Data & Information Analysis
- An introduction to data science
- Data preprocessing (cleaning data, missing and outliers)
- Data visualization
- Software lab part 1
- Supervised learning: linear regression
- Supervised learning: classification methods (logistics and discriminant)
- Supervised learning: resampling methods
- Supervised learning: model selection and regularization
- Supervised learning: nonlinear regression
- Supervised learning: tree-based methods
- Supervised learning: support vector machines
- Supervised learning: additive models
- Software lab part 2
- Unsupervised learning: feature selection
- Unsupervised learning: principle components analysis
- Unsupervised learning: clustering methods
- Software lab part 3
- Examples of practical examples
- Optional content (deep learning, information systems, and data-driven decision making)