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Curricular information is subject to change
1. Describe the current state of the art in data analytics for biopharmaceutical manufacturing
2. Explain the architecture of a data analytics system.
3. Describe commonly utilised multivariate statistics such as principal components analysis and recognize the appropriate application of these techniques.
4. Understand how machine learning works and how to avoid the pitfalls commonly encountered during the construction of prediction models.
5. Conduct a range of statistical analyses of bioprocess data using the R statistical computing environment.
6. Produce well documented R code and use GitHub version control.
7. Develop a data analytics dashboard in R using the Shiny package.
Student Effort Type | Hours |
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Lectures | 0 |
Total | 0 |
Not applicable to this module.
Description | Timing | Component Scale | % of Final Grade | ||
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Continuous Assessment: Assessment on this module will comprise of 4 short exercises followed by a larger project | Throughout the Trimester | n/a | Standard conversion grade scale 40% | Yes | 100 |
Remediation Type | Remediation Timing |
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In-Module Resit | Prior to relevant Programme Exam Board |
• Feedback individually to students, post-assessment
Not yet recorded.