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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