ECE 2720

Data Science for Engineers

Course Description

Course information provided by the 2025-2026 Catalog.

An introduction to data science for engineers. The data science workflow: acquisition and cleansing, exploration and modeling, prediction and decision making, visualization and presentation. Tools for data science including numerical optimization, the Discrete Fourier Transform, Principal Component Analysis, and probability with a focus on statistical inference and correlation methods. Techniques for different steps in the workflow including outlier detection, filtering, regression, classification, and techniques for avoiding overfitting. Methods for combining domain-agnostic data analysis tools with the types of domain-specific knowledge that are common in engineering. Ethical considerations. Optional topics include classification via neural networks, outlier detection, and Markov chains. Programming projects in Python.

Prerequisites REF-FA25/Corequisites REF-FA25 MATH 1920 and either CS 1110 or CS 1112. Corequisite: MATH 2940. Corequisites: None.

Last 4 terms offered (None)

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