Big Data Science

Big Data Science at Queen Mary University of London is an innovative and comprehensive programme designed to equip students with the essential skills and knowledge required to analyze, interpret, and harness the power of large and complex data sets. As data continues to grow exponentially across industries, the demand for professionals who can develop advanced data analysis techniques, implement scalable computing solutions, and drive data-informed decision-making is higher than ever. This programme combines theoretical foundations in statistics, computer science, and data engineering with practical, hands-on experience using the latest tools and technologies in the field. Students will learn how to design and build data models, apply machine learning algorithms, and develop data-driven solutions that address real-world problems across sectors such as finance, healthcare, technology, and public policy.

The curriculum covers core topics including data mining, big data architecture, distributed computing, data visualization, and ethical considerations in data science. Students will have opportunities to work on industry-relevant projects, collaborate with experts, and gain practical experience using platforms like Hadoop, Spark, Python, R, and SQL. The programme also emphasizes the importance of security, privacy, and ethical use of data to prepare graduates for responsible leadership roles in the data science landscape. With a strong focus on employability, Queen Mary’s Big Data Science programme provides networking opportunities, career support, and pathways to further research or industry positions. Upon graduation, students will be well-equipped to pursue careers as data analysts, data scientists, machine learning engineers, or continue to advanced postgraduate study. Recognized for its academic excellence, this programme prepares students to become innovative professionals capable of transforming data into actionable insights that can influence business strategies and societal progress.

The programme is organised in three semesters. The first semester is composed by three core modules plus one optional module that cover the foundational techniques and tools employed for Big Data Science analysis.

The second semester has four modules that are chosen among a set of options. The module selection allows students to focus on domain-specific research or industry applications for Big Data Science.  Module options allow students to specialize in several areas: Computer Vision, Internet Services (Semantic Web and Social Media), Business, and Internet of Things.

Students carry out a large project full time in the third semester, after agreeing to a topic and supervisor in the first semester, and completing the preparation phase over the second semester.

Undertaking a masters programme is a serious commitment, with weekly contact hours being in addition to numerous hours of independent learning and research needed to progress at the required level. When coursework or examination deadlines are approaching independent learning hours may need to increase significantly.

Core modules

• Big Data Processing
• Data Mining
• Applied Statistics
• MSc Project

Option modules

• Advanced Program Design
• Advanced Database System Technology
• Sensors and the Internet of Things
• Business Technology Strategy
• Techniques for Computer Vision
• The Semantic Web
• Information Retrieval
• Digital Media and Social Networks
• Machine Learning
• Introduction to Computer Vision

Requirements

• An upper second class degree is normally required, usually in electronic engineering, computer science, maths or a related discipline. Students with a good lower second class degree may be considered on an individual basis. Applicants with unrelated degrees will be considered if there is evidence of equivalent industrial experience.
• For international students we require English language qualifications IELTS 6.5 or TOEFL 92 (internet based).
• Degree transcripts. Please provide a transcript of your degree(s). If you have not yet completed your degree please provide a transcript of your results achieved to date. 
• If your degree was from an overseas institution, you should supply a transcript of your marks for each year of your studies and a copy of your degree certificate together with a certified translation if the document is not in English. Please note that original documentation will be required before you enrol. International applicants are also advised to include high school transcripts
• Please provide the contact details of two referees, at least one reference must be from an academic referee who is in a position to comment on the standard of your academic work and suitability for postgraduate level study. Where appropriate, a second referee can provide comment on your professional experience.
• Curriculum Vitae (CV)/ Resume
• Statement of purpose. Your statement of purpose should explain why you want to study your chosen programme and how it will help your life and career. This should typically be one side of A4 paper.

The Big Data Science program at Queen Mary University of London offers a range of financing options to support students throughout their studies. Tuition fees vary depending on the student's residence status, with domestic students benefiting from the UK's student finance system, which may include government loans for undergraduate studies or scholarships for postgraduate taught degrees. International students are responsible for paying the full international tuition fee, which is set annually and may be higher than domestic rates. Queen Mary University of London provides a variety of scholarships and bursaries aimed at both home and international students, recognizing academic excellence and financial need. These include merit-based scholarships awarded upon application, as well as departmental bursaries that can help offset tuition and living expenses. Additionally, students can explore external funding opportunities, such as government grants, private scholarships, and sponsorship programs from corporations and organizations supporting students in data science fields. The university also offers guidance on student loans and financing plans, emphasizing accessible payment options to ease financial burdens. For postgraduate students, there are potential part-time work opportunities, including research assistantships and administrative roles, which can supplement income while gaining valuable experience. Students are encouraged to carefully review the specific costs associated with the Big Data Science program and to apply early for available funding opportunities. Financial aid options are regularly updated, and applicants are advised to check the university’s official website for the most current information. Overall, Queen Mary University of London aims to make the Big Data Science program accessible through a combination of tuition support, scholarships, external funding, and part-time employment opportunities, ensuring that students can focus on their academic and research pursuits without undue financial stress.

Big Data Science at Queen Mary University of London is a comprehensive postgraduate program designed to equip students with the necessary skills and knowledge to analyze and interpret large datasets. The course combines theoretical foundations with practical applications, enabling graduates to address real-world challenges across various industries, including finance, healthcare, technology, and government. students will learn about advanced data management techniques, machine learning algorithms, statistical analysis, and data visualization. Emphasis is placed on developing programming skills in languages such as Python and R, as well as understanding the ethical and social implications of data science. The program's structure typically includes core modules that cover foundational topics and elective modules allowing students to specialize in areas like artificial intelligence, data security, or computational modelling. Queen Mary University of London boasts state-of-the-art facilities, including dedicated data science labs and access to high-performance computing resources, facilitating a hands-on learning environment. The program also encourages collaborative projects and internships with industry partners, providing valuable work experience and networking opportunities. Graduates of Big Data Science are well-prepared for careers in data analysis, data engineering, research, and consultancy, with the potential to work in multidisciplinary teams on innovative data-driven solutions. The program is suitable for students with backgrounds in computer science, mathematics, statistics, or related fields who wish to deepen their expertise in big data analytics and its applications in various sectors. Admission requirements typically include a relevant undergraduate degree, proficiency in programming and statistics, and a motivation to work in the fast-growing field of data science. The course duration is usually one year for full-time students and up to two years for part-time students. Queen Mary University of London is renowned for its research contributions and vibrant academic community, making it an ideal place for aspiring data scientists to develop their careers.