FP041-15 Scientific Programming and Mathematical Modelling
Introductory description
FP041-15 Scientific Programming and Mathematical Modelling
Module aims
To develop an understanding of the basic principles of mathematical models and demonstrate basic competence in computer programming.
This is an interdisciplinary module which links Mathematics, Data Science, and Computer Science.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Unit 1. Introduction to data science and scientific programming i
Unit 2. Introduction to Mathematical Modelling
Unit 3. Basics of descriptive and inferential analysis
Unit 4. Statistics fundamentals
Unit 5. Modelling with regression models.
Unit 6. Modelling with classification models.
Unit 7. Applying data science models to real world problems.
Learning outcomes
By the end of the module, students should be able to:
- Critically observe a real-world problem and apply a mathematical model to provide insights and/or solutions.
- Demonstrate understanding of basic mathematical concepts in data science, relating to descriptive analysis, and inferential analysis, and regression and classification algorithms.
- Utilize a programming language such as Python to prepare data for analysis and build mathematical models.
- Produce a rigorous analytical report which considers a broad range of mathematical and statistical methods to describe and analyse a given dataset.
Indicative reading list
Bender, E.A., 2012. An introduction to mathematical modeling. Courier Corporation.
Hill, C., 2016. Learning scientific programming with Python. Cambridge University Press.
Langtangen, H.P. and Langtangen, H.P., 2009. A primer on scientific programming with Python (Vol. 2). Berlin, Germany: Springer.
View reading list on Talis Aspire
Interdisciplinary
This module has links between Mathematics, Data Science, and Computer Science.
Subject specific skills
Mathematical Skills
Analytical Skills
Problem-solving skills
Investigative Skills
IT Skills
Transferable skills
Mathematical Skills
Analytical Skills
Problem-solving skills
Communication Skills
Investigative Skills
IT Skills
Study time
| Type | Required |
|---|---|
| Seminars | 48 sessions of 1 hour (32%) |
| Private study | 72 hours (48%) |
| Assessment | 30 hours (20%) |
| Total | 150 hours |
Private study description
Private Study.
Costs
No further costs have been identified for this module.
You must pass all assessment components to pass the module.
Assessment group D
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
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| Case Study | 34% | 10 hours | Yes (extension) |
|
Analyse a data set using a board range of mathematical and Statistical Methods, producing an analytical report. |
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Reassessment component is the same |
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Assessment component |
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| Final Examination | 66% | 20 hours | No |
|
Final Examination - Testing all unit content.
|
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Reassessment component is the same |
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Feedback on assessment
Written feedback provided on Tabula
Courses
This module is Core for:
- Year 1 of FIOE Warwick International Foundation Programme