Data Science

Data Science
Companies have access to more data than ever. But they can’t truly make use of it without expert and informed analysis.

This course will develop your skills in a field that is in high demand, from industry to government, healthcare and beyond. Our BSc in Data Science is for students with an affinity for maths and statistics, who want to learn how to handle complex data and extract valuable insights.

Our curriculum is designed in partnership with industry, and taught by leading experts in their fields. You’ll learn different techniques for handling and analysing data, and when to apply them. You’ll develop programming skills in Python and R, and familiarise yourself with statistical software.

As you develop, you’ll code and design algorithms, structure datasets, and find an optimal solution that addresses a specific business need. You’ll even explore the latest ideas in machine learning and LLMs, as well as data mining and natural language processing.

Our course is designed to help you succeed in your career, with projects that give you the chance to learn by doing. Test theories in the lab, learn to research and present your findings, and take part in consultancy projects that tackle industry problems. You can include a placement year to gain work experience and apply data science skills in a company.

Year in Industry

Your year in industry takes place between your second and final year, giving you invaluable work experience. You earn a salary and there may be the possibility of a job with the same company after graduation.

Your Future
You graduate with a solid grounding in the fundamentals of data science and a range of professional skills, including:

• programming

• modelling

• design

To help you appeal to employers, you also learn key transferable skills that are essential for all graduates. These include the ability to:

• think critically

• communicate your ideas and opinions

• analyse situations and troubleshoot problems

• work independently or as part of a team

You can also gain extra skills by signing up for one of our Kent Extra activities, such as learning a language or volunteering.

An industrial placement can greatly enhance your studies and have a dramatic impact on your graduate choices.

Year 1

The following modules are what students typically study, but this may change year to year in response to new developments and innovations.

Year 1 compulsory modules currently include the following:

• Mathematics for Data Science - Mathematics underpins many of the methods and techniques used in data science. You’ll be equipped with the mathematical foundations necessary to excel in numerous areas in data science including data analysis, interpretation, modelling, and machine learning.

• Programming I - The module is a blend of theoretical instruction and hands-on exercises with the Python programming language. The skills you acquire will help you learn other programming languages such as Java and C++, to name a few.

• Programming II - In this module, you’ll learn how an object-oriented approach to software development allows us to think in a particular way about solving problems. This approach increases the likelihood that our code will be well-written and reliable.

• Internet Technologies - In this module you’ll examine the fundamental technologies that make modern web applications work.  You’ll learn to use operating and cloud systems to deploy, configure, and monitor software. You’ll dive into networking, from the basic principles of network latency and bandwidth to addressing and transmitting at different layers, from the datalink to the HTTP application layer. You’ll also develop a foundational frontend web development skill set, learning how to structure web pages using HTML, style them using CSS, and develop interactive web pages using JavaScript.

• Probability and Statistics for Data Science - Alongside computational skills, the module builds a solid foundation in probability and statistics. You will learn core principles such as parameter estimation, confidence intervals, and hypothesis testing, gaining the ability to draw meaningful inferences from sample data. By applying these concepts directly within R, you will see how statistical methods operate in practice and how they can be used to answer questions across science, industry, and society. This module provides the foundations for you to gain the analytical and technical skills needed for academic success and professional advancement.

For more detailed information about these modules, please visit our website.

Year 2

The following modules are what students typically study, but this may change year to year in response to new developments and innovations.

Year 2 compulsory modules currently include the following:

• Algorithms - Throughout the module you will develop the skills to read and interpret problem descriptions, and the knowledge you need to solve these problems. Your deepened understanding of algorithms from runtime, to executable programmes will set you up for an exciting and successful career in rapidly expanding digital industries.

• Database Systems - This module introduces you to the theory and practice of database systems. You’ll model, design, implement, and use database systems, gaining valuable skills you will need in your career as a software developer.

• Predictive and Explanatory Modelling in Context - Explanatory and predictive modelling is essential to data-driven decision-making. Throughout this module, you’ll learn about regression, the cornerstone of versatile statistical analysis and master diagnostics, model specification, selection, and interpretation.

• Optimisation for Data Analysis - You'll gain expertise in the theory and applications of optimisation techniques and algorithms, focusing on the methods most relevant to data science. You'll master how to optimise solutions, analyse and improve the performance of algorithms, and acquire decision-making techniques. The module will equip you with an understanding of the way many standard problems in data science can be formulated as optimisation problems. In addition, you’ll gain skills in applying basic optimisation algorithms and techniques, including Newton's and gradient based methods, to solve problems. Throughout the module computing tools will be used to illustrate how optimisation techniques and algorithms are used to compute solutions to relevant problems in data science.

• Preparing for Professional Practice - Gaining work experience is vital to improving your chances of finding employment once you graduate. This module is designed to simulate real-world work experiences, where you will work in groups on open-ended projects requiring a combination of diverse skills and knowledge.

• Fundamentals of AI - In this dynamic module, you will be introduced to the essential concepts of AI, setting the stage for a profound exploration into more advanced realms such as machine learning and bio-inspired computations. Through engaging weekly classes, your understanding will evolve, seamlessly transitioning from foundational principles to the intricacies of advanced concepts.

For more detailed information about these modules, please visit our website.

Year in Industry

Year in Industry

You spend a year working in an industrial or commercial environment between Stages 2 and 3. Our students go to a wide range of companies including:

• IBM

• Intel

• Disney

• Morgan Stanley.

They have also been to overseas employers in locations including Amsterdam, Hong Kong and the US.

Year 3

The following modules are what students typically study, but this may change year to year in response to new developments and innovations.

Year 3 compulsory modules currently include the following:

• Natural Computation - In this module, you’ll learn in detail the main mechanisms used by natural organisms for adaptation and optimisation. You’ll also learn how computer scientists have developed abstractions of those mechanisms to create several adaptive, intelligent algorithms to solve difficult real-world optimisation problems.

• Machine Learning and Deep Learning - A strong grasp of statistical modelling and optimisation principles forms the bedrock of machine learning. This module covers essential and advanced topics of machine learning and deep learning, blending theory with practical computing tools, such as R and Python.

• Bayesian Machine Learning - You’ll delve into fundamental Bayesian Inference concepts, including prior and posterior distributions, Bayesian estimation, Bayes factor, model selection, and forecasting. You’ll learn various posterior sampling algorithms and see how to apply them through real-world instances in linear regression and classification.

• Data Science Project - Propose your own project and explore the subject in depth using the data science methods that you have studied in the earlier modules. We’ll guide you through planning the project, providing you with initial comments, and then you will prepare and give a presentation describing your initial findings.

Optional modules may include the following:

• Data Mining and Knowledge Discovery - Data mining and knowledge discovery techniques are widely used in real-world applications. Examples of high-stakes applications include analysing data to decide whether or not a patient should undergo a surgery or a customer should be granted a loan or hired for a job. You’ll learn in detail how data mining algorithms work to automatically extract knowledge from data, and why these algorithms – which are based mainly on machine learning (but also on statistics) – are so important for today’s data-driven society.

• Natural Language Processing - Natural language processing (NLP) is an incredibly important and valuable component of artificial intelligence, making it a fascinating and rewarding area of study for computer science students. In today's digital age, numerous technologies rely on NLP to interpret and generate human language, such as virtual assistants, search engines for the World Wide Web, and large language models and chatbots. By delving into the realm of NLP, you can gain a deeper understanding of how these cutting-edge technologies work and the significant impact they have on our daily lives. Studying NLP not only allows you to explore the intricacies of artificial intelligence but also provides you with valuable skills that are highly sought after in the tech industry.

For more detailed information about these modules, please visit our website.