course-description.qmd

---
title: "Course Description"
editor: visual
---

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.