This textbook, Process Improvement using Data, (freely-available, Creative Commons) is the basis for an undergraduate course in data analysis at McMaster University. Chemical Engineering students in their final year and graduate students learn how to design experiments properly, analyze data using least squares and classical statistical tools and develop good visualizations. You can access all the course materials (https://learnche.org/4C3) , including lectures notes, and class video recordings. All assignments and full solutions are available. The course material is used at several universities for undergraduate engineering statistics courses.
Videos of all the class materials are available from this YouTube playlist, to supplement the above textbook.
Collected datasets (http://openmv.net) that may be freely used in any way you like. I use these datasets in teaching assignments, but many of them are from real applications, and so contain problems such as missing values, strong collinearity and noise.
Latent Variable Methods
A graduate course in Latent variable modelling and data analysis (https://learnche.org/latent) for engineering applications is available: full course notes, videos and data sets. All the literature references (https://learnche.org/literature) on latent variable methods are collected on a separate website.
An R tutorial (http://learnche.org/4C3/Software_tutorial) is provided for students to self-study.