PH3S109 - Mathematics and Statistics for the Pharmaceutical Industry 01 Jan 2022 - 31 Aug 2028 | Version 1
Associated Module Information
| Module Code: | PH3S109 | ||
|---|---|---|---|
| Module Title: | Mathematics and Statistics for the Pharmaceutical Industry | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Applied Sciences | ||
| Faculty Sub Group: | Chemistry and Pharmaceutical Science | ||
| Module Leader: | Suzanna Kean | ||
| Module Team: | |||
| First Intended Intake: | SEP 2025 | Final Year of Intake: | |
| Date Closed: | |||
| Credit Value: | 20 | Credit Level: | 6 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 100400 - applied mathematics | ||
| HECOS Code Weighting: | 100 | ||
Document Version Information
| Version | 1 |
|---|---|
| Valid From | 01 Jan 2022 |
| Valid To | 31 Aug 2028 |
Module Aims
N/A
Content Summary
Indicative Syllabus:
Mathematics for the pharmaceutical Industry
Graph Analysis: Presenting data, chart types; linear graphs, calculating slope and intercept and relating to model equations; Arrhenius equation; calibration curves; non-linear graphs – applications to physical chemistry and analysis.
Calculus: Differentiation, integration; applications to kinetics, analysis and chromatography; differential equations, applications to formulation
Algebra: Simple algebraic manipulation; simplifying calculations with large amounts of experimental data; application to chemical analysis
Calculation of volumes, area, flow rates
Statistics
Basic Statistical methods: Mean, mode, median, standard deviation, variability, sampling, dealing with atypical results
Error Analysis: Accuracy and precision; quantification of experimental error.
Calibration and regression methods
Significance tests, CUSUM analysis, Simple analysis of variants (ANOVA), Sampling statistics, basic probability
Introduction to spreadsheets, graphing data, determination of slope, intercept, presentation of errors, non-linear graphs;
This module is delivered through open/distance learning resource packs, pre-recorded lectures, problem-solving exercises and case studies
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Independent Study | 100 |
| Directed Study | 70 |
| Formative Assessment - Independent | 30 |
| Total Hours Selected | 200 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | Manipulate simple algebraic functions, derive functions to manipulate large amounts of data, use appropriate graphical format to present data and derive physical constants and analyse non-linear curves |
| LO2 | Have a basic understanding of statistics, probability, and errors |
| LO3 | Apply the mathematical principles learned to case studies |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Asynchronous Assessment | Report 1 | Written assignment based on a series of questions on the module content | 0 | 3000 | 60 | No | 40 |
| Asynchronous Assessment | Case study 1 | Written assignment using the tools gained to analyse real life data | 0 | 2000 | 40 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | LO3 | |
| Report 1 | ✔ | ✔ | ✔ |
| Case study 1 | ✔ | ✔ | ✔ |