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Queen Mary University of London: Big Data Science
| Institution | Queen Mary University of London View institution profile |
|---|---|
| Department | Electronic Engineering and Computer Science |
| Web | https://www.qmul.ac.uk/ |
| eecs-msc-enquiries@qmul.ac.uk | |
| Telephone | +44 (0)20 7882 7333 |
| Study type | Taught |
Summary
The Big Data science movement is transforming how Internet companies and researchers over the world address traditional problems. Big Data refers to the ability of exploiting the massive amounts of unstructured data that is generated continuously by companies, users, devices, and extract key understanding from it. A Data Scientist is a highly skilled professional, who is able to combine state of the art computer science techniques for processing massive amounts of data with modern methods of statistical analysis to extract understanding from massive amounts of data and create new services that are based on mining the knowledge behind the data. The job market is currently in shortage of trained professionals with that set of skills, and the demand is expected to increase significantly over the following years.
If you are looking to pursue a career as a data scientist, this programme is designed for you. You will cover the fundamental statistical (e.g. machine learning) and technological tools (e.g. cloud platforms, Hadoop) for large-scale data analysis.
The course leverages the world-leading expertise in research at Queen Mary with our strategic partnership with IBM and other leading IT sector companies to offer to students a foundational MSc on the field of Data Science.
The MSc modules cover the following aspects:
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Statistical Data Modelling, data visualization and prediction
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Machine Learning techniques for cluster detection, and automated classification
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Big Data Processing techniques for processing massive amounts of data
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Domain-specific techniques for applying Data Science to different domains: Computer Vision, Social Network Analysis, Bio Engineering, Intelligent Sensing and Internet of Things
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Use case-based projects that show the practical application of the skills in real industrial and research scenarios.
You will attend lectures that explain the core concepts, techniques and tools required for large-scale data analysis. Laboratory sessions and tutorials will put these elements to practice through the execution of use cases extracted from real domains. You will also undertake a large project where you will demonstrate the application of Data Science skills in a complex scenario.
The programme is offered by academics from the Networks, Centre for Intelligent Sensing, Risk and Information Management, Computer Vision and Cognitive Science research groups from the School of Electronic Engineering and Computer Science. This is a team of more than 100 researchers (academics, post-docs, research fellows and PhD students), performing world leading research in the fields of Intelligent Sensing, Network Analytics, Big Data Processing platforms, Machine Learning for Multimedia Pattern Recognition, Social Network Analysis, and Multimedia Indexing.
This course is also available for January 2021 entry. For details please visit the institution course page.
MSc
| Level | RQF Level 7 |
|---|---|
| Entry 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. |
| Location | Mile End Mile End Road Tower Hamlets E1 4NS |
| Level | RQF Level 7 |
|---|---|
| Entry 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. |
| Location | Mile End Mile End Road Tower Hamlets E1 4NS |
| Level | RQF Level 7 |
|---|---|
| Entry 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. |
| Location | Mile End Mile End Road Tower Hamlets E1 4NS |
| Level | RQF Level 7 |
|---|---|
| Entry 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. |
| Location | Mile End Mile End Road Tower Hamlets E1 4NS |
| Level | RQF Level 7 |
|---|---|
| Entry 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. |
| Location | Mile End Mile End Road Tower Hamlets E1 4NS |
| Level | RQF Level 7 |
|---|---|
| Entry 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. |
| Location | Mile End Mile End Road Tower Hamlets E1 4NS |
| Level | RQF Level 7 |
|---|---|
| Entry 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. |
| Location | Mile End Mile End Road Tower Hamlets E1 4NS |
| Level | RQF Level 7 |
|---|---|
| Entry 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. |
| Location | Mile End Mile End Road Tower Hamlets E1 4NS |
| Level | RQF Level 7 |
|---|---|
| Entry 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. |
| Location | Mile End Mile End Road Tower Hamlets E1 4NS |
Queen Mary University of London
Queen Mary University of London was established in 1785 and is a leading research-intensive university with a difference – one that opens the doors of opportunity to anyone and supporting students to succeed.
With schools split across five campuses in central and east London, students are able to select from an exceptionally broad range of subjects across three main faculties, including the Faculty of Humanities and Social Sciences, Faculty of Science and Engineering, and Faculty of Medicine and Dentistry.
Facts
- Queen Mary is a member of the Russell Group of universities in the UK, which …
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