Prerequisites: CS 100 or CS 101 or CS 106 or CS 113 or CS 115 or BME 210. Pre or Corequisites: MATH 225 or MATH 244 or MATH 279 or MATH 305 or MATH 333 or ECE 321 or IE 331 or MNET 315. Restriction: This course is intended for engineering majors. This is a course for junior level undergraduates in any engineering discipline focusing on the use of data science techniques to solve problems in engineering. We will first discuss the Python programming language and how it can be used to access, manipulate, explore, and visualize scientific datasets. We will discuss statistics and probability as it applies to engineering problems such as safety factors and probability of part failure; this includes conditional probability, probability distributions, hypothesis testing, and Bayesian inference. We will then discuss more advanced statistical models (“machine learning”), including linear and logistic regression, decision trees, and clustering. Possible applications of these methods will be demonstrated in such disciplines and topics as (but not limited to): chemical, mechanical and electrical engineering (optimization and controls), materials engineering (structure and property databases), biomedical engineering (medical diagnosis and medical imaging) and electrical and computer engineering (signal processing, target tracking, robotic navigation). Students will gain hands-on experience in implementing and utilizing these various methods through computational laboratory assignments and reports and a semester-long engineering design project.