Aegis-GPUNCA-ADS learning guides
One short guide per official objective, grouped by exam domain. Each point is backed by a quote from an NVIDIA source. Terms link to the glossary the first time they appear.
Data Manipulation and Preparation
- 1.1 Joining and manipulating data with cuDF and pandas
- 1.2 Cleaning data and handling quality and governance
- 1.3 GPU-accelerated ETL with RAPIDS, Dask and Spark
- 1.4 Feature engineering for numbers and categories
- 1.5 Class imbalance and synthetic data
- 1.6 Dimensionality reduction and sampling
- 1.7 Efficient storage with Parquet and modern frameworks