This course offers a comprehensive approach to transforming data into meaningful insights. Through exploratory data analysis, participants will learn how to effectively explore datasets, address inquiries, and present results visually. The course equips individuals with the necessary tools to clean and validate data, visualize distributions and relationships between variables, and utilize regression models for prediction and explanation. Participants will have the opportunity to analyze data pertaining to demographics and health, such as the National Survey of Family Growth and the General Social Survey. However, the techniques and methodologies covered in this course are applicable across various fields including science, engineering, and business. Key tools utilized throughout the course include Pandas, a robust data manipulation library, as well as other essential Python libraries such as NumPy and SciPy. Additionally, participants will gain proficiency in StatsModels for regression analysis and Matplotlib for data visualization. By acquiring these tools and skills, participants will be well-prepared to work with real-world data, uncover valuable insights, and effectively communicate compelling results.