Hands-on scientific computing
Form of learning:
Target group and prerequisites
Competence in data science and scientific computing in general requires a lot more than data science skills: there are many secondary skills needed to do practical work efficiently. These skills are often not taught in courses, or they can be missed because of non-traditional career paths. This course is more of a self-study guide to these skills and tools needed in scientific computing. Rather than a single guided course, it is more of a map to browse whenever a topic is relevant to one’s work. However, one can also systematically study the material and receive credits.
The material is divided into levels A, B, C, D, E and F. A student may choose a suitable level for them and continue from there. The course gathers existing online material and organises them to a guide, since in real work being able to find and understand the best resources is more important than taking courses.
The course aim is to provide basic tools and skills that one needs to perform data science and scientific computing, and succeed in other courses. The course mainly links example materials on the topics but students are also highly recommended to learn to search for other relevant material that suits their own needs. While self-studying, one may do exercises and answer questions in order to receive the course credits.
After modules A, B and C student will:
- know how to set up their computer for scientific computing
- know data management in a nutshell
- be familiar with Jupyter Notebooks
- have tools for making figures, creating posters and editing code
- basics of LaTex
- be able to write shell scripts
- know what is Git, how to use it and why use it
- know how to do work remotely using SSH
- be familiar with how Make can automate repetitive stuff in projects
After modules D, E and (F) student will:
- know how to use advanced computational resources
- know specifically about usage of Aalto cluster Triton
- be able to break their programs into smaller functionalities
- be familiar with software testing and profiling
- have tools to debug their programs
- know how to make their project reproducible and available for others
Online self-studying, written material, videos, exercises (questionnaires, scripting, writing text documents)
Please note: if you take more than 6 months to complete the exercises, they might change and your contribution may not be valid anymore.
Depends on student’s competence level but from beginning until the end with exercises ~27 hours per credit.
The course is divided into 2 modules (A-C, D-E) . To earn 1 credit for 1 completed course module, the student should have completed at least 90% of the module exercises and 50% of them should be correct.