ENGRD 2720

Data Science for Engineers

Course Description

Course information provided by the 2026-2027 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 MATH 1920, and either CS 1110 or CS 1112.

Corequisites MATH 2940.

Last 4 Terms Offered 2026SP, 2025FA, 2025SP, 2024FA

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