Basic concepts in data engineering, including data acquisition, data management and models for learning with associated algorithms; iterative algorithms; tree-based and regression-based classification; graphs and graph-based methods; clustering; neural networks basics and their training; data structures for storing and processing data; introduction to databases. Prerequisites: Grade of C or better in CSCE 110, CSCE 111, CSCE 120, CSCE 121, or CSCE 206; grade of C or better in STAT 211; junior or senior classification Credits 3. 3 Lecture Hours.
Basic concepts in data engineering, including data acquisition, data management and models for learning with associated algorithms; iterative algorithms; tree-based and regression-based classification; graphs and graph-based methods; clustering; neural networks basics and their training; data structures for storing and processing data; introduction to databases. Prerequisites: Grade of C or better in CSCE 110, CSCE 111, CSCE 120, CSCE 121, or CSCE 206; grade of C or better in STAT 211; junior or senior classification Credits 3. 3 Lecture Hours.
