1Foundational Skills & Classical Machine Learning 1.4Data Science Fundamentals
1.4.7Data Processing
Not written yet
This module is on the syllabus, but nobody has written it yet. If you'd like to take it on, say so on Discord or start the file on GitHub.
| Part | 1 Foundational Skills & Classical Machine Learning |
|---|---|
| Topic | Data Science Fundamentals |
| Subtopic | Data Processing |
| Practice | Contestants should develop practical skills necessary to implement AI methods in code. This includes knowing how to use library functions effectively, call the method on a particular data, and interpret outputs. |
| Scope | Data Processing concerns the handling of missing data and irregular data, including in sequence modeling settings. Techniques involve basic imputation strategies (mean/median/forward-fill for sequences) and padding for variable-length sequences. Covered here are also normalization and standardization techniques, train/validation/test splitting strategies, basic data augmentation (flipping, cropping, noise addition), tokenization and vocabulary building for text and audio and patching for images. |