Data Science 201C is a 30-credit second-year minor course designed to provide a practical and hands-on introduction to the core principles and workflows of data science.  The curriculum is structured into six 5-credit modules that guide students through the entire data lifecycle.  It begins with the crucial foundational skills of data acquisition and cleaning ensuring students can collect and prepare datasets for analysis.  This is followed by Exploratory Data Analysis (EDA) where they learn to summarize data using visualizations and statistics to uncover patterns and trends. The course is built on a project-based learning model progressing from basic data analysis to the application of fundamental machine learning models.  Students will be taught how to train simple classification and regression models and evaluate their performance using standard metrics.  A key component of the course is building reproducible data pipelines and integrating workflow automation, which is a critical skill for any data professional.  The curriculum also addresses the vital non-technical aspects of the field by including a module on data ethics and governance focusing on privacy bias detection and mitigation strategies. The capstone of the course is a mini-project that requires students to apply all the skills they’ve learned throughout the semester.  This final project challenges them to take a dataset from its raw form to a full analysis, culminating in insights presented through visualizations and a short video demonstration.  The project submission serves as a tangible portfolio piece showcasing their ability to complete a data science project from start to finish and present their findings effectively.