Data Analytics and Information Engineering.

Catalog description

Introduction to data analysis, setting a well-defined goal/objective, delineating constraints, structured data analysis plan, stakeholder agreement, analytics problem formulation, defining key metrics, identify data needs, data collection/acquisition, harmonize, rescale, and clean data, data mapping/relationships, pivot, unpivot, slicers, and SQL queries. Introduction to visualization using a relevant software application. Formatting and exporting graphs, rows/column shelves, mark card, identifying outliers, analysis using filter and group, date hierarchy. Using charts, plots, calculations, data blending and parameters. Analytical model building, validation, testing, solution deployment, model life-cycle management. Reporting using dashboards. Visual story design. Visualization tools, Hourglass model, story elements, storyboarding, testing, fallacies due to over generalization, misinterpretation, and correlation as opposed to causation. Introduction to machine learning, artificial intelligence, and big-data analytics. (Fall) Prerequisite: EG1294; Corequisite: EG3395.