Computer Science (BSc) including Foundation Year

Credits: 10 ECTS

This module aims to develop students’ academic presentation skills and research methods (including use of library and database systems) with a focus on the importance of appropriate and accurate referencing.

Classes also aim to develop effective reading and listening techniques for comprehension and summary tasks, and critical evaluation skills. This module emphasises learner training, which includes self-reflection, goal-setting and evaluation, to better prepare students for their academic studies.

Learning outcomes

1. Identify key points in listening extracts to complete comprehension and summary tasks.
2. Identify key points in reading texts to complete comprehension and summary tasks.
3. Conduct research using library and online resources.
4. Apply the Cardiff University Harvard Referencing system.
5. Prepare and deliver effective academic presentations.

Credits: 10 ECTS

This module introduces you to how science works and how scientists investigate the world. You will become more comfortable asking questions and learn to design simple experiments, and carefully analyse results. You will also learn to think critically, work independently, and study effectively in a UK university setting.

You will practise how to read scientific information, spot mistakes or weak arguments, and share your ideas clearly through posters, videos, and presentations.

Learning outcomes

1. Apply the scientific method to create testable hypotheses and design simple experiments based on observation and logical reasoning.
2. Identify strengths and weaknesses in scientific information by evaluating evidence and recognising bias or limitations in research claims.
3. Analyse and interpret data using appropriate methods to find patterns and draw logical and valid conclusions.
4. Communicate scientific ideas clearly through a short video presentation and visual tools, using correct scientific terms and presentation techniques.
5. Demonstrate the ability to reflect on the scientific method and your own learning process using study skills relevant to higher education.

Credits: 10 ECTS

This module helps you build strong skills in key areas of mathematics such as algebra, trigonometry, and geometry. You will learn how to solve problems, think logically, and understand important mathematical ideas. These skills are essential to study a degree on Physical Sciences and Engineering.

You will explore how to work with different kinds of equations, graphs, and shapes. You will also begin learning basic calculus, including how to find slopes and areas using simple rules.

Learning outcomes

1. Accurately simplify and manipulate algebraic expressions and functions, including polynomials, rational expressions, exponents, to solve problems.
2. Solve and interpret linear, quadratic, simultaneous, and trigonometric equations and inequalities using appropriate algebraic and graphical methods.
3. Use coordinate geometry and vector operations to solve geometric problems involving lines, circles, and basic conic sections in two dimensions.
4. Apply trigonometric rules, graphs, identities, and inverse functions to solve problems involving angles, triangles, and periodic patterns in radians and degrees.

Credits: 10 ECTS

This module introduces you to the fundamental principles and techniques of data analysis, providing a foundation in data collection, cleaning, and manipulation, alongside essential descriptive statistics and data visualisation methods.

You will learn how to summarize and interpret data effectively, identify patterns and trends, and gain a basic understanding of inferential statistics, including hypothesis testing and confidence intervals. The module emphasises practical application, enabling students to analyse real-world data sets and draw meaningful conclusions.

Learning outcomes

1. Collect and organize data from various sources, and prepare it for analysis using appropriate techniques.
2. Calculate and interpret descriptive statistics, including measures of central tendency, dispersion, and correlation.
3. Visualise data effectively using a range of graphical methods, such as histograms, scatter plots, and box plots.
4. Perform basic inferential statistical tests, and interpret the results.
5. Communicate findings from data analyses clearly and concisely, using appropriate statistical terminology and visualisations.

This module introduces you to the powerful combination of mathematics and computation for understanding and simulating real-world phenomena. You will learn the basic rules of calculus and core programming principles to turn mathematical ideas into working computer programs.

Learning outcomes

1. Write simple algorithms using variables and conditional statements to model mathematical problems.
2. Differentiate standard mathematical functions from first principles and apply rules such as the product, quotient, and chain rules to solve practical problems.
3. Translate mathematical problems into clear computational algorithms and accurately implement them in code.
4. Analyse simple mathematical models that use calculus and simulation of differential equations by applying numerical methods and visualization techniques.
5. Apply computational thinking and programming skills to investigate and solve problems in scientific and mathematical contexts by integrating advanced modelling, simulation, and visualisation methods.

Additional course.