This course presents a comprehensive overview of the key elements of data science for engineers. Topics include data cleaning, organization and manipulation, data collection, visualization and noise filtering. Data analysis techniques including regression, decision trees, feature selection, clustering and classification are covered. Emphasis is on spatial analysis and visualization, as well as the analysis of time series. An introduction to advanced topics such as deep learning, big data management and analysis is provided. The focus is on the practical application of data science in the engineering context to make predictions and decisions based on the statistical inference of data. (Lec: 3, Lab: 1.5, Tut: 0)
This course presents a comprehensive overview of the key elements of data science for engineers. Topics include data cleaning, organization and manipulation, data collection, visualization and noise filtering. Data analysis techniques including regression, decision trees, feature selection, clustering and classification are covered. Emphasis is on spatial analysis and visualization, as well as the analysis of time series. An introduction to advanced topics such as deep learning, big data management and analysis is provided. The focus is on the practical application of data science in the engineering context to make predictions and decisions based on the statistical inference of data. (Lec: 3, Lab: 1.5, Tut: 0)
