Course Description

This course provides a comprehensive introduction to data science, blending theoretical foundations with practical applications. Students will begin with data visualization, learning the essential tools and principles for creating informative and impactful visual representations of data. The course then progresses to data wrangling, where students will clean, manipulate, and prepare data for analysis.

As the semester unfolds, students will explore web scraping techniques to gather data from online sources, followed by an examination of the ethical considerations surrounding data collection, privacy, and algorithmic bias. The course then delves into statistical modeling, introducing both linear and logistic regression, and other core methods used in predictive analytics.

Throughout the course, various special topics, such as bootstrapping, randomization tests, or interactive web applications, will be covered, offering students a chance to explore current trends and tools in data science. The curriculum is designed to equip students with a versatile toolkit, enabling them to tackle real-world data challenges and communicate their findings effectively.