DSC 530 - Experiment Design for Data Science

Units: 4
When Offered: Spring
This course covers the most essential features of experimental research design and the measures associated with it. Principles and procedures involved in collecting, organizing, analyzing, presenting, and interpreting data from quantitative experimental design inquiry are primary areas of examination. Both descriptive and inferential statistical approaches are presented, e.g., describing data, its frequency, measures, and presentation to discrete and continuous probability, analysis of variance (ANOVA), linear and multiple regression, time series and forecasting, quality management and decision theory. Lectures focus on developing tangible understanding of experimental design and corresponding analytic models, and interpreting research results. Laboratory exercises will focus on using programming statistical software for data analysis and presentation.
Grade Type: Letter Grade