MSci (Hons) Data Science degree • City, University of London

This course is for 2022/23 entry.

What is unique about this course?

Key information

There is high demand for specialists who can extract insights and value from data. These insights inform everything from marketing activities to government strategy, transforming business and society. This integrated master’s degree provides you with a strong basis in computer science alongside specialist skills to analyse complex data sets.

Start date

September 2022

UCAS code

G102

Duration

4 years full-time
5 years with placement year

Institution

City, University of London

Location

Northampton Square

Entry requirements

Below is a list of countries with information on each about which qualifications we accept.

English language requirements

If your first language is not English, we will require evidence of English language proficiency. Minimum requirements are:

IELTS:

IELTS: 6.0 overall with a minimum of 6.0 in writing and 5.5 in all other components.

PTE Academic: 59 overall with a minimum of 59 in each component.

English language programmes

Don’t meet the English language requirements? INTO City, University of London offers English language programmes to help prepare you for study at university. These intensive and flexible courses are designed to improve your English ability for entry to degree courses.

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Course overview

Data science is an in-demand discipline across a wide range of sectors, from energy and transport to the creative industries. This degree will equip you with the valuable combination of computing and data analysis skills needed for a career in this fast-growing area.

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.

Accreditation

This course is pending accreditation by the BCS: The Chartered Institute for IT for the purposes of fully meeting the further learning academic requirement for registration as a Chartered IT Professional.

Structure

Course content

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
Year 2
Year 3
Year 4
Placement

Year 1

Study our common first year for all our computer science students, learning six core topics including operating systems, web development and Java.

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.

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)

In this module, you will learn the theory and practice of computer vision, which enables computers to perceive, and reason, about the world using images.
Principles of Data Science (15 Credits)

This module introduces techniques of knowledge representation and reasoning, statistical machine learning, high-performance computation, pattern recognition, information retrieval, data mining, data warehousing and data visualisation used in the DS process.
Introduction to AI (15 Credits)

The main aim and focus of the module is to provide you with a broad introduction in theory and practice to the main areas of nature-inspired, adaptive Artificial Intelligence.
Programming and Mathematics for AI (15 Credits)

The main purpose of the module is for you to gain fundamental programming and mathematical skills for studying specialist Artificial Intelligence topics.
Agents and Multi Agents Systems (15 Credits)

This module allows you to learn agent architectures that perform differently according to the characteristics of the task environment.
Games Technology (15 Credits)

The module will introduce aspects of both game and game engine design, including an overview of the structure of a typical game development company.
Advanced Databases (15 Credits)

This module will enable you to gain advanced knowledge on, and develop skills for, different ways of representing, managing, and manipulating data.
Theory of Computation (15 Credits)

Understand some of the standard abstract models of computation, how they may be shown to be equivalent, and what their limitations are.
Advanced Games Technology (15 Credits)

The module will cover advanced game engine design in a practical format. A fully featured game engine will be developed from first principles.
Professional Experience (Placement) Placement Reports (30 Credits)

This module is designed to focus your learning; analyse and assess your learning at university and apply this in an appropriate and intelligent manner to your placement role.
Data Visualization (15 Credits)

This module is designed to equip you with the technical and design skills to create and use data visualization applications for the workplace and research environments.
Digital Signal Processing and Audio Programming (15 Credits)

This module ensures that you will be able to master tasks of developing and applying digital signal processing; including one-dimensional signals and some 2-dimensional signal processing for images.
Advanced Programming: Concurrency (15 Credits)

The module aims to provide you with the practical knowledge of the problems involved in programming and how to resolve these problems and the implementation mechanisms used for obtaining correct concurrent systems.
Functional Programming (15 Credits)

This module will introduce you to functional programming, and the styles of programming and program structuring it makes possible. The emphasis is on solving problems, and using abstraction to produce components that are more general and reusable.
Cloud Computing (15 Credits)

This module aims to introduce you to the fundamental concept of computing-on-demand leading to Cloud computing. It follows a practical approach to building and running a test-bed cloud.
Information Security Fundamentals (15 Credits)

This module presents the fundamentals for information security from a strategic perspective, giving you the appropriate knowledge and skills to discuss, communicate within organisations.
User Centred Systems (15 Credits)

Introduction to the importance of usability in interactive systems. You will learn techniques for the design and evaluation of interactive systems.
Semantic Web Technologies and Knowledge Graphs (15 credits)

In this module you will learn the semantic technologies to meet today’s demands from industry, where data scientists must semantically orchestrate diverse types of data sources. This module gives a practical combination of web and data management technologies, knowledge representation and artificial intelligence.

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)

This module introduces and evaluates a range of established and cutting-edge, data-driven Neural Networks techniques and evaluation methods that can either be used in isolation or integrated with symbolic AI and Machine Learning in a number of application domains
Machine Learning (15 Credits)

This module introduces modern machine learning methods and techniques for the modelling and reasoning about uncertainty in large datasets, as well as the latest machine learning techniques used in diverse applications such as graph mining and link analysis, and multimodal signal processing including large scale video and sensor data.
Big Data (15 Credits)

Big Data, meaning large and complex datasets, are a key part of Data Science, and this course will cover methods and tools for capturing, curating, searching, and analysing large amounts of data.
Visual Analytics (15 Credits)

This module introduces Visual Analytics as the science of human-computer data analysis. It explains the principles of effective division of labour between the human and the computer and the role of interactive visual displays in supporting human analysts in analytical work.
Information Retrieval (15 Credits)

This practically-oriented module aims to provide you with a broad introduction to the field of documentary information retrieval. It aims to instil in you an appreciation of the problems involved in information provision, information retrieval and in the evaluation of information retrieval systems and applications.
Individual Project (45 Credits)

This module gives you an opportunity to undertake an independent piece of systematic research and/or problem-solving for a defined topic or problem.
Software Systems Design (15 Credits)

This module focuses on software systems design which is a fundamental component in an academic curriculum aimed at advanced training of system developers and integrators.
User-Centred System Design (15 Credits)

This module draws on the School’s research strengths in this area to introduce key concepts of the underpinning discipline of human-computer interaction (HCI) and provide you with techniques for effective interactive system design.
Digital Signal Processing and Audio Programming (15 Credits)

This module introduces you to the mathematical and computational framework for processing, analysing, and synthesising digital signals. We focus one-dimensional signals, in particular audio and vibrations, but cover also some 2-dimensional signal processing for images.
Advanced Programming: Concurrency (15 Credits)

Many practical applications in science and industry deal with processes that are executing concurrently, from GUI event handling, databases, to controlling devices such as antilock breaking systems in automobiles. Such systems pose a number of problems that are not encountered in sequential programs.

This module aims to provide you with the practical knowledge of the problems involved, the techniques used to design such systems so as to resolve these problems.
Advanced Algorithms and Data Structures (15 Credits)

This module revisits basic data structures and algorithms with more in-depth analysis and introduces advanced algorithms and data structures for diverse applications such as logic programming, machine learning, simulation, signal processing, and information retrieval.
Cloud Computing (15 Credits)

This module aims to introduce you to the fundamental concept of computing-on-demand leading to Cloud computing. Emphasis is given on the different technologies to build Clouds and how these are used to offer computing on-demand.
Computational Cognitive Systems (15 Credits)

This module allows you to analyse cognitive processes from the point of view of their being computational, information processing systems by gaining specific knowledge and skills in modelling perception, attention and memory, learning, and decision-making.
Advanced Games Technology (15 Credits)

The module will cover advanced game engine design in a practical format. A fully featured game engine will be developed from first principles.
Semantic Web Technologies and Knowledge Graphs (15 credits)

In this module you will learn the semantic technologies to meet today’s demands from industry, where data scientists must semantically orchestrate diverse types of data sources. This module gives a practical combination of web and data management technologies, knowledge representation and artificial intelligence.

Placement

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.

Download course specification:

USDASC - MSci Data Science

Teaching and assessment

The teaching and learning methods used will build on your ability to learn independently and develop your specialist knowledge as you progress through the programme.

This is reflected in the programme structure which begins with fundamental concepts and skills followed by core knowledge preparing you for advanced Data Science focused electives and a large individual project in Stage 3 and 4.

Your progression will be guided by active researchers in Computer Science. In particular those in:

Machine Learning
Pattern Recognition
Data Visualisation
High Performance Computing.

The culminating individual project will largely be conducted independently with appropriate academic supervision and, where appropriate, in collaboration with industrial partners.

The programme is delivered and assessed through a combination of:

lectures (including programmed student activity)
supervised tutorials
supervised laboratory work
independent coursework
group project work
and individual project work.

Taught modules are usually delivered through a series of 20 hours of lectures and 10 hours of tutorials/laboratory sessions. You are expected to undertake independent study and complete substantial coursework assignments for each module, amounting approximately to 120 hours per module.

The coursework includes programs, theoretical work, and essays, and is primarily formative. Project work plays an important part in computing undergraduate programmes. The Team Project provides you with experience of the issues involved in software development projects as well as enhancing team-working and related transferable skills.

In the Individual Project you are expected to carry an independent investigation of a significant computing problem allowing you to apply what you've learnt through the programme.

Our academic staff will supervise you through a series of supervision sessions to solve a real world problem or develop techniques that primarily involve the analysis of using complex data from industry, academia or government.

Assessment methods

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.

Percentage of the course assessed by coursework

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.

Assessment

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%

Overall workload

Most contact hours will take the form of lectures, interactive sessions, practical workshops and small group classes, supported by a personal tutorial system. The number will decrease as you progress and you become more able to direct your own learning.

Approximate study time based on 2019/20 entry is as follows:

Workload

Year 1

Scheduled learning: 20%

Independent learning: 80%

Year 2

Scheduled learning: 18%

Independent learning: 82%

Year 3

Scheduled learning: 21%

Independent learning: 79%

Year 4

Scheduled learning: 15%

Independent learning: 85%

Fees and funding

Fees for year 2022/23

To be confirmed

Overseas/EU

To be confirmed

The fees indicated are for undergraduate entry in the 2022/ 23 academic year only. Fees for future years may be subject to an inflationary increase, which is normally 2%.

Funding options

Explore up-to-date information about funding options, available financial support and typical living costs.

Fee waivers are available for this course.
Means tested support is available for 2022/23 entry.

Future finance loans

Future Finance offers students loans of between £2,500 and £40,000 to help cover tuition fees and living expenses. All students and courses are considered. All loans are subject to credit checks and approval for further details please visit our finance pages.

Additional expenses

Some of our degrees may involve additional expenses which are not covered by your tuition fees. Find out more about additional expenses.

Career

Our graduates find roles in data science, data analysis and visualisation in a variety of sectors, including:

health
retail
creative industries
security
transport
energy
government.

We have internships and industrial links with several partner organisations. We also host ‘Data Bites’ a series of talks on data topics, which regularly features presentations from employers in the data science market. This exposes our students to employers during their studies, and helps our graduates find employment.

Graduates starting a new business can benefit from City’s London City Incubator and our links to Tech City.

Placement support

The School has its own dedicated placements team that provides on-hand placement and internship support and guidance for you throughout your studies. You can undertake one of the following placement schemes:

One-year placement - these are taken after you have successfully completed your third year of studies; students work between nine and fifteen months on placement before returning to City to complete their final year
Summer internships - these can be taken every summer on completion of the academic year.

Our dedicated team of placement specialists, the Professional Liaison Unit, has extensive experience in helping students to secure placement employment across a range of industries. The team also has longstanding relationships with an impressive and diverse range of companies spanning both large and small organisations including start-ups in Tech City and in particular organisations that are active in the big data and data science area such as:

NHS
Facebook UK
Amazon UK
BBC
Tableau Software
Google
Microsoft
Cancer Research UK.

Placements are highly encouraged at City. Students that complete a placement year benefit from gaining professional experience working on real-life projects and are also more likely to achieve higher grades, secure a graduate-level job and earn a higher salary.

In recent years students from our computer science courses have been able to obtain placements at leading companies within their chosen field such as:

Amazon
AT&T
BBC
Facebook
Google
Oracle
Unilever.

How to apply

Applications for degree courses must be made through the Universities and Colleges Admissions Service (UCAS).

Apply now

You can apply through your school or college using the Apply system, which enables you to submit your application directly to the UCAS website.

You can apply to up to five universities or institutions on the form. The UCAS code for City, University of London is C60.

Please take care to enter the correct course code when applying, particularly for subjects with a Foundation year or with BEng (Hons) and MEng (Hons) or BSc (Hons) and MSci (Hons) options.

UCAS has implemented an 'invisibility of choices' policy so that, on the initial application and while you are receiving decisions, each institution can see only their entry and not those of other institutions you have chosen. This ensures that your application for a course at City is considered solely on your academic and personal qualities.

You should submit your completed application form to UCAS with a £26.50 application fee. If you want to apply to City, University of London only, you can make a single choice application at a reduced rate of £22.

For enquiries about the admissions process at City, please contact our Admissions Office

Complete the Admissions enquiry form.

Call: +44 (0)20 7040 8716.

When to apply

Your application for entry in September 2022 should arrive at UCAS between September 2021 and 26^th January 2022. Applications that arrive after 26^th January 2022 will be considered only at City's discretion.

Contacting UCAS

Website: www.ucas.com

Address: Universities and Colleges Admissions Service (UCAS), Rosehill, New Barn Lane, Cheltenham, Gloucestershire, GL52 3LZ

Telephone:

from inside the UK 0871 468 0468
from outside the UK +44 (0)871 468 0468

For callers with hearing difficulties:

from inside the UK use the Text Relay service on 18001 0871 468 0468
from outside the UK dial +44 151 494 1260 (text phone) and then ask the operator to dial 0871 468 0468.

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Key information

Entry requirements

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