BSc (Hons) Data Science Bachelor's degree at Sheffield Hallam University

This module develops your knowledge and understanding of key statistical techniques, statistical software and project-based skills, working collaboratively with local partners on an applied project. You'll learn through a mix of lectures, workshops and laboratory sessions. You'll study topics such as: Project-based statistics, from data collection to conclusions; Statistical programming; Data manipulation, summary and visualisation; Commonly used statistical techniques and statistical modelling;

Assessment: Coursework (100%)

This module introduces computer programming and its deployment in a mainstream programming language for data science and artificial intelligence applications. You'll learn fundamental programming concepts and develop essential skills for the entire data processing pipeline, from collecting and cleaning raw data to performing sophisticated data analysis. You'll gain essential skills in statistical concepts and simple machine learning models, along with hands-on experience in creating AI-driven a

Assessment: Coursework (100%)

This module develops the skills required of a professional mathematician, including reading, thinking and working with mathematical and statistical ideas, and programming. In lectures, workshops and group tutorials, you'll develop confidence and accuracy with foundational concepts and techniques, extending these to new topics which are essential for successful further study. You'll study topics such as: Functions, solving equations and complex numbers; Calculus, analytical and numerical techniqu

Assessment: Coursework (60%), Exam (40%)

This module explores problem solving and mathematical thinking skills by introducing mathematical theory and abstraction. Through interactive workshops you'll develop an appreciation of rigour and methods of mathematical proof. You'll study topics such as: Problem-solving strategies; Communicating solution methods with clear definitions and justifications; Connecting and applying prior learning and skills to new problems; Proof methods; Set theory and logic; Number bases; Modular arithmetic; Fun

Assessment: Coursework (100%)

This module will impart key concepts in computer science to students and to further develop important skills in computer-based problem-solving and data manipulation. Indicative Content: Introduction to algorithms (role and importance of algorithms in computer science). Concurrent, and parallel algorithms; Sorting algorithms (merge sort, heap sort, quick sort); Searching algorithms; Linked lists (Singly linked lists; double linked lists); Stacks and Queues; Recursion; Tree structures (creating, s

Assessment: Coursework (100%)

This module delivers knowledge and practical skills of machine learning algorithms for artificial intelligence applications. It will present the key mathematical principles and concepts required to create machine learning algorithms through data-driven approaches. You will gain practical experience while designing, implementing, and evaluating machine learning systems to solve real-world artificial intelligence problems. You'll study topics such as: Data-driven approaches to machine learning; Li

Assessment: Coursework (100%)

This module will introduce a comprehensive approach of data organization within a database, encompassing the methods of storage, retrieval, access, and manipulation in a cloud-based environment. This module will acquaint learners with the principles of storing, retrieving, and analysing data through the use of databases. Learners will have the opportunity to investigate the mechanisms by which data is stored and transferred across systems within a cloud environment. The curriculum will cover var

Assessment: Coursework (100%)

This module develops your theoretical understanding and practical application of more advanced statistical techniques that are used widely in industry. You'll also expand on techniques covered at level 4. You'll study topics such as: Statistical modelling and application; Model formulation and selection; Identification of issues; Non-linear models; Understanding the relevant theory; Probability and theory of statistical inference; Bayesian statistics.

Assessment: Coursework (100%)

The aim of this module is to enhance students' professional development through the completion of and reflection on meaningful work placement(s). A work placement will provide students with opportunities to experience the realities of professional employment and experience how their course can be applied within their chosen industry setting. The placement will: Allow student to apply the skills, theories and behaviours relevant and in addition to their course; Enable students to enhance their in

Machine Learning techniques, such as artificial neural networks, are widely used in modern intelligent systems. The module aims to deliver key concepts, theories and practical skills to implement state-of-the-art neural networks techniques within the artificial intelligence problems domain. This module enhances the knowledge and experiences from corresponding level 5 machine learning modules and focuses on smart system design, development, evaluation and optimisation. Indicative Content: Artific

Assessment: Coursework (100%)

This module is a research project of your choice – you'll identify a computer-based problem, investigate the requirements, analyse results of research undertaken and design, and develop and evaluate a solution to that problem. You'll then evaluate the project's success, your learnings and opportunities for further work. You'll apply skills and learning such as: Ideation and planning a larger-scale project; Information gathering and literature reviews; The selection of tools, techniques or method

Assessment: Coursework (40%), Practical (60%)

This module emphasizes the necessity of incorporating ethical design principles into AI and data science projects, highlighting the importance of fairness, transparency, and accountability. Learners will explore data ethics, including privacy, consent, and bias, ensuring responsible data handling throughout its lifecycle. The module also covers data security, focusing on protecting data from unauthorized access and breaches, and maintaining integrity and confidentiality. Students will engage wit

Assessment: Practical (100%)