2022-10-21 CAS ADS M4 Ethics and best practices for Data Science 1 (09:00-12:30)
Free and mandatory for enrolled CAS Applied Data Science participants.
Reiter
About
- Ethics and best practices for scientific computing and data science
Computing and software is at the core of many science and business projects. Consequently reproducible code is a basic criterium for high quality science and products. Scientific code should be Findable, Accessible, Interoperable and Reproducible (FAIR). Producing FAIR code is a societal obligation for people working with software to be used by others.
Working with data may come with high responsibility and require high moral integrity. Misuse of data may not only hurt individuals, but entire political and democratic systems.
In this CAS Applied Data Science Module we reflect on ethics and best practices for people working with software and data. We learn about basic security issues and meassures, why and how to licence Free Open Source Software and how to use version control software like Git and produce good documentation with ReadTheDocs.
The modules consists of 5 courses - Cyber security fundamentals
- Ethics, best practises for data science, documentation techniques
- Collaborative distributed version control (Git) 1
- Collaborative distributed version control (Git) 2
- Free Open Source Software, Licences and Data Privacy Aspects (legal)
Target group
- CAS Applied Data Science participants
- Researchers
Prerequisites
- You should be able to navigate within the file tree on the command line and edit text files
- Basics in at least one programming language. The training is as language independent as possible, but examples and practical work is in Python.
- You will need to bring a laptop.
- You need to install some software during the course
Methods
- Theoretical sessions with accompanying practical work. Discussion. Project work.
Certificate
- Module certificates are only issued upon request as the successful module attendance anyway is part of the final CAS certificate.
Coaches
- The coaches are local and external experts
Practical information (time, location ...)
Time : 2022-10-21 from 09:00 to 12:30
Location : Room 323 Parkterasse 14, University of Bern (see orientation plan for the room when logged in)
Online Participation: Login to see the link for remote participation.
Training language: English
Participants : Max 24
Registration : Mandatory
Coaches : PD Dr. Sigve Haug (responsible)
Certificate: 2 ETCS
Location : Room 323 Parkterasse 14, University of Bern (see orientation plan for the room when logged in)
Online Participation: Login to see the link for remote participation.
Training language: English
Participants : Max 24
Registration : Mandatory
Coaches : PD Dr. Sigve Haug (responsible)
Certificate: 2 ETCS
Tasks (Module 4 Project)
For Module 4 you need to
- Create your own GitHub repository for your CAS material and projects
- Document repository and subfolders with Readme files
- Make standalone Python script(s) of (parts of) your Module 3 Jupyter notebooks with inline documentation of the code
- Upload the script(s) to your repository Module 3 folder
- Optional: Create a GitHub webpage for your CAS repository or/and document your Module 3 scripts with sphinx (or some other tool) and publish it on ReadTheDocs
- Add the link to your GitHub repository on the Module 4 Ilias
Deadline for adding link: November 30
- Create your own GitHub repository for your CAS material and projects
- Document repository and subfolders with Readme files
- Make standalone Python script(s) of (parts of) your Module 3 Jupyter notebooks with inline documentation of the code
- Upload the script(s) to your repository Module 3 folder
- Optional: Create a GitHub webpage for your CAS repository or/and document your Module 3 scripts with sphinx (or some other tool) and publish it on ReadTheDocs
- Add the link to your GitHub repository on the Module 4 Ilias
Deadline for adding link: November 30
Schedule
09:00 - 10:30 Introduction to best practices and documentation,
team building (5 per team) and explanation of tasks
10:30 - 11:00 Break
11:00 - 12:00 Team work on tasks
12:00 - 12:30 Q&A session
team building (5 per team) and explanation of tasks
10:30 - 11:00 Break
11:00 - 12:00 Team work on tasks
12:00 - 12:30 Q&A session
About the coaches
Sigve studied physics in Germany, Spain and Norway. He has been involved in neutrino physics experiments and high energy frontier experiments, often with main focus on the computing challenges related to the large and distributed data from these experiments. Today he is working for the Albert Einstein Center for fundamental Physics and the Mathematical Institute of the University of Bern where he is leading the Data Science Lab.