Prerequisites:

Basic Python knowledge and knowledge about data processing, ML models and training of models is required.

Summary:

The AI revolution is moving even more rapidly than the digital revolution and leads to the emergence of completely new tools and technologies that affect the scientific process. In this course, we will learn about data-based research software, tools and communities that are relevant in creating and sharing such software, and about best practices in training machine-learning models. Research software that is based on ML models requires an additional layer of best practices in the implementation, including testing of non-deterministic processes. Security aspects as well as bad examples are discussed to highlight the importance of adhering to a best practices code of conduct.

Learning Objectives:

After the course participants will

- Understand and follow best practices in the underlying dataset
- Understand and follow best practices in training ML models
- Write better data-based research software, including appropriate tests
- Avoid negative impact from legal and security issues

This course introduces the general-purpose programming language Python, which is used by web developers, data scientists and machine learning experts. Understanding the basics of Python will allow you to grasp the concepts of tools you might encounter and quickly apply them to your own research.

Within the scope of this course, you will master the Python philosophy, syntax, and writing your own scripts and modules. In addition, you will use your newly acquired skills to perform hands-on exercises and learn to conduct reproducible in-silico research. After completing this course, you will be ready to start your own Python journey and delve deeper into the world of Data Science.

Requirements: No previous programming knowledge is required! We will use our own server platform for the course, therefore no additional installation of software is needed.