Course Overview

Course Coverage

This hands-on, project-based course introduces data science, blending computer science and statistics to extract insights from diverse datasets. Students master Python’s data science tools, including Jupyter, NumPy, Pandas, and Scikit-learn, and apply advanced visualization techniques. The curriculum emphasizes statistical understanding and analytical models like regression and clustering. A key focus is on data storytelling, enabling students to effectively communicate complex insights, bridging technical analysis with strategic decision-making.

Student Learning Outcomes

• (Foundations) Gain a comprehensive understanding of data science, exploring its evolution, significance, and multidisciplinary applications.
• (Hacking) Develop proficiency in using Python’s data science toolkit, including Jupyter Notebook, NumPy, Pandas, and Scikit-learn, through hands-on labs and projects.
• (Visualization) Learn to effectively communicate insights using Python’s Matplotlib and Seaborn libraries for sophisticated data visualization.
• (Quantification) Enhance your statistical knowledge and learn how to apply statistical methods within the context of data science for more informed analysis.
• (Analytics) Master the application of analytical models such as regression, clustering, and decision trees to address and solve real-world data science challenges.
• (Storytelling) Acquire the skills to effectively communicate data-driven insights, tailoring narratives to engage and inform various stakeholders across multiple platforms.