This course combines practical Python programming, data analysis, and machine learning concepts to give students hands-on exposure to the tools and workflows of data science. Participants will explore libraries such as NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn to build real data applications. Students will also be introduced to generative AI tools to assist with coding, debugging, and understanding machine learning workflows.

Course Learning Outcomes (CLOs):

By the end of this course, students will be able to:

• Demonstrate Python programming fluency for data tasks
• Use Pandas to clean, transform, and prepare data
• Perform exploratory data analysis and visualizations using libraries like Seaborn and Matplotlib
• Understand supervised and unsupervised machine learning models
• Complete a data science project using a structured workflow