BSc (Econ)(Hons) Economics, Finance and Management
Spanning three years, with options to extend to four or five years to include a year ab...
MSc Big Data Science
Mile End
INTAKE: September
Program Overview
The MSc Big Data Science program at Queen Mary caters to individuals with a strong quantitative background, often in fields like mathematics, statistics, computer science, or engineering. The program aims to provide students with a deep understanding of the principles and techniques involved in handling and analyzing large datasets. It emphasizes both theoretical understanding and practical application, equipping students with the tools and techniques necessary to extract meaningful insights from big data.
Curriculum: The curriculum is structured to provide a comprehensive understanding of the big data landscape. It typically covers core topics such as statistical modeling, machine learning, data mining, database management, distributed computing, and data visualization. Students learn how to collect, clean, process, and analyze large datasets using various tools and techniques. The program often includes opportunities for students to specialize in specific areas of interest, such as machine learning, cloud computing, or data visualization. A significant component of the program is often a substantial project or dissertation, allowing students to apply their knowledge to a real-world problem.
Research Focus: Queen Mary University of London has a strong research focus in areas related to big data, including machine learning, artificial intelligence, and data analytics. The MSc Big Data Science program benefits from this research expertise, with many modules taught by leading researchers in their respective fields. Students have opportunities to engage with ongoing research projects, potentially through research-based dissertations, allowing them to contribute to cutting-edge advancements in data science.
Industry Engagement: The program fosters strong links with industry through collaborations with tech companies, offering students opportunities for internships, placements, and real-world project work. These interactions provide valuable experience and insights into the practical application of big data science in industry settings. The university's location in London, a global tech and financial hub, further enhances industry engagement opportunities, providing access to a wide range of organizations working with big data.
Global Perspective: The field of big data science is inherently global, and Queen Mary's program reflects this. The program attracts students from diverse backgrounds, fostering a multicultural learning environment. Furthermore, the curriculum often explores the global impact of big data and the ethical and societal considerations surrounding its use in various contexts worldwide.
Location
Mile End
Score
IELTS 6.5
Tuition Fee
£ 28950
Postgraduate Entry Requirements:
- Applicants should have successfully completed a bachelor's degree or its equivalent from a recognized institution with a minimum overall score of 60% or equivalent.
- English language proficiency is required, and applicants must provide evidence of their English language skills through an approved language test.
- IELTS: A minimum overall score of 6.5 with no individual component below 6.0.
- TOEFL: A minimum overall score of 92, with at least with at least 17 in Listening, 18 in Reading, 20 in Speaking, and 21 in Writing.
- PTE Academic: A minimum overall score of 71 with 65 in Writing, and 59 in Reading, Listening and Speaking..
- Some postgraduate programs may have specific subject prerequisites or additional requirements.
Students must provide:
- academic marksheets & transcripts
- letters of recommendation
- a personal statement - SOP
- passport
- other supporting documents as required by the university.
Work experience: Some postgraduate courses may require relevant work experience in the field.
It is important to note that meeting the minimum entry requirements does not guarantee admission, as the university considers factors such as availability of places and competition for the program. Additionally, some courses may have higher entry requirements or additional selection criteria, such as interviews or portfolio submissions.
Queen Mary University of London offers a range of scholarships and bursaries to its students.
Queen Mary International Excellence Scholarships: A scholarship program for international undergraduate and postgraduate students who have an offer of admission from Queen Mary University of London and have demonstrated academic excellence. The scholarship covers full tuition fees for one year of study.
Queen Mary Law Scholarships: A scholarship program for undergraduate and postgraduate law students who have an offer of admission from Queen Mary University of London and have demonstrated academic excellence. The scholarship covers full or partial tuition fees, depending on the level of academic achievement.
Queen Mary Engineering and Materials Science Scholarships: A scholarship program for undergraduate and postgraduate students studying engineering or materials science who have an offer of admission from Queen Mary University of London and have demonstrated academic excellence. The scholarship covers full or partial tuition fees, depending on the level of academic achievement.
Graduates of the MSc Big Data Science program have exciting career opportunities in diverse sectors.
Data Scientist: Graduates can work as data scientists, applying their skills to analyze and interpret data for informed decision-making.
Big Data Analyst: Graduates may pursue roles as big data analysts, responsible for extracting valuable insights from large datasets.
Data Engineer: Graduates can become data engineers, developing and maintaining data infrastructure and pipelines.
Business Intelligence Analyst: Graduates with expertise in big data can work as business intelligence analysts, helping businesses make strategic decisions based on data-driven insights.
Data Consultant: Graduates may work as data consultants, providing expertise and solutions to organizations seeking to leverage their data.
Research Scientist: Graduates can embark on research careers, exploring new methods and techniques in the field of big data science.
