1 credit
Summer 2026 Lecture Distance Learning Lower Division
Introduction to data science for sophomore mechanical engineers with no prior knowledge of the topic; Overview of Python basics using Jupyter notebooks; review of probability theory as the language of uncertainty, Monte Carlo sampling for uncertainty propagation; basics of supervised (Bayesian generalized linear regression, logistic regression, deep neural networks, convolutional neural networks), and unsupervised learning (k-means clustering, principal component analysis, Gaussian mixtures).
Learning Outcomes1Represent uncertainty in parameters in engineering or scientific models using probability theory.
2Propagate uncertainty through physical models to quantify the induced uncertainty in quantities of interest.
3Solve basic supervised learning tasks, such as regression and classification.
4Solve basic unsupervised learning tasks, such as clustering, dimensionality reduction, and density estimation.
5Develop and apply various Python coding skills.
6Load and visualize data sets in Jupyter notebooks.
7Visualize uncertainty in Jupyter notebooks.
8Recognize basic Python software (e.g., Pandas, numpy, scipy, scikit-learn) and advanced Python software (e.g., pytorch, pyrho, Tensorflow) commonly used in data analytics.
Prerequisites
made by Purdue students.