Fast Python data science practical course

Python Basics

• 1. Introduction
• 2. Getting started
• 3. Basic data types
• 4. Functions
• 5. Conditionals and comparisons
• 6. Loops
• 7. Object oriented programming
• 8. Modules and packages
• 9. Some additional topics
• 10. Debugging code

Machine learning tools

• 1. Numpy
• 2. Polars
• 3. Scikit-learn
• 4. Optuna
• 5. Matploblib
• 6. Pandas
• 7. PyTorch
• 8. PyTorch Lightning

Examples of applications

• 1. Fraud detection
• 2. Time series analysis
• 3. Image classification with neural networks
• 4. Text classification
• 5. Demographic data analysis

Other

• 1. Local linear neural networks
• 2. Boostrapping and permutation tests
• 3. Bias/variance tradeoff, mse, convergence, etc
• 4. GAN
• 5. Bayesian inference, Pyro, PyStan and VAEs
• 6. Rpy2
• 7. Simulation studies using sstudy