Data Science (BSc Hons)

This three-year course provides a solid foundation in mathematics and statistics, the core of data science theory and practice. You'll explore modern mathematics and relevant topics, applying your skills to real-world problems across various fields. Develop practical abilities to analyse, solve, and evaluate data-heavy challenges – vital for employment and further studies. 

Customise your learning with optional modules in different data science applications, tailoring the undergraduate course to your interests and aspirations. See how your existing skills are enhanced by the knowledge you gain. Graduates pursue careers in private and public companies, government, and NGOs.

Explore our course, shaping mathematically inclined students into skilled data scientists for diverse careers or specialised studies in data science, data analytics, and related fields.

Our Data Science undergraduate course is designed to transform students with a strong mathematics background into accomplished professionals, ready to enter the growing field or pursue further specialisations.

What you will study
Year 1
Students are required to study the following compulsory modules.

• Vectors and Matrices (15 credits)
• Mathematical Coding (15 credits)
• Calculus and Mathematical Analysis (30 credits)
• Discrete Mathematics (15 credits)
• Our Mathematical World (15 credits)
• Analysis of Data (15 credits)
• Probability and Randomness (15 credits)

Year 2
Students are required to study the following compulsory modules.

• Applications in AI and Data Science (15 credits)
• Big Data Analysis and Visualisation (15 credits)
• Linear Algebra and Differential Equations (30 credits)
• Operational Research: Linear Programming (15 credits)
• Vector Calculus (15 credits)
• Statistical Data Analysis and Time Series (30 credits)

Year 3
Students are required to study the following compulsory modules.

• Machine Learning (15 credits)
• Responsible Artificial Intelligence (15 credits)
• Optimisation Techniques (15 credits)
• Advanced Optimisation Techniques (15 credits)
• Linear Models and Statistical Inference (15 credits)

Students are required to choose 30 credits from this list of options.

• Research Methods and Mathematics Project (30 credits)
• Mathematics Work Placement (30 credits)

Students are required to choose 15 credits from this list of options.

• Financial Time Series (15 credits)
• Graph Theory and Applications (15 credits)
• Bayesian Methods and Their Applications (15 credits)

What you should know about this course

• Study a course with a heightened focus on mathematical and statistical aspect of the data science field.
• You will learn advanced programming, machine learning and artificial intelligence.
• This course is specifically aimed at providing you with a wider knowledge, expertise, skills and understanding of the fast growing data science field.

Teaching

• Learning takes place through a combination of timetabled learning and independent study.
• You can view more information about how each module is taught within our 'What you will study' section.
• Seminars and workshops enable you to discuss and develop your understanding of topics covered in lectures in smaller groups. You will also be able to meet your personal tutor. Timetabled learning may fall between 9am and 9pm depending on your courses and tutorials.

Class sizes

• The typical student intake per year of Data Science courses averages 80-100. Lectures are typically attended in large groups and labs/tutorials by smaller groups. This can vary more widely for modules that are shared between degree courses.

Independent learning

• Outside of timetabled sessions, you are also expected to dedicate time (around 24 hours per week) to self-study. This may involve further reading and research, preparing coursework and presentations, and preparing for tests and exams. Our facilities are designed to support you in these activities.
• You can use Stockwell Street library and online resources to support further reading and research.

Overall workload

• If you are studying full-time, you should expect the workload to be similar to a full-time job. For part-time students, this will reduce in proportion with the number of courses you are studying.
• Each module you study towards this degree is worth 15 or 30 credits. These represent around 150 or 300 study hours respectively. If you receive 72 contact hours for a 30-credit module, you should expect to commit 228 hours to independent study to complete it successfully.

In addition, during the week you can also:

• Attend additional support classes in some modules
• Attend guest lectures from industry experts
• Take part in employability and enterprise workshops
• Join student societies.

Assessment

• You can view how each module is assessed within our 'What you will study' section.
• Each course has formal assessments which count towards your grade. Some courses may also include 'practice' assignments, which help you monitor progress and do not count towards your final grade.

Feedback summary

• We aim to give feedback on assignments within 15 working days.
• Dates and timetables
• The academic year runs from September to June.
• Full teaching timetables are not usually available until term has started. For any queries, please use our contact form.

Careers and placements
Will I have a work placement?

• This course can be taken in sandwich mode, which means you can take a year to work in industry between your second and final years of study. Sandwich placements are relevant to your degree subject and are paid roles. It is the very best way of preparing you for successfully finding a job quickly when you graduate.
• You will also have the option to select a module for which you will be required to undertake a short-term placement. The module will be assessed on your reflection on this placement and how you can apply your knowledge to the workplace. Many placements are found through our network of industry contacts, and students are supported in securing these.
• For those students who are interested in pursuing a career in teaching you also have the option to select a module for which you will be required to undertake a short-term placement in a school or college.

How long is my placement?

• Sandwich placements last for between 9-13 months. Work-based learning module placements are normally one day a week for either one or two terms depending on the number of credits available from the module.

What are the financial arrangements?

• Sandwich placements are paid roles whereas work-based learning module placements are normally unpaid.

What sort of careers do graduates pursue?

• Students from Data Sciences have secured placement years at the Department for Work & Pensions, NHS England, NATS, Home Office, HMRC, Department of Health, GSK, Intel and Lloyds Banking Group.
• Career opportunities for this course are varied. Most industries are always on the lookout for graduates with intellectual, mathematical and numerical skills that can be applied to a wide range of practical situations.
• You may choose to work in a variety of fields such as statistical modelling, operational research, research, internet technology, the pharmaceutical industry, accounting, banking, the Civil Service, teaching, logistics, consultancy, actuarial science, risk management and marketing.

Are internships available?

• Students are encouraged to take up Summer internships during the Summer holidays, though it is up to the student to find them. Support is available to students from the Employability and Careers Service when applying for placements and internships.

Do you provide employability services?

• Employability activities take place all the time at Greenwich and students are encouraged to take part in as many opportunities as possible. The central Employability and Careers Service provides support for students preparing to apply for placements and graduate roles, such as CV clinics, mock interviews and employability skills workshops. In addition, your School has a dedicated Employability Officer who will be organising work-related activities throughout the year which will help you to build you industry knowledge and networks.