</br /><p>This Programme Variation/Integrated Foundation forms part of a broader programme of study and should be read alongside your programme specification.</p>

Core

To pass Programme Stage 0 you must have acquired 120 credits at level HE3 as specified in the programme scheme. To progress from Programme Stage 0 to Programme Stage 1 of the degree, the Foundation Year requirements must have been satisfied. In particular, as stated above, a minimum overall year 0 average of 60% must be achieved. 

Stage 0  

Stage P

Programme Variation Structure

Data Science with Integrated Foundation and Placement Year

Certificate of Higher Education

Master of Science with Honours

Diploma of Higher Education

Bachelor of Science (Honours)

engage in critical peer review process of papers, software and proposals, and give positive advice for improvement and innovation 

Skills

rationally exploit both traditional and novel technological approaches 

Values and attitudes

rigorously assess alternative approaches and novel designs and implementations 

use the latest hardware and software technologies to create high-performance systems with a high level of capacity 

use and explain the theory of computer science 

Knowledge

analyse and solve problems based on theoretical considerations, and develop innovative solutions, taking into account user needs and constraints 

understand and propose means to consider professional, social, cultural and ethical issues related to data analysis in the context of the scientific process 

plan and manage a large scale individual problem solving computing project 

explain legal issues relating to computing: intellectual property, data protection, computer misuse and health and safety 

gain skills about professional ethics and privacy in the context of data protection 

analyse and solve problems and develop innovative solutions, processing real data, designing and implementing big data methods and tools, selecting, applying and evaluating big data techniques 

identifying and managing scientific and technical risks and uncertainty associated with data science and its applications 

design, develop, adapt and critically evaluate data science computer algorithms and systems 

review and critically evaluate the literature and current developments and challenges in data science, such as designing distributed solutions or efficient learning algorithms  

demonstrate the ability to use data science methods and techniques (e.g. in data analysis, pattern recognition and machine learning, high-performance computing, knowledge extraction, visual analytics) 

embrace technical challenges as an opportunity for personal development  

define a technical goal and encourage and lead others in order to achieve it 

design and use data visualization tools both to perform human-empowered explorative analysis and to also communicate findings effectively to technical and non-technical audiences 

analyse, develop and select robust algorithms and tools that can handle uncertainty and large amounts of data 

assess the nature of intellectual property and its ownership, and respect it accordingly 

explain the concepts of computer programming and data analysis methodologies 

analyse, evaluate and act upon descriptive documents 

Curriculum Catalogue

Data Science with Integrated Foundation and Placement Year - MSci (Hons), MSci (Hons)

USDSIP