Big Data and Digital Futures (MSc)
12 monthsProgramme duration
29,340 GBPTuition Fee/year
SepStarting Month
Programme overview
Main Subject
Data Science and Artificial Intelligence
Degree
MSc
Study Level
Masters
Study Mode
On Campus
This degree responds directly to the growing demand from employers within industry, governments and the non-profit sector as well as across research fields for a new generation of postgraduates who can critically engage with big data theoretically, methodologically and practically. In contrast to many big data-focused degrees (such as Data Science or Data Analytics) where the emphasis is almost exclusively on data practices and computational tools, this degree underpins key practical skills with a range of theoretical approaches to data.
How is our world influenced by big data? How are our lives represented in big data? This course will enable you, whatever your disciplinary background, to understand and act in a society transformed by data, networks and computation and develop a range of interdisciplinary capacities.
Our course offers you:
How is our world influenced by big data? How are our lives represented in big data? This course will enable you, whatever your disciplinary background, to understand and act in a society transformed by data, networks and computation and develop a range of interdisciplinary capacities.
Our course offers you:
- Core knowledge in statistical modelling and programming for data-driven careers
- An extensive understanding of the relationship between big data technology and society
- Practical and critical application of these techniques to cutting-edge methods across the data spectrum
- Python and R programming skills (using infrastructure such as Jupyter Notebooks and Posit)
- Introductory Data Science and Machine Learning/AI techniques, including Generative AI
- Statistics in Social Science (up to multiple linear regression and logistic regression)
- Advanced Statistics (generalised linear models, multilevel modelling and casual inference)
- Basics in Social Network Analysis, Web Scraping, Reproducible Analysis, Data Visualisation, SQL, Deep Learning, Agent-Based Modelling
- Writing and communication skills for analysis/discussing technical content
- Critical academic research skills with an interdisciplinary focus
Programme overview
Main Subject
Data Science and Artificial Intelligence
Degree
MSc
Study Level
Masters
Study Mode
On Campus
This degree responds directly to the growing demand from employers within industry, governments and the non-profit sector as well as across research fields for a new generation of postgraduates who can critically engage with big data theoretically, methodologically and practically. In contrast to many big data-focused degrees (such as Data Science or Data Analytics) where the emphasis is almost exclusively on data practices and computational tools, this degree underpins key practical skills with a range of theoretical approaches to data.
How is our world influenced by big data? How are our lives represented in big data? This course will enable you, whatever your disciplinary background, to understand and act in a society transformed by data, networks and computation and develop a range of interdisciplinary capacities.
Our course offers you:
How is our world influenced by big data? How are our lives represented in big data? This course will enable you, whatever your disciplinary background, to understand and act in a society transformed by data, networks and computation and develop a range of interdisciplinary capacities.
Our course offers you:
- Core knowledge in statistical modelling and programming for data-driven careers
- An extensive understanding of the relationship between big data technology and society
- Practical and critical application of these techniques to cutting-edge methods across the data spectrum
- Python and R programming skills (using infrastructure such as Jupyter Notebooks and Posit)
- Introductory Data Science and Machine Learning/AI techniques, including Generative AI
- Statistics in Social Science (up to multiple linear regression and logistic regression)
- Advanced Statistics (generalised linear models, multilevel modelling and casual inference)
- Basics in Social Network Analysis, Web Scraping, Reproducible Analysis, Data Visualisation, SQL, Deep Learning, Agent-Based Modelling
- Writing and communication skills for analysis/discussing technical content
- Critical academic research skills with an interdisciplinary focus
Admission Requirements
7+
130+
100+
70+
2:1 undergraduate degree (or equivalent).
1 Year
Sep
- Candidates are required to submit references or letter(s) of recommendation for acceptance
Tuition fees
Domestic
11,840 GBP
International
29,340 GBP
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