BSc (Hons) Data Science Bachelor's degree at the University of Kent

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. You'll develop fundamental knowledge of calculus and linear algebra and learn how to use these concepts in Python coding applications to solve challenging problems.

Designed for beginners and assuming no experience, this module equips you with essential programming concepts and skills. 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++. Practical assignments and projects enable you to apply and hone your skills.

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. You'll also learn about abstraction and inheritance and develop a deeper practical understanding of how to program beyond simple problems.

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 HTTP application layer. You'll also develop a foundational frontend web development skill set, learning how to structure web pages using HTML, s

Jump into the world of data with a hands-on introduction to probability and statistical reasoning using tools like R, Excel, and collaborative platforms such as GitHub. Through practical exercises and real-world assignments, you will develop confidence in working with uncertainty and data – from organising and visualising data to analysing data sets and reporting results.

Algorithms are the most essential concept in computer science. Understanding algorithms and how to analyse them–both for correctness and efficiency–and learning how to develop new algorithms is the most crucial step in becoming a successful computer scientist. Throughout the module you will develop the skills to read and interpret problem descriptions, and the knowledge you need to solve these problems.

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. By the end of the module, you'll be able to use query languages, emerging techniques, and future technologies in the field of database systems.

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. Through hands-on activities and real data analysis, you'll gain the skills to extract actionable insights and forecast future trends confidently.

Optimisation techniques play an important role in practically all areas of data science, including data analysis and machine learning. An understanding of the core principles of optimisation algorithms and their properties is essential for all practitioners in this field. You'll gain expertise in the theory and applications of optimisation techniques and algorithms, focusing on the methods most relevant to data science.

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. You'll collaborate with others within set time frames to tackle authentic mathematical and data science challenges using real-world data sets.

The omnipresence of Artificial Intelligence (AI) is undeniable; it is not merely a futuristic concept but a current reality that permeates almost every facet of our lives. 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.

Natural organisms are well adapted to solve complex problems related to their survival in very dynamic and diverse environments. 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.

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. You'll explore key concepts including classification, prediction, and regression tree-based methods through engaging real-world datasets.