BIO09096 2021 Bio-Industry Data & Digital Technologies
Full Title
Bio-Industry Data & Digital Technologies
Transcript Title
Bio-Industry Data & Digital Te
Code
BIO09096
Attendance
N/A %
Subject Area
BIO - Bio Tech/Eng/Chem
Department
LIFE - Life Sciences
Level
09 - NFQ Level 9
Credit
05 - 05 Credits
Duration
Semester
Fee
€
Start Term
2021 - Full Academic Year 2021-22
End Term
9999 - The End of Time
Author(s)
Mary Butler
Programme Membership
SG_SBIOI_E09 202100 Postgraduate Certificate in BioIndustry 4.0
SG_SVALI_O09 202300 Postgraduate Diploma in Science in Validation and Digitalisation Technologies
SG_SVALI_M09 202300 Master of Science in Validation and Digitalisation Technologies
SG_SBIOI_M09 202300 Master of Science in BioIndustry 4.0
Description
This module aims to provide students with a detailed knowledge and understanding of the future needs of the biopharmaceutical sector developing data literacy and skills vital for ensuring that biopharma organisations can transform large, complex, disconnect data sets into tangible business assets.
Learning Outcomes
On completion of this module the learner will/should be able to;
1.
Demonstrate a knowledge of the theories, techniques and models for data identification, acquisition, cleaning, and aggregation of the range of biopharma data types
2.
Critically assess a range of BioPharma data management systems architectures (ERP, MES, LIMS) in terms of their suitability to acquire, process and manage large data collections
3.
Evaluate the challenges with data integrity, storage, security, management, cybersecurity, and data ethics
4.
Research and assess a range of architecture models considering future positive impact on the biopharma business. Assessment of company requirements for implementation of architectural models.
5.
Prepare data for use in industry 4.0 analytical use cases to support data driven business insights. Overview of key trends in the application of data and analytics to pharma analytics use cases, including business continuity.
Teaching and Learning Strategies
This module will be taught using on‑line lectures. A range of Computer‑Aided Learning (CAL) packages are also used to support this module (e.g. Moodle,Adobe Connect, Panopto, Camtasia). Students are provided with electronic materials for self‑assessment and preparation for assessments/assignments. Self‑directed, student‑centred, independent learning is a core aspect through completion of module coursework.
Module Assessment Strategies
The assessment approach for this module will be 100% continuous employed including some of the following: Enquiry based Projects, Assignments/mini projects including Viva/Presentation Short-form assessment exams incl. MCQs, Short answer and Long Answer Questions.
Repeat Assessments
Students will have opportunities to re-submit work as agreed with their lecturer.
Indicative Syllabus
The following is a summary of the main topics included: types of big data, data acquisition, data warehousing, BioPharma data management systems architectures
Coursework & Assessment Breakdown
Coursework & Continuous Assessment
100 %
Coursework Assessment
| Title | Type | Form | Percent | Week | Learning Outcomes Assessed | |
|---|---|---|---|---|---|---|
| 1 | MCQ 1 | Coursework Assessment | Multiple Choice/Short Answer Test | 20 % | Week 8 | 1,2,3 |
| 2 | End of Semester Assessment | Coursework Assessment | Assignment | 30 % | Week 11 | 1,2,3,4,5 |
| 3 | Project & Viva | Project | Closed Book Exam | 50 % | Week 12 | 1,2,3,4,5 |
Part Time Mode Workload
| Type | Location | Description | Hours | Frequency | Avg Workload |
|---|---|---|---|---|---|
| Lecture | Online | Lecture | 2 | Weekly | 2.00 |
| Independent Learning | Not Specified | Self Study | 5 | Weekly | 5.00 |
Total Part Time Average Weekly Learner Contact Time 2.00 Hours
Module Resources
Non ISBN Literary Resources
Data Science for Business: What you need to know about data mining and data analytic thinking. Foster Provost & Tom Fawcett.
McKinsey & Company: An executive primer on artificial general intelligence. Federico Berruti, Pieter Nel and Rob Whiteman.
Ten Red Flags Signalling your Analytics Programme will Fail: Oliver Fleming, Tim
Fountaine, Nicolaus Henke and Tamim Saleh.
The New Moats: Why Systems of Intelligence are the Next Defensible Business Model. Jerry Chen.
A business analytics approach to augment six sigma problem solving: A biopharmaceutical manufacturing case study. Will Fahey, Paul Jeffers & Paula
Carroll.
Updated Literary Resources
Journal Resources
URL Resources
Other Resources
None
Additional Information