Artificial Intelligence (AI) and Data Informed Decision Making (DIDM) rely heavily on data, and the use of AI and DIDM is necessary in order to maintain competitiveness. Industry benchmarks indicate that 70 to 80% of the effort in implementing AI and DIDM is associated with the task of acquiring pertinent data. Organizing and thereby making industrial data easier to acquire would help mitigate the efforts involved.

This course introduces the current tools used to address this problem. Students will learn about industry standards, approaches, and data transport protocols. Working both in team and individual environments, these concepts will be applied to real-world scenarios.

Students must be familiar with Python and basic object oriented programming principles, and have completed an introductory university-level statistics course that introduces topics such as regression, variance, standard deviation, and root mean square error.