Aim

This advanced course is designed for first-year PhD students in Integrative Neuroscience to equip them with the programming, computational, and software development skills necessary to produce reproducible, efficient, and modern scientific analyses. It bridges the gap between research programming and professional software development, guiding students from fundamental coding principles to advanced applications such as data analysis, visualization, simulation, and machine learning.

Summary of the main learning goals:

• Acquire critical programming skills and learn good scientific programming practices (hands-on).

• Understand the principles of reproducible research and the modern scientific development ecosystem.

• Utilize AI-assisted coding tools effectively while maintaining a strong grasp of fundamental programming concepts.

• Set up and manage a scientific development environment and workflows.

• Master numerical computing with NumPy, basic plotting with Matplotlib, and basic analytics with SciPy, Pandas, and Scikit-learn.

• Understand error handling, debugging strategies, type hints, and code documentation for robust code development.

• Apply statistical analyses and sophisticated machine learning techniques using Scikit-learn, and understand the use and deployment of deep learning models with PyTorch and/or Tensorflow

• Develop simple numerical simulations of mathematical models of biological systems.

• Develop sophisticated data analysis routines for (neuro)biological datasets.

• Understand testing, continuous integration, code review, packaging, and distribution techniques for professional software development.