Contents

1. Introduction to Python Programming
   • Overview of Python and its applications in science and engineering
2. Setting Up the Development Environment
   • Hands-on with VS Code and JupyterLab
   • Simple practices for writing and running Python code
3. Basic Python Programming
   • Variables, data types, strings, lists, dictionaries
   • Conditional statements and loops
   • Writing and using functions, etc.
4. Intermediate and Advanced Python
   • Introduction to classes and object-oriented programming
   • Common algorithms: sorting, searching, and recursion
   • File I/O and exception handling, etc.
5. Data Visualization with Matplotlib
   • Introduction to Matplotlib and plotting basics
   • Creating effective and publication-quality scientific plots
   • Introduction to other libraries (e.g., Seaborn, etc.)
6. Introduction to Machine Learning and Neural Networks
   • Key concepts in machine learning
   • Architecture of artificial neural networks (ANNs)
7. Architecture of artificial neural networks (ANNs)
   • Implementing a basic ANN using only Python and NumPy
   • Hands-on training and testing with small datasets
8. Advanced Neural Networks with TensorFlow or PyTorch
   • Building deep neural networks with TensorFlow or PyTorch
   • Training, evaluation, and model optimization
9. Python for Materials Science
   • Introduction to computational materials science
   • Using Python to preprocess, postprocess, and visualize DFT data
   • Practical example: analyzing band structures or density of states