Last modified: 23 Jul 2024 11:02
Data Science is an interdisciplinary field that seeks to identify and understand phenomena captured in structured or unstructured data, extract insights, and add value by generating predictions that aid optimization of processes and equipment. These techniques show considerable promise for bringing about a revolution, increasing the significance and value of owning and collecting data of all types. This course introduces the common techniques and considers the implications for data managers.
| Study Type | Postgraduate | Level | 5 |
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| Term | Second Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
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Data Science, short for data-driven science, is an interdisciplinary field that seeks to identify and understand phenomena as captured in structured or unstructured data, extract knowledge or insights from these data, and add value to the data by generating predictions or recommendations that aid optimization of processes, workflows, and equipment usage. Spanning broad areas of information and computational science, mathematics, and statistics, these techniques are showing considerable promise for bringing about a revolution in the uses to which data can be put, solving the challenges of handling exceptionally large datasets, and increasing the significance and value of owning and collecting data of all types.
This course will introduce the subject by explaining the commonly employed techniques, demonstrating with examples the benefits they might generate for a business, and considering the implications for those managing data if the full benefits are to be achieved. Topics to be covered will include: data analytics and data mining; challenges and solutions for large datasets (“Big Data”) and real-time analysis; machine-learning, artificial intelligence, neural networks, and the importance of training data; classification, pattern-recognition and cluster analysis; probabilistic analysis and uncertainty quantification; visualisation; and data integration into models for making predictions and recommendations that aid automation and optimization.
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 50 | |
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| Assessment Weeks | Feedback Weeks | |||
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| Knowledge Level | Thinking Skill | Outcome |
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| Assessment Type | Summative | Weighting | 50 | |
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| Assessment Weeks | Feedback Weeks | |||
| Feedback | ||||
| Knowledge Level | Thinking Skill | Outcome |
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There are no assessments for this course.
| Assessment Type | Summative | Weighting | ||
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| Assessment Weeks | Feedback Weeks | |||
| Feedback | ||||
| Knowledge Level | Thinking Skill | Outcome |
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| Knowledge Level | Thinking Skill | Outcome |
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| Procedural | Create | Perform prediction based on statistical tools such as regression, classification, and clustering |
| Procedural | Analyse | Conduct exploratory data analysis to generate hypotheses and intuition about the data |
| Procedural | Apply | Perform data munging/scraping/sampling/cleaning in order to get an informative, manageable data set |
| Procedural | Analyse | Communicate results through visualisation, stories, and interpretable summaries |
| Conceptual | Understand | Describe the typical workflow of data analysis |
| Procedural | Apply | Store and manage data in order to be able to access data - especially big data - quickly and reliably during subsequent analyses. |
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