Data Analysis in Engineering

3 credits · Graduate Programs

Fundamentals of matrix theory, linear systems, probability and statistics. Data classification, analysis and bias identification. Random data acquisition, qualification and analysis. Least squares estimation and data analysis. Random process, stationarity test and kinematic modelling. Kalman filtering and real-time data analysis. Introduction to signal processing and time series analysis. Practical applications of data analysis and processing in geomatics engineering. Prerequisite(s): Admission to the MEng (course-based) program. Antirequisite(s): Credit for Geomatics Engineering 664 and 563 will not be allowed.