Data Science and Applied Analytics | MSc

Welcome to MSc Data Science and Applied Analytics

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.

How You Study

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.

How you are assessed

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.

The University of Lincoln's policy on assessment feedback aims to ensure that academics will return in-course assessments to students promptly usually within 15 working days of the submission date.

An Introduction to Your Modules

† 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.

Big Data Analytics and Modelling 2022-23CMP9781MLevel 72022-23This 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.CoreData Programming in Python 2022-23CMP9065MLevel 72022-23This 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.CoreFrontiers of Data Science Research 2022-23 CMP9066MLevel 72022-23This module provides an introduction to cutting-edge topics in data science-related research topics, including both theory and practical applications. The module is designed to follow a research seminar format, involving input from colleagues across the corresponding research groups at Lincoln, alongside guest lectures from industry representatives and leading international researchers. Students can further benefit from opportunities to discuss possible research topics with potential project supervisors and clients, and production of a research proposal, presentation, and literature review leading into the Research Project.CoreFundamentals of Data Engineering 2022-23CMP9067MLevel 72022-23This 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.CoreImage and Text Processing for Data Science 2022-23AGR9012MLevel 72022-23Data 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.CoreIntroduction to Data Mining 2022-23AGR9013MLevel 72022-23This 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.CoreResearch Methods (LIAT) 2022-23AGR9014MLevel 72022-23This 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.CoreResearch Project 2022-23CMP9140MLevel 72022-23This module gives students with the opportunity to carry out a significant project, focusing on an area of particular personal and professional interest, through the development of a dissertation and substantive software implementation. The research project is an individual piece of work, which gives students the chance to apply and integrate elements of study from a range of modules, centred on a specific research question. Students are expected to undertake work that is relevant to the ongoing research in one of the established research centres within the Lincoln School of Computer Science and will work closely under the supervision of a member of that research centre. Students are required to undertake the development of a software artefact that is non-trivial in scale and goals, and is supported by best-practice application of appropriate theoretical frameworks.CoreSimulation, Mathematical and Statistical Modelling 2022-23AGR9015MLevel 72022-23This 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.CoreProfessional Practice 2023-24CMP9793MLevel 72023-24Optional

Fees and Funding

For eligible students, there are more ways than ever before to fund your postgraduate study, whether you want to do a taught or research course. For those wishing to undertake a Master's course, you can apply for a loan as a contribution towards the course and living costs. Loans are also available to those who wish to undertake doctoral study. The University offers a number of scholarships and funded studentships for those interested in postgraduate study. Learn how Master's and PhD loans, scholarships, and studentships can help you fund your studies on our Postgraduate Fees and Funding pages.

Programme Fees

International Postgraduate Taught Application Deadline

Please note that international applications for taught postgraduate programmes starting in September 2022 have now closed.

Entry Requirements 2022-23

Students should hold a suitable undergraduate degree with a 2:2 classification or higher.

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/home/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.

https://www.lincoln.ac.uk/home/studywithus/internationalstudents/englishlanguagerequirementsandsupport/englishlanguagerequirements/

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.

https://www.lincoln.ac.uk/home/studywithus/internationalstudents/englishlanguagerequirementsandsupport/pre-sessionalenglishandacademicstudyskills/

Career Opportunities

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.

Postgraduate Events

Find out more about how postgraduate study can help further your career, develop your knowledge, or even prepare you to start your own business at one of our postgraduate events.

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Key Information

MSc Data Science and Applied Analytics