Big Data Analytics for Business courses

About Big Data Science With Industrial Experience Msc in Queen Mary University of London

You will cover the fundamental statistical (such as machine learning) and technological tools (such as cloud platforms, Hadoop) for large-scale data analysis.

 The MSc modules cover the following aspects:

• statistical data modelling, data visualisation and prediction
• machine learning techniques for cluster detection, and automated classification
• big data processing techniques for processing massive amounts of data
• domain-specific techniques for applying data science to different domains: computer vision, social network analysis, bioengineering, intelligent sensing and internet of things
• case-based projects that show the practical application of the skills in real industrial and research scenarios.

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. 

During your industry placement, you will learn to apply key technical knowledge and skills from your taught modules in the workplace; enhancing your employment potential and understanding where you need to improve. You will secure the placement yourself with support from the Placement Team. This module is only open to students enrolled on the programme of study with integrated placement

​​​Academic qualification equivalents

• A bachelor degree with related subject

English language requirements (one of the below):

• IELTS : 6.5 overall including 6.0 in Writing, and 5.5 in Reading, Listening and Speaking. 
• TOEFL IBT: 92 overall including 21 in Writing, 18 in Reading, 17 in Listening and 20 in Speaking.
• PTE: 62 overall including 57 in Writing, and 51 in Reading, Listening and Speaking.

Queen Mary University Of London Highlights

Queen Mary University of London Average Tuition Fees And Other Expenses

The cost of attendance for the 2020/2021 academic session is presented on the table below

Note: Fee payment in installments is available

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