three topic areas, designed and organised to give students hands-on experience with building models in simulation, and using fundamental mathematical and statistical methods.
This MSc programme is also available with a Professional Practice pathway. Students spend a three to twelve month period undertaking a period of professional practice at the end of first year to gain hands-on experience through a paid work placement. Students will be responsible for sourcing their own paid placements but will be supported by academic staff. Students will be interviewed before being accepted onto the Professional Practice programme to assess their understanding of the work involved and commitment to finding a Professional Practice placement.
† Some courses may offer optional modules. The availability of optional modules may vary from year to year and will be subject to minimum student numbers being achieved. This means that the availability of specific optional modules cannot be guaranteed. Optional module selection may also be affected by staff availability.
We want you to have all the information you need to make an informed decision on where and what you want to study. In addition to the information provided on this course page, our What You Need to Know page offers explanations on key topics including programme validation/revalidation, additional costs, and contact hours.
The programme is assessed through a variety of means, including in-class tests, coursework, presentation, posters, and examinations. The majority of assessments are coursework based, reflecting the practical and applied nature of computer science. The final stage project enables students to further specialise and complete a piece of work of significant complexity.
Postgraduate Application Support
Applying for a postgraduate programme at Lincoln is easy. Find out more about the application process and what you'll need to complete on our How to Apply page. Here, you'll also be able to find out more about the entry requirements we accept and how to contact us for dedicated support during the process.
A minimum UK 2:2 degree or equivalent plus GCSE Maths A*-C, or evidence of a Maths component graded at a minimum of 50% as part of the degree. A 2:2 degree in a STEM subject will also satisfy the Mathematics element.
If you have studied outside of the UK, or are unsure whether your qualification meets the above requirements, please visit our country pages for information on equivalent qualifications.
https://www.lincoln.ac.uk/studywithus/internationalstudents/entryrequirementsandyourcountry/
Overseas students will be required to demonstrate English language proficiency equivalent to IELTS 6.0 overall, with a minimum of 5.5 in each element. For information regarding other English language qualifications we accept, please visit the English Requirements page.
If you do not meet the above IELTS requirements, you may be able to take part in one of our Pre-session English and Academic Study Skills courses. These specialist courses are designed to help students meet the English language requirements for their intended programme of study.
You will need to have funding in place for your studies before you arrive at the University. Our fees vary depending on the course, mode of study, and whether you are a UK or international student. You can view the breakdown of fees for this programme below.
The University offers a range of merit-based, subject-specific, and country-focused scholarships for UK and international students. To help support students from outside of the UK, we offer a number of international scholarships which range from £1,000 up to the value of 50 per cent of tuition fees. For full details and information about eligibility, visit our scholarships and bursaries pages.
Postgraduate Funding Options
Find out more about the optional available to support your postgraduate study, from Master's Loans to scholarship opportunities. You can also find out more about how to pay your fees and access support from our helpful advisors.
This Master’s programme aims to develop graduates who understand relevant approaches to designing data science tools, their implementation and evaluation, analytical aspects of big data, and their meaning and importance to both businesses and the public sector. Graduates may pursue roles in organisations across these sectors, while some may choose to continue their studies at doctoral level.
For more information about this course, please contact the Programme Leader.
Dr Miao Yu
myu@lincoln.ac.uk
To get a real feel for what it is like to study at the University of Lincoln, we hold a number of dedicated postgraduate events and activities throughout the year for you to take part in.
