BSc Data Science

Previous undergraduate study: 2 subjects at A level or equivalent and GCSE Maths and English, Grade C or Grade 4 or above. 

Other qualifications: we also consider a variety of qualifications, including A levels with EPQ, International Baccalaureate, BTECs, T-levels, Cambridge Pre-U subjects, Irish Leaving Certificate, Scottish Highers, Welsh Baccalaureate and other international qualifications. Overseas qualifications may be accepted and subject to evidence of equivalency typically verified through ECCTIS (UK ENIC).

Contextual offers: we strive to support you in succeeding, whatever your background, and we encourage you to apply to study with us even if you don’t meet the academic requirements. We view all applications holistically and will consider your CV, work experience, background, and other experience when assessing your ability to succeed in your chosen undergraduate degree courses or programme.

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

Mature students: you may be eligible to enrol through our mature student process, which requires you to submit a CV and a short supporting statement of 500 to 600 words. This should explain why you want to further study in this programme and how it will support your career plans.

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 6.0 or above with no element below 5.5 (an online English test is offered (SPEEXX) if IELTS is unavailable).

TOEFL iBT®: a minimum score of 80 overall.

Trinity College London Integrated Skills in English (ISE): ISE III at minimum pass (please note that Trinity College ISE academic results are only valid for two years).

Duolingo English test: minimum 95 points.

Cambridge Certificate: minimum B g

You may be eligible for a government student 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.

If you experience financial difficulty as an undergraduate student, you can apply for support from our Hardship Fund. Hardship funds are made available to UK students twice in the academic year. You’ll be informed when applications are open, and the Scholarship, Bursaries, and Prizes Committee will allocate funds.

You can get 2% off your total fees if you pay annually in advance or 10% off if you pay upfront in full for an undergraduate course.

Module overview:

This module introduces the basics of data science, covering its multidisciplinary nature, data types, sources, and its role in business. You'll also learn the fundamentals of statistical methods and machine learning.

Gained skills:

Develop skills in data assessment, processing, and understanding data-driven decision-making in business, along with foundational insights into statistical inference and machine learning.

Real-world application:

Apply your knowledge to help businesses make data-driven decisions, such as improving customer segmentation in retail through purchase data analysis.

Assessment:

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

Module overview:

Get a foundational understanding of the Python programming language. Discover essential computer programming concepts such as variables, data types, and error handling.

Gained skills:

Learn fundamental Python syntax, elementary data types, and error handling. You'll also explore critical libraries and packages for data science.

Real-world application:

Use your Python programming skills to automate data tasks and create functional programs, like writing scripts to scrape and clean large datasets.

Assessment:

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

Module overview:

Get an introduction to differential and integral calculus, including functions, inverse functions, and the link between differentiation and integration.

Gained skills:

Develop mathematical techniques for solving classical mechanics problems and understand higher-dimensional function derivations.

Real-world application:

Apply mathematical modelling to optimise logistics and scheduling in supply chain management. For example, you could use differential calculus to identify cost-saving opportunities.

Assessment:

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

Module overview:

Learn the fundamentals of probability theory, including Bayesian statistics and probability distributions. You'll also learn about joint and marginal distributions.

Gained skills:

Develop an understanding of probabilistic concepts and moments and can calculate expectation values. You'll also be familiar with important inequality equations and limit theorems in statistical modelling.

Real-world application:

Use your statistical knowledge to forecast market trends and consumer behaviours. For instance, apply Bayesian statistics to predict the next quarter's product demand.

Assessment:

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

Module overview:

Dive into advanced Python programming concepts, focusing on object-oriented and functional programming paradigms.

Gained skills:

You'll master object-oriented programming concepts, functional programming, and relevant libraries in Python.

Real-world application:

Build robust software solutions using Python's advanced features. For instance, you could apply Bayesian statistics to predict the next quarter's product demand.

Assessment:

You'll complete a 3,000-word portfolio.

Module overview:

Focus on managing data quality and preparing data for analytical tasks. You'll learn advanced techniques for acquiring and formatting data.

Gained skills:

You'll become proficient in data quality management and data wrangling techniques for various data sources.

Real-world application:

Maintain high data quality standards by ensuring the reliability of business intelligence systems. For instance, prepare and clean data for a comprehensive sales analysis in a large retail organisation.

Assessment:

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

Module overview:

Learn the fundamentals of linear algebra, including vectors, matrices, and techniques for analysing problems in analytical geometry.

Gained skills:

Develop abilities in linear equation systems, vectors, vector spaces, and matrix decomposition methods.

Real-world application:

Apply linear algebra to machine learning algorithms for predictive analytics. For example, use matrix operations to develop recommendation systems.

Assessment:

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

Module overview:

Get an in-depth look at statistical inference, focusing on Bayesian inference, error propagation methods, and hypothesis testing.

Gained skills:

You'll master point estimation methods, maximum likelihood, and error propagation. You'll also gain insights into statistical testing and decision theory.

Real-world application:

Conduct A/B testing to optimise website user experiences by interpreting advanced statistics. For instance, evaluate which version of a webpage results in higher user engagement.

Assessment:

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

Module overview:

Learn the essentials of academic research methodologies, structured academic writing, and proper citation techniques.

Gained skills:

You’ll gain the ability to conduct academic research, write well-structured papers, and manage sources effectively.

Real-world application:

These skills will be invaluable in roles requiring detailed report writing and data analysis. For example, you might use your quantitative skills to prepare comprehensive research reports for market analysis.

Assessment:

You'll complete an advanced workbook consisting of 6 assignments, each 150-300 words.

Module overview:

Discover the principles of relational data modelling and SQL databases. You'll also learn about NoSQL database systems.

Gained skills:

You'll become skilled in designing, creating, and modifying SQL queries and data schemas. You'll also understand key NoSQL concepts.

Real-world application:

With these skills, you can design and manage efficient database systems for business operations. For instance, you could develop a relational database to efficiently store and retrieve customer data.

Assessment:

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

Module overview:

Master exploratory data analysis and the principles of data visualisation. You'll learn practical data visualisation fundamentals in Python.

Gained skills:

You'll become adept at big data visualisation and effective visual communication of data science results.

Real-world application:

Enhance business presentations with compelling visual data insights. For example, use exploratory data visualisation techniques to present sales performance metrics to stakeholders.

Assessment:

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

Module overview:

Study modern software development principles and project management techniques, focusing on Agile methodologies.

Gained skills:

You'll develop skills in Agile software development and automated software testing.

Real-world application:

Apply Agile techniques to manage data science projects effectively. For instance, you could use Scrum to manage the development of a machine learning model for customer segmentation.

Assessment:

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

Module overview:

Dive into supervised learning techniques, including regression, classification, and support vector machines.

Gained skills:

You'll learn to apply and critically assess supervised machine learning models.

Real-world application:

Build predictive models to improve company decision-making processes. For example, you might develop a regression model to predict future product sales based on historical data.

Assessment:

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

Module overview:

Explore unsupervised learning techniques and feature engineering methods. You'll also learn about clustering models and dimensionality reduction.

Gained skills:

You'll gain expertise in unsupervised learning and effective feature engineering.

Real-world application:

Use unsupervised learning to identify hidden patterns in large datasets. For instance, cluster customers based on purchase behaviour for targeted marketing campaigns.

Assessment:

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

Module overview:

Understand the theoretical foundations of neural networks and deep learning. You'll also learn to implement and evaluate neural networks.

Gained skills:

You'll be skilled in implementing and optimising deep learning models using modern frameworks.

Real-world application:

Develop deep learning models for image and speech recognition. For example, create a neural network to automate customer service responses.

Assessment:

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

Module overview:

Address the ethical implications of data science and AI, focusing on privacy, fairness, and moral dilemmas.

Gained skills:

You'll critically assess ethical issues in data science and formulate guidelines for responsible practices.

Real-world application:

Apply ethical guidelines in developing AI solutions to ensure they are fair and unbiased. For example, create policies to safeguard user privacy in data collection.

Assessment:

You'll complete a 3,000-word research essay.

Module overview:

Conduct independent research in data science, from formulating a research question to presenting your findings in a final-year thesis.

Gained skills:

You'll gain scientific research skills and the ability to communicate your findings effectively.

Real-world application:

Showcase your expertise through significant third-year research projects, such as developing and presenting an innovative data analysis method.

Assessment:

You'll complete a 7,500-10,000 word bachelor thesis.

• Project: from model to production

  Get practical experience in deploying predictive models within enterprise-level applications or services. This project-centred module addresses key considerations such as data management, processing workflows, and service reliability.

• Project: build a data mart in SQL

  Use your understanding of data modelling and database theory to tackle real-world problems in this case study module. You'll be responsible for designing and implementing a functional database while making crucial architectural decisions.

• Agile project management

  Receive a hands-on introduction to agile project management, highlighting its differences from traditional, plan-driven approaches. You'll delve into core values, roles, and activities typical of agile frameworks, culminating in a practical project exercise.

• Self-arranged internship I (*)

  Enhance your employability by arranging and completing an internship that hones your practical and analytical abilities. Through preparatory tutorials, you'll examine workplace dynamics and set clear, personal objectives for your placement.

• Self-arranged internship II (*)

  Building on your prior internship experience, this module offers another opportunity to apply your academic skills in a professional, entrepreneurial environment. You'll focus on enhancing your communication, problem-solving, and time management abilities.

*You'll be responsible for arranging a suitable internship in your field. Once placed, please complete an application to the internship module via myCampus to ensure that your internship can be recognised and credited towards your course requirements.

Business intelligence and data analytics

• Business intelligence

  Learn about the processes and models involved in data provisioning, generating actionable information, and performing analyses. This module also guides you through data modelling and warehousing, and teaches strategies to enhance business operations.

• Project: business intelligence

  Design and implement your own business intelligence prototype. This project-based module helps you navigate common obstacles in creating small-scale BI solutions, from initial design to practical execution.

Sales and marketing

• Applied sales I

  In today's global market, attracting and retaining customers is more challenging than ever. This module emphasises the importance of strategic sales thinking, covering essential topics like sales organisation, alternative distribution channels, and negotiation tactics.

• Applied sales II

  Build upon your foundational sales knowledge by diving deeper into the principles that drive customer satisfaction and loyalty. You'll apply these concepts in a real-world scenario, enhancing your understanding of effective sales management.

Supply chain management and industry 4.0

• Supply chain management I

  Gain insights into both the theoretical and practical aspects of supply chain management (SCM). This module explores modern logistics, process flows, and network standards essential for effective SCM.

• Supply chain management II

  Learn how to establish and sustain a competitive edge through strategic supply chain management. You'll analyse key activities and tools within the Plan, Source, Make, Deliver, and Return categories to optimise SCM processes.

Data engineering and big data technologies

• Big data technologies

  This module explores the four key characteristics of big data – volume, variety, velocity, and veracity. You'll learn about various data sources, types, and storage formats, as well as managing data structures for large datasets.

• Cloud computing

  Develop cloud computing skills and understand its ability to enable technologies and analytics capabilities. You'll explore advanced topics like serverless computing and cloud storage, along with popular cloud service providers.

Artificial intelligence

• Artificial intelligence

  Trace the evolution fo artificial intelligence from early milestones to current advancements. You'll explore modern AI systems and discover how you can contribute to this rapidly growing field.

• Project: artificial intelligence

  Take on the task of creating your own AI system in this project-based module. You'll address specific requirements, practical limitations, and expected outcomes as you bring your AI concept to life.

Banking and finance

• Crypto and blockchain

  Learn about cryptocurrencies and blockchain technology, tracing the history of money from traditional coins to digital assets. Explore the state of the cryptocurrency market and its underlying principles. 

• Fintech

  Discover the rapid growth of financial technology solutions in recent years. This module covers the major sectors influenced by Fintech, the latest technological advancements, and insights into the future direction of the industry. 

Business intelligence and data analytics

• Advanced data analysis

  Stay ahead of the curve by mastering a variety of data analytics platforms essential in today's business landscape. This module covers everything from measuring business performance to text mining, web data analytics and social media analytics.

• Project: data analysis

  Apply your advanced analytical skills to real-world problems through this hands-on project. You'll develop a comprehensive report that simulates the demands of a professional data science environment, putting your knowledge to the test.

Sales and marketing

• Online marketing

  Dive into the world of online marketing by exploring digital advertising campaigns and email marketing. You’ll assess different channels, navigate legal considerations like GDPR, and use web analytics to evaluate campaign success.

• Social media marketing

  Social media has evolved from a tool for personal communication to a platform for commercial advertising. Learn how to use channels like Facebook, Instagram, and Pinterest to create an effective marketing strategy.

Supply chain management and industry 4.0

• Product development in industry 4.0

  Find out how the fourth industrial revolution affected product development. You'll analyse how emerging trends and alternative design approaches are reshaping the industry, focusing on placing the consumer at the centre of innovation.

• Project: smart product solutions

  Showcase your ability to apply agile engineering methods to smart product solutions in this practical project. You'll also demonstrate your proficiency in selecting the right tools to analyse and adapt various business models.

Data engineering and big data technologies

• Data engineering

  Gain a deep understanding of essential data engineering concepts, including storage infrastructure, data security, system architecture, and the latest advancements in storage technology. You'll also explore the logic behind data pipelines.

• Project: data engineering

  Build your own professional portfolio by tackling a real-world data-engineering project. Whether you choose to set up a Docker container environment or construct a data pipeline using DataOps principles, this module gives you the freedom to explore your interests.

Artificial intelligence

• Self-driving vehicles

  Focus on the critical safety standards and IT security concerns surrounding autonomous vehicles. This module offers an in-depth look at key areas such as sensor fusion, feature detection, calibration, localisation, satellite-based systems, and motion planning.

• Seminar: current topics and trends in self-driving technology

  Dive into autonomous vehicle technology. This seminar explores the technical innovations and philosophical debates that will shape the future of self-driving cars, culminating in a research essay where you apply your newfound knowledge.

A BSc Data Science degree increases your future employability prospects. The programme provides you with the technical expertise to analyse and interpret data and equips you with critical thinking and problem-solving skills sought after in a variety of industries. Given the rapid growth and high demand for data science professionals, graduates of data science undergraduate programmes have a distinct advantage in the job market.

While data scientists often work closely with IT professionals, their role is broader and more analytical. A data scientist uses a blend of expertise in mathematical statistics, computer science, and domain-specific knowledge to derive insights and solve problems. Pursuing a Data Science BSc prepares you for interdisciplinary roles that connect IT, business strategy, and analytical tasks. 

A BSc Data Science degree is one of the best options to pursue if you aim to become a data scientist. It provides comprehensive training in mathematical, statistical machine learning, and computational techniques, along with practical coding skills in languages like Python. Completing a data science undergraduate programme ensures you have the essential foundation and specialised knowledge needed to thrive. 

In our Data Science BSc, you'll explore further mathematics modules that will equip you with essential mathematical methods like differential and integral calculus, as well as vector spaces and matrix calculations. Additionally, you'll delve into mathematical statistics, enabling you to turn data into actionable insights and solve real-world problems with confidence.

While A level Mathematics is often recommended for the BSc Data Science course, it's not always a requirement. At LIBF, we ask for any two subjects at A Levels, along with GCSE Maths and English. We push the boundaries of accessibility, supporting students from diverse educational backgrounds. You'll receive all the guidance you need to master the mathematical techniques essential for data science.

After your BSc Data Science degree, you can advance your knowledge by pursuing postgraduate study in areas such as Data Management, Data Science, Business Intelligence, and Artificial Intelligence. These courses delve deeper into advanced higher mathematics and complex data analysis techniques, enhancing your career prospects further and making you a highly sought-after professional.