This course aims to provide researchers with the tools to make more effective use of digital resources and techniques for facilitating research and research quality. The curriculum will treat tools, tips, and tricks that are industry standards both within and outside research. These topics will be taught by experts at Chalmers e-Commons, Chalmers digital research infrastructure.
Running schedule
The course consists of a weekly full-day workshop, involving lectures, group/individual tasks, and discussions, for a total of 10 days (1 day per week, for 10 weeks).The course will focus on situations that are applicable to scientific research, for example data analysis and visualisation. If you have a data set that you want to work with, you are highly encouraged to bring it to the course.
Number of credits: 3
Number of participants: 20
Intended learning outcome
On successful completion of the course you will be able to:
- Navigate and automate tasks in a Linux environment
- Develop robust data analysis pipelines that are modular, maintainable, and reproducible, using best practices in code organization and version control
- Effectively manage research data and analysis code, applying systematic strategies to ensure data integrity, accessibility, and reproducibility through proper documentation and metadata standards
- Utilize high-performance computing facilities to optimize computational workflows, scale analyses, and process large datasets relevant to scientific research
- Apply machine learning techniques to analyze complex data, interpret results, and communicate findings effectively through appropriate data visualizations
Admission requirements
To get the most out of this course, the students need to have a basic understanding of Python, at the level where they are able to define a simple function. If you have no programming experience at all, we will run a 1-2 day course at the beginning of fall to bring people to this level. This course will be open to all researchers at Chalmers. Alternatively, you can do the tutorial at the Python course on W3Schools, up to the module on functions (https://www.w3schools.com/python/python_functions.asp). If you are not sure if you have the required level, please have a look at the W3Schools tutorial.
Content
The following topics will be discussed during the course:
- Unix shell
- Python refresher
- Structured data analysis
- Research data management
- Data visualization
- Research code management
- Using Python Notebooks for communication
- Introduction to machine learning & AI
- High performance computing
- Ethics of AI
Course leader and examiner
Leon Boschman, course leader
Sverker Holmgren, examiner
Running schedule
The course consists of a weekly full-day workshop, involving lectures, group/individual tasks, and discussions, for a total of 10 days (1 day per week, for 10 weeks). Most of the tasks involve actual data, and you are encouraged to bring your own data to the course.
Language
English
Literature
This course builds on the material in the sources below. All the reference material is freely avialable online. If you have any trouble acquiring the material, please reach out to the course organisers. The books, sites, and articles with reference material are:
- The Linux Command Line, A complete Introduction by William Shotts (available at https://www.dbooks.org/the-linux-command-line-1593279523/)
- Python Data Science Handbook by Jake VanderPlas (available at https://jakevdp.github.io/PythonDataScienceHandbook/)
- https://github.com/c3se/gts-course
- An Overview of Artificial Intelligence Ethics, IEEE Transactions on Artificial Intelligence, vol. 4, no. 4, August 2023
- Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3:160018 doi: 10.1038/sdata.2016.18 (2016)
- Barker, M., Chue Hong, N.P., Katz, D.S. et al. Introducing the FAIR Principles for research software. Sci Data 9, 622 (2022). https://doi.org/10.1038/s41597-022-01710-x
Assessment
Full attendance at at least 8 sessions, and active participation in hands-on exercises and discussions during these sessions. The practice-oriented nature of this course requires you to be present on Campus, so please make sure that you are available for all the assigned dates. Grading U, G.
