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CHEN40770

Academic Year 2024/2025

Data Sci for Biopharm Manufact (CHEN40770)

Subject:
Chemical Engineering
College:
Engineering & Architecture
School:
Chem & Bioprocess Engineering
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Colin Clarke
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

Modern biopharmaceutical manufacturing plants produce data at a staggering rate with information from each stage of the production process captured, in some cases, in near real-time. BioPharma companies are increasingly utilising “big data” approaches to enable rapid access and visualisation of these data as well as the application of complex statistical analyses to gain new process knowledge and increase the efficiency of their manufacturing operations. In this module, students will gain an understanding of data analytics system architecture and the advantages over traditional relational databases. In addition, students will become familiar with a variety of univariate and multivariate statistics analyses used to study bioprocess data. Students will also learn to utilise the R statistical software environment and construct a dashboard for data visualisation.

About this Module

Learning Outcomes:

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 Hours:
Student Effort Type Hours
Lectures

24
Autonomous Student Learning

96
Total

120

Approaches to Teaching and Learning:
Lectures
Problem based learning
Autonomous student learning

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Practical Skills Assessment: Short excercise Week 2 Other No

10

 No
Practical Skills Assessment: Short exercise Week 3 Other No

10

 No
Practical Skills Assessment: Short excercise Week 4 Other No

10

 No
Practical Skills Assessment: Short excercise Week 5 Other No

10

 No
Individual Project: A project to design a Shiny Dashboard for biopharmaceutical manufacturing Week 12 Other No

60

 No

Carry forward of passed components
No
 

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

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