Curious about studying Data Science and Analytics (Including Placement Year) - BSc (Hons)at University of Essex? We've gathered all the key details—entry requirements, modules, fees, and more. Hear from real students by checking out reviews, or take the next step by booking an open day to explore it for yourself.
Bachelor of Science (with Honours) - BSc (Hons)
University of Essex
Full Time
Oct 2026
4 Year
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What is the BSc (Hons) Data Science and Analytics?The BSc (Hons) Data Science and Analytics is a three-year degree, extendable to four with an optional year in placement or abroad.
You’ll secure a broad understanding of solid theory and practical applications of mathematics, computing and data analysis, ready to make use of our increasing generation of data to inform important decisions and predict new trends. ###
Why this courseThe BSc (Hons) Data Science and Analytics lets you blend pure mathematics, applied topics and statistical, financial and analytical methods. You’ll also develop programming skills in languages such as Python and SQL. You’ll gain a balanced understanding being taught by experts in both our
School of Mathematics, Statistics and Actuarial Scienceand our
School of Computer Science and Electronic Engineering. Add a placement year or study abroad to put your learning into practice, expand your perspective and build confidence in professional or international settings. You’ll graduate into a well-rounded data analyst ready for a range of industries, from health to business. Crucially, you’ll leave with a personal portfolio to demonstrate your mathematical capabilities in job applications and interviews. ###
Who should apply* Future data analysts and mathematicians * Students eager to master Big Data and make informed decisions * Those considering careers that apply mathematics or further its modern understanding * Practical, analytic thinkers
This module will allow you to build your knowledge of differentiation and integration, how you can solve first and second order differential equations, Taylor Series and more.
In this module you will learn the fundamentals of probability and statistics, including axioms and combinatorial analysis, distributions, and independence conditions. You will learn how to apply the addition rule of probability and construct diagrams to visually represent data sets. The course also covers the use of descriptive statistics to analyse data and provides hands-on experience with the R software package.
The aim of this module is to provide an introduction to the fundamental concepts of computer programming. After completing this module, students will be expected to be able to demonstrate an understanding of the basic principles and concepts that underlie the procedural programming model, explain and make use of high-level programming language features that support control, data and procedural abstraction. Also, they will be able to analyse and explain the behaviour of simple programs that incorporate standard control structures, parameterised functions, arrays, structures and I/O.
Want to become a Java programmer? Topics covered in this module include control structures, classes, objects, inheritance, polymorphism, interfaces, file I/O, event handling, graphical components, and more. You will develop your programming skills in supervised lab sessions where help will be at hand should you require it.
Databases are everywhere. They are employed in banking, production control and the stock market, as well as in scientific and engineering applications. For example, the Human Genome Project had the goal of mapping the sequence of chemical base pairs which make up human DNA. The result is a genome database. This module introduces the underlying principles of databases, database design and database systems. It covers the fundamental concepts of databases, and prepares the student for their use in commerce, science and engineering.
Matrices and complex numbers are two fundamental concepts which arise throughout mathematics. In this module you will be introduced to these objects and learn fundamental techniques for working with them in a variety of contexts.
Our Team Project Challenge gives you the opportunity to develop a range of professional skills by working as part of a small student team on a specific project. The projects are research-based and incorporate the concepts of specifications, design, and implementation. You’ll learn about sustainability, project and time management, design, legal issues, health and safety, data analysis and presentation, team reporting, and self-evaluation. You’ll also develop skills such as critical thinking and problem solving, agility, leadership, collaboration across networks, and effective oral and written communication, as well as curiosity and imagination, all of which will enhance your knowledge, confidence and social skills necessary to innovate and succeed in a competitive global environment.
This course covers the principles of project management, team working, communication, legal issues, finance, and company organisation. Working in small teams, students will go through the full project life-cycle of design, development and implementation, for a bespoke software requirement. In this course, students gain vital experience to enable them to enter the computer science/Electrical engineering workforce, with a degree backed by the British Computer Society, and by the Institute of Engineering and Technology.
The aim of this module is to build on the foundations of data and information systems laid down in the first year, learn how to design and manage fully structured data repositories and explore the rather different principles and techniques involved in representing, organising and displaying unstructured information.
Artificial intelligence will be a great driver of change in the coming decades. This module provides an introduction to three fundamental areas of artificial intelligence: search, knowledge representation, and machine learning. These underpin all more advanced areas of artificial intelligence and are of central importance to related fields such as computer games and robotics. Within each area, a range of methodologies and techniques are presented, with emphasis being placed on understanding their strengths and weaknesses and hence on assessing which is most suited to a particular task.
This module continues your journey into probability and statistics. Topics include distribution theory, estimation and Maximum Likelihood estimators, hypothesis testing, basic linear regression and multiple linear regression. You will continue to develop your skills with implementations in R.
Are you able to solve a small linear programming problem using an appropriate version of the Simplex Algorithm? Learn to formulate an appropriate linear programming model and use the MATLAB computer package to solve linear programming problems. Understand the methods of linear programming, including both theoretical and computational aspects.
Data structures and algorithms lie at the heart of Computer Science as they are the basis for the efficient solution of programming tasks. In this module, students will study core algorithms and data structures, as well as being given an introduction to algorithm analysis and basic computability.
Everything in the world is linked together, and this module introduces the theory of networks which illuminates these mysterious links. You will begin with an introduction to the most common types of network and their mathematical properties, as well as typical network models. You will also use Python/R methods to model and analyse real-world networks.
What are the principles of actuarial modelling? And what are survival models? Examine how calculations in clinical trials, pensions, and life and health insurance require reliable estimates of transition intensities/survival rates. Learn how to estimate these intensities. Build your understanding of estimation procedures for lifetime distributions.
A Capstone Project is a self-study module which allows you to step out of the classroom and gain experience of a topic from mathematical sciences beyond your taught modules. You will develop your independent research skills, as well as report-writing and communication skills, both written and oral.
Can you calculate confidence intervals for parameters and prediction intervals for future observations? Represent a linear model in matrix form? Or adapt a model to fit growth curves? Learn to apply linear models to analyse data. Discuss underlying assumptions and standard approaches. Understand methods to design and analyse experiments.
In this module, you will study three application areas of statistics - multivariate methods, demography and epidemiology, and sampling, and how to apply and assess these methods in a variety of situations.
Ever considered becoming an Actuary? This module covers the required material for the Institute and Faculty of Actuaries CT4 and CT6 syllabus. It explores the stochastic process and principles of actuarial modelling alongside time series models and analysis.
What skills do you need to succeed during your studies? What about after university? How will you harness your knowledge and soft skills to realise your career goals? This module helps you take an active role in developing transferrable skills and capitalising on your unique background. As well as broad reflection on your professional development, this module will help you explore different career directions and prepare you for the application process, supported by an advisor from within the department.
Placement Year
Living and accommodation costs (per year, approximately)- 8,776 – 14,995
4.3
