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Data Science at City, University of London - UCAS

Course options

2024 - 2025
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This course is subject to validation. You can still apply for this course while it is being approved.

City, University of London has participated in the Teaching Excellence Framework.These ratings were awarded in 2023, for four years.

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22May
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Undergraduate

Course summary

Data science is an in-demand discipline across a wide range of sectors, from energy and transport to the creative industries. This Data Science degree will equip you with the valuable combination of computing and data analysis skills needed for a career in this fast-growing area. During this Data Science degree, you will develop skills in computer science, such as algorithmic thinking and programming. You’ll also develop specialist data science skills needed for the extraction of actionable insights from data. These skills, combined with creative problem-solving, can answer challenging questions and bring new knowledge to light. This skill set will be widely applicable within the computing industry and in various other sectors including retail and health, making our graduates highly employable.

  • Acquire leading-edge knowledge, skills and techniques required by the data science profession
  • Become proficient in a broad range of programming languages and software design techniques
  • Work with and learn from active researchers in machine learning, high-performance computing and data visualization
  • Apply your knowledge and skills to develop solutions in data-intensive sectors where insights can deliver commercial advantage or social benefit
  • Access excellent work experience opportunities at nearby Tech City.
Accredited by BCS, The Chartered Institute for IT for the purposes of fully meeting the academic requirement for registration as a Chartered IT Professional and on behalf of the Engineering Council for the purposes of fully meeting the academic requirement for registration as a Chartered Engineer.

Modules

On the Data Science MSci you will begin your studies by developing a firm foundation in computer science, before broadening your study to encompass data science theory, methods and techniques. Year 1 Study our common first year for all our computer science students, learning six core topics including operating systems, web development and Java. -Computation and Reasoning (15 Credits) -Mathematics for Computing (15 Credits) -Systems Architecture (15 Credits) -Programming in Java (30 Credits) -Databases (15 Credits) -Operating Systems (15 Credits) -Computer Science, Ethics & Society (15 credits) Year 2 Deepen your knowledge of computer science with core modules such as C++ and data structures. Boost your professional skills with a team project and a work-based project. -Data Structures and Algorithms (15 Credits) -Language Processors (15 Credits) -Object-Oriented Analysis and Design (15 Credits) -Professional Development in IT (15 Credits) -Team Project (30 Credits) -Programming in C++ (15 Credits) -Computer Networks (15 Credits) Year 3 Build your data science skill set with five core modules and three elective modules, including principles of data science, and AI. -Computer Vision (15 Credits) -Principles of Data Science (15 Credits) -Introduction to AI (15 Credits) -Programming and Mathematics for AI (15 Credits) -Agents and Multi Agents Systems (15 Credits) -Games Technology (15 Credits) -Advanced Databases (15 Credits) -Theory of Computation (15 Credits) -Advanced Games Technology (15 Credits) -Professional Experience (Placement) Placement Reports (30 Credits) -Data Visualization (15 Credits) -Digital Signal Processing and Audio Programming (15 Credits) -Advanced Programming: Concurrency (15 Credits) -Functional Programming (15 Credits) -Cloud Computing (15 Credits) -Information Security Fundamentals (15 Credits) -User Centred Systems (15 Credits) -Semantic Web Technologies and Knowledge Graphs (15 credits) -Project Management (15 credits) Year 4 Develop professional data science expertise with five core modules, including big data and visual analytics. Showcase your knowledge with a data-intensive individual research project. -Neural Computing (15 Credits) -Machine Learning (15 Credits) -Big Data (15 Credits) -Visual Analytics (15 Credits) -Information Retrieval (15 Credits) -Individual Project (45 Credits) -Software Systems Design (15 Credits) -User-Centred System Design (15 Credits) -Digital Signal Processing and Audio Programming (15 Credits) -Advanced Programming: Concurrency (15 Credits) -Advanced Algorithms and Data Structures (15 Credits) -Cloud Computing (15 Credits) -Computational Cognitive Systems (15 Credits) -Advanced Games Technology (15 Credits) -Semantic Web Technologies and Knowledge Graphs (15 credits) -Project Management (15 credits) The one year placement can be undertaken following successful completion of year 3 and will be required to last for a minimum of 9 months.

Assessment method

Most modules are assessed with examinations and coursework. Details can be found in the individual module specifications. Typically, modules are mainly assessed through written examination, and coursework also contributes to module assessment. The written examinations will contain theoretical questions, including mathematical aspects, as well as writing and analysing small amounts of code and small essays on the applications of computational techniques. As you move over to the more specialised modules as part of your Programme Stage 3 and Programme Stage 4, you will be expected to demonstrate how well you can synthesise various pieces of knowledge and be also assessed on how well you can critically reflect on the solutions you are suggesting. The balance of assessment by coursework (assessed essays and assignments) unseen examinations and a final year project will to some extent depend on the optional modules you choose. Year 1 Written examination: 41% Coursework: 59% Year 2 Written examination: 35% Coursework: 65% Year 3 Written examination: 24% Coursework: 76% Year 4 Written examination: 35% Coursework: 65%

How to apply

31Jan

This is the deadline for applications to be completed and sent for this course. If the university or college still has places available you can apply after this date, but your application is not guaranteed to be considered.

Application codes

Course code:
G102
Institution code:
C60
Campus name:
City, University of London
Campus code:
L

Points of entry

The following entry points are available for this course:

  • Year 1

Entry requirements

Qualification requirements

We do accept applications from students who are completing a combination of qualifications. For this course, this would be something like: D* in IT with a grade B in ‘A’ Level Computer Science and a grade B in another ‘A’ Level. We may also take ‘AS’ Level grades into consideration.

Student Outcomes

Operated by the Office for Students
86%
Employment after 15 months (Most common jobs)
93%
Go onto work and study

The number of student respondents and response rates can be important in interpreting the data – it is important to note your experience may be different from theirs. This data will be based on the subject area rather than the specific course. Read more about this data on the Discover Uni website.

Fees and funding

Tuition fees

No fee information has been provided for this course

Additional fee information

No additional fees or cost information has been supplied for this course, please contact the provider directly.
Data Science at City, University of London - UCAS