Course pages 2017–18
Scientific Computing Practical Course
This is a practical course taken by Part IA CST students only. It is taught through one introductory lecture, followed by an online course which is equivalent in content to approximately 5 lectures. Students will normally work though the online component at their own pace. The course will be entirely examined through practical exercises.
Lecturer: Dr D. Wischik
No. of lectures: 1 (plus an online course with roughly 5 lectures worth of material in the Lent term)
Suggested hours of supervisions: none
Prerequisite courses: Foundations of Computer Science, NST Mathematics
This course is a prerequisite for Foundations of Data Science (Part IB)
Aims
This course is a hands-on introduction to using computers to investigate scientific models and data.
Syllabus
- Python notebooks. Overview of the Python programming language. Use of notebooks for scientific computing.
- Numerical computation. Writing fast vectorized code in numpy. Optimization and fitting. Simulation.
- Working with data. Data import. Common ways to summarize and plot data, for univariate and multivariate analysis.
Objectives
At the end of the course students should
- be able to import data, plot it, and summarize it appropriately
- be able to write fast vectorized code for scientific / data work