Introduction to data acquisition, basic data manipulation (cleaning, outlier detection), analysis (regression, clustering, classification), basic statistics and machine learning tools, information visualization to communicate information from data. Prerequisite: Math 30-1. This course cannot be taken for credit if credit has been obtained in CMPUT 174, 175, 195, 274, 275, or ENCMP 100.
Introduction to data acquisition, basic data manipulation (cleaning, outlier detection), analysis (regression, clustering, classification), basic statistics and machine learning tools, information visualization to communicate information from data. Prerequisite: Math 30-1. This course cannot be taken for credit if credit has been obtained in CMPUT 174, 175, 195, 274, 275, or ENCMP 100.
