This course provides a foundation in Exploratory Data Analysis (EDA) using the Python ecosystem to bridge the gap between raw data and actionable insights. Students navigate the data science workflow by executing complex wrangling, interpreting descriptive statistics, and analyzing multivariate relationships. The curriculum covers hypothesis testing and time-series analysis while distinguishing between supervised and unsupervised learning contexts. Emphasizing critical thinking and ethical interpretation, the course uses hands-on labs and projects to prepare students to synthesize technical findings into narrative-driven reports for advanced analytics and modeling.
Prerequisites
CISM148 with a grade of C or better