DATA 200 - Data Visualization

3 credit hours - This course provides a comprehensive introduction to both data analysis and data visualization techniques. Students will learn essential skills for exploring and analyzing data, including data cleaning, model building, and machine learning (supervised and unsupervised learning). The visualization component focuses on creating clear and effective visualizations using Matplotlib and Seaborn in Python, covering a wide range of plot types such as line plots, histograms, scatter plots, and box plots. Students will also explore advanced visualization techniques, including Folium for interactive maps and Shiny (optional) for dynamic web-based dashboards. A key focus will be on using visualizations for exploratory data analysis (EDA) and data storytelling, enabling students to communicate insights clearly and persuasively. Prerequisite(s): DATA 161 or DATA 171.