Mathematics & Statistics - Computational Science
Most problems in Applied Mathematics are modelled using a set of equations that can be written down but cannot be solved analytically.
Duration
Autumn Trimester - September to December
MODE OF DELIVERY:Face-to-Face
Eligibility
Learning Recommendations:
Students are recommended to have successfully completed level one courses in calculus and algebra, along the lines of the following module pairs:
1. MATH10300 - Calculus in the Mathematical Sciences and MATH10270 - Linear Algebra in the Mathematical Sciences
2. MATH10330 - Calculus in the Phy Sciences and MATH10280 - Linear Algebra in the Physical Sciences
Module Requisites and Incompatibles
Not applicable to this module.
Careers / Further progression
Open Learning means you can fit university around your life. Whether you're looking to progress your career, or you've just finished school and wondering if university is for you, Open Learning fits around your schedule and gives you all the benefits of being a full-time student, without the full-time commitment.
Open Learning allows you to select the modules you wish to study, set the pace of your study, and whether you undertake the module assessment. It also can lead to undergraduate degree entry in UCD if you are taking the Certificate in Open Learning (30 credits) or if you are a Mature Student you can take one module (5 credits) in lieu of the MSAP exam.
There are 14 Progression Pathways to choose from Students who earn 30 credits (6 x 5 credit modules) receive a NFQ Level 7 Certificate in Open Learning. This can be used to apply through the CAO for dedicated places on 14 different UCD degree programmes depending on Grade Point Average (GPA) and modules completed.
Further information
TRIMESTER: Autumn
To get started, you need to first complete and submit an online pre-registration form. Applications will reopen again on 8 August 2023.
In this module we examine numerical methods that can be used to solve such problems with a computer. Practical computer lab sessions will cover the implementation of these methods using mathematical software (Python). No previous knowledge of computing is assumed.
Topics and techniques discussed include but are not limited to the following list:
- The programming environment: installing and running Python, version control with Git
- Introduction to programming: functions, loops, logical statements, arrays, floating-point arithmetic, data storage, debugging code, documentation
- Visualising results and datasets: plotting using Matplotlib and other visualisation software
- Interpolation: Lagrange polynomials, Newton's divided-difference. Linear least squares
- Root-finding for single-variable functions: Bracketing and Bisection, Newton–Raphson method. Error and reliability analyses for the Newton–Raphson method.
- Solving ordinary differential equations (ODEs): Euler Method, Runge–Kutta method. Shooting methods. Error analysis.
- Numerical integration: Midpoint, Trapezoidal and Simpson methods. Error analysis.
- Matrices: condition numbers, inversion
We have a dedicated team who supports the Open Learning programme:
Jenny Doyle, Centre Operations Manager - jennifer.doyle@ucd.ie
Lucy and Ciarán are members of our Operations Team and they can also help you with your queries. Get in touch with us by emailing all@ucd.ie