MSc Data Science

Previous undergraduate study: 2:2 honours degree and above or equivalent – from a recognised institution.

Other qualifications: overseas qualifications may be accepted, and will be subject to evidence of equivalency normally verified through ECCTIS (UK ENIC).

Work experience and professional qualifications: if you haven't studied at undergraduate level, we welcome applications from anyone who can demonstrate relevant professional experience. In place of work experience, you may be invited to interview or asked to submit a piece of written work to support your application.

Recognition of prior learning: are you thinking of transferring institutions, or have you studied before? Our recognition of prior learning process unlocks the value of your past learning and experience.

If you’re a non-native English speaker, you’ll be asked to provide proof of your English language proficiency in one of the following forms:

IELTS: level 6.5 or above with no element below 6.0 (an online English test is offered (SPEEXX) if IELTS not available).

TOEFL iBT®: a score of 88 overall (with 18 in reading, 19 in listening, 19 in speaking and 23 in writing).

Trinity College London Integrated Skills in English (ISE): ISE II with distinction in each skill (please note that Trinity College ISE academic results are only valid for two years).

Duolingo English test: a score of 110 overall, with 100 in each component.

Cambridge Certificate of Proficiency or Cambridge Certificate of Advanced English: a score of 176 overall, with 169 in each component.

Alternatively, you may be accepted if you have previously studied in English at an appropriate level and attended a recognised institution.

You may be eligible for a government master's loan.

As an LIBF student, you’ll have the opportunity to apply for an LIBF Scholarship. The LIBF Scholarship is an award of £1,200 towards your tuition fees, spread over the duration of your course and reducing your total fees. You are eligible to apply if your household income is less than £3,500 per month.

You can get up to 5%* off your total fees if you pay in advance (*reduction depends on study duration).

Module overview:

Enhance your Python knowledge from essential syntax to advanced object-oriented programming and error handling. Discover crucial Python libraries and concepts such as version control in detail.

Gained skills:

You'll master Python for diverse applications, from web development to data analytics and scientific computing. Develop proficiency in writing clean, efficient, and adaptable code.

Real-world application:

Leverage Python to automate data processing workflows and build sophisticated data analysis tools. For example, develop a predictive model for market trends using Python libraries, or create a data pipeline to streamline a retail company’s inventory management.

Assessment:

You'll complete a 3,000-word written assignment for the final assessment.

Module overview:

Strengthen your foundational understanding of differentiation, integration, and vector calculus, essential for developing advanced algorithms. Dive into tensors and matrix equations to support data-intensive computations.

Gained skills:

Apply rigorous mathematical techniques to analyse data sets and build predictive models. Learn how to deal with complex mathematical challenges in data and computer science projects.

Real-world application:

Use advanced mathematical skills to develop and refine algorithms for data-processing tasks in finance, such as risk assessment models, or optimise algorithms for engineering simulations. Apply these techniques to enhance computational models for large-scale analysis projects.

Assessment:

You'll take a 90-minute exam consisting of both closed (e.g., multiple choice) and open questions.

Module overview:

Explore the fundamental building blocks of statistics, including probability distributions, Bayesian approaches, and hypothesis testing. Learn how these statistical models underpin data science for effective analysis, data visualisation, and data-driven decision-making.

Gained skills:

Master statistical computing techniques for thorough data analysis, from evaluating stochastic data to summarising and visualising complex datasets. Apply advanced statistical methods to build and optimise predictive models.

Real-world application:

Enable precise data-driven decision-making in various fields, such as optimising clinical trials in healthcare or improving economic forecasts. For instance, Bayesian statistics can be applied to assess medical research data accurately or enhance marketing strategies using robust statistical software.

Assessment:

You'll complete an advanced workbook consisting of six assignments, each 150-300 words.

Module overview:

Dive deep into machine learning, covering supervised, unsupervised, and reinforcement learning methods and how they apply to data. Learn to evaluate and apply the best learning models for different data-centric tasks.

Gained skills:

Gain the expertise to build, fine-tune, and assess machine learning models tailored to data science applications. Understand the intricacies and trade-offs of different learning approaches.

Real-world application:

Implement machine learning solutions across diverse industries, from developing personalised recommendation systems for e-commerce to creating efficient fraud detection algorithms for financial services. Enhance predictive maintenance systems in manufacturing by applying advanced models.

Assessment:

You'll take a 90-minute exam comprising closed and open questions.

Module overview:

Develop your research skills with a focus on methodologies, case studies, and data collection strategies critical for data science research. Learn to conduct high-quality evaluation research and craft informed research proposals.

Gained skills:

Enhance your capacity to design, collect, analyse, and interpret qualitative and quantitative data. Improve your skill in evaluating research quality and applying appropriate scientific methods to complex data problems.

Real-world application:

Conduct insightful and actionable research on data to improve business performance and strategy. For example, advanced research methods can be used to guide data-driven product development, or empirical methods can be applied to assess organisational data strategies.

Assessment:

You'll complete a 3,000-word written assignment.

Module overview:

Explore neural networks and deep learning techniques fundamental to advanced data science, understanding their components and applications. Learn to design, train, and optimise deep learning models for various scenarios.

Gained skills:

Develop a deep understanding of neural network architectures and training methods. Gain the ability to create and implement advanced deep learning models effectively.

Real-world application:

Apply deep learning to innovative fields such as computer vision, natural language processing, and autonomous systems. For example, develop a facial recognition system to enhance security protocols or build a real-time language translation application.

Assessment:

You'll complete a 3,000-word written assignment.

Module overview:

Learn to define and evaluate use cases, setting up metrics and KPIs central to the success of any project. Understand how to monitor project status and avoid common pitfalls in data science assessments.

Gained skills:

Develop the ability to set up clear, data-driven project objectives and define success metrics. Create effective tools for monitoring and evaluating key projects.

Real-world application:

Enhance project outcomes in various industries by establishing clear goals and monitoring progress accurately using data metrics. For example, track the milestones and outcomes of a project to ensure it meets company goals, such as improving customer satisfaction through targeted marketing campaigns.

Assessment:

You'll complete a 3,000-word written assignment.

Module overview:

Understand and apply data science methodologies, focusing on data quality, feature engineering, and predictive model evaluation. Learn to create new features and assess models using advanced data techniques.

Gained skills:

Gain proficiency in feature engineering and model evaluation. Develop advanced skills in dealing with data quality and the practicalities of predictive modelling and feature selection techniques.

Real-world application:

Improve the reliability and accuracy of predictive models in sectors such as healthcare, where precise diagnostic systems are crucial, or in marketing, where accurate customer segmentation can drive targeted campaigns. Deploy sophisticated data solutions for enhancing business strategies and operational efficiency.

Assessment:

You'll complete a 3,000-word case study.

Module overview:

Apply your acquired data science knowledge to a substantial research project, exploring a topic relevant to your specialisation. Develop and defend a thesis, demonstrating your expertise and research capabilities.

Gained skills:

Enhance your ability to conduct independent research in, critically analyse existing literature, and develop innovative solutions. Strengthen your problem-solving and academic writing skills specific to the field of data science.

Real-world application:

Contribute innovative insights and knowledge to the field and showcase your ability to tackle complex scenarios independently. For example, research innovative data analysis methods to improve patient outcomes in healthcare or optimise logistics processes in supply chain management through data science techniques.

Assessment:

You'll complete a 15,000-word master thesis.

Module overview:

Dive into big data fundamentals, exploring various data types, sources, and advanced storage frameworks essential in data science. Understand distributed computing environments and the ethical impacts of big data.

Gained skills:

Handle large datasets efficiently and establish robust database structures tailored to data science projects. Design and implement distributed computing solutions for big data analytics.

Real-world application:

Use big data management to transform businesses by employing enhanced data analytics or developing real-time processing systems. For example, implement a distributed database system to improve data access and analysis for a large organisation, enhancing decision-making capabilities.

Assessment:

You'll deliver a 7-10 minute presentation and write a 1,500-word reflection paper.

Module overview:

Explore agile methodologies like Scrum and Kanban, as well as DevOps practices crucial for integrated software tools and data operations. Learn to develop high-quality code and create effective APIs tailored for data-intensive projects.

Gained skills:

Master agile project management and software development techniques specific to data-intensive environments. Gain expertise in API design and overcoming challenges in deploying data-heavy models to production.

Real-world application:

Enhance the efficiency and scalability of data-driven software tools across industries. For example, create a robust API for a data analytics platform to integrate seamlessly with other systems or develop continuous delivery pipelines for deploying data-focused software updates.

Assessment:

You'll deliver a 7-10 minute presentation and write a 1,500-word reflection paper.

Module overview:

Delve into the principles of industrial automation, focusing on classic and Industry 4.0 approaches relevant to data science. Understand modelling techniques and supervisory control for flexible, data-driven production environments.

Gained skills:

Acquire the ability to design and implement automation systems using data science methods. Develop advanced skills in system performance analysis and applying data insights to supervisory control.

Real-world application:

Enhance manufacturing processes through data-driven automation technologies, maximising efficiency and productivity. For example, design automated quality control systems that use machine learning to detect product defects in real-time or develop adaptive production systems that adjust based on data feedback.

Assessment:

You'll complete an advanced workbook consisting of six assignments, each 150-300 words.

Module overview:

Study the components, sensors, and motion control systems of self-driving cars, focusing on data science applications. Learn about communication protocols and the social impact of autonomous vehicles.

Gained skills:

Gain proficiency in sensor technologies and motion control, which are essential for autonomous vehicles in data science. Understand and evaluate the ethical and social implications of self-driving technology from a data perspective.

Real-world application:

Contribute to the automotive industry by developing data-driven technologies for autonomous vehicles, enhancing safety and efficiency. For example, design a sensor fusion system that analyses data from multiple sources to improve environmental awareness and decision-making in self-driving cars, or implement algorithms that use real-time data for autonomous vehicle navigation.

Assessment:

You'll take a 90-minute exam comprising closed and open questions.

*You'll be responsible for arranging a suitable internship for your field of study. Once arranged, please complete an application to the internship module via myCampus, to make sure your internship can be recognised and credited towards your course requirements.

Module overview:

Apply your data science skills in a professional environment through a self-arranged internship. Prepare with tutorials on professional expectations and goals pertinent to data science roles.

Gained skills:

Develop practical data science experience and knowledge in a real-world setting. Enhance your problem-solving, communication, and management skills within the context of data science projects.

Real-world application:

Gain valuable industry experience that improves your employability and practical understanding of data science. For example, work with a tech startup to apply your data science skills to solve intricate business challenges or join a research lab to contribute to cutting-edge projects and innovations in data science.

Assessment:

You'll complete a 3,000-word internship reflection paper (IRP).

A Masters in Data Science is worth it, given the growing demand for data specialists across various industries. The masters degree equips you with advanced skills in machine learning, statistical analysis, and model engineering, highly sought after in today’s job market. Graduates often enjoy lucrative career opportunities and the flexibility to work in diverse fields such as technology, finance, healthcare, and consulting.

With an MSc in Data Science, you can pursue a wide range of career paths, including roles such as Data Scientist, Business Intelligence Analyst, Deep Learning Engineer, Data Analyst, and Data Engineer. These positions are in high demand across various industries like technology, finance, healthcare, retail, and consulting.

An MSc Data Science degree in the UK typically ranges from 1 to 2 years, depending on whether you study full-time or part-time. At LIBF, our programme offers flexible online learning, allowing you to complete your degree at your own pace. Our part-time students pay the same total cost as full-time students, spread over their chosen study duration, with no extra fees.

The highest salaries for MSc Data Science graduates can vary significantly based on your work experience, where you live, and the industry you work in. According to Prospects, salaries can range from £25,00 for junior data scientists to upwards of £60,000 and, in some cases, as high as £100,000. This is backed up by uk.indeed.com, where the average salary for a Senior Data Science Manager in the UK is £72,132 (correct as of August 2024).

An MSc Data Management degree emphasises the strategic and operational aspects of managing data resources, including data mining methods, governance, architecture, and cyber security. Essentially, Data Science centres on extracting value from data, while the postgraduate study of Data Management focuses on efficiently administrating and overseeing data systems.

Prospective students studying an MSc Computer Science degree can expect to study a range of topics alongside data, including software development, algorithms, and computer systems, providing a comprehensive understanding of computing principles. Essentially, Data Science is specialised in data analysis and interpretation, while Computer Science encompasses the broader field of computer programming and software engineering.

A Masters in AI specialises in creating algorithms and systems that enable machines to perform tasks requiring human intelligence, like natural language processing and computer vision. In contrast, a Masters in Data Science covers a broader scope, including data mining, machine learning, and data visualisation, making it versatile for multiple domains. While AI focuses on the technologies behind intelligent systems, Data Science incorporates those technologies alongside statistical methods and analytical techniques.

A Statistics Masters focuses on theoretical and applied statistical methods to analyse data, often geared towards research and academic applications. While both fields involve data analysis, MSc Data Science incorporates a broader range of techniques for predictive modelling and decision-making.

A Data Analytics Masters is more centred on interpreting data to make strategic business decisions, often involving immediate and practical applications. Data Science equips you with skills for both data interpretation and predictive analytics, making you adept at handling a wide range of data-centric tasks.