DSC - Data Science

Explore the fundamentals of data science in this introductory course, delving into its applications across various fields of study. Students will navigate the entire data science project lifecycle, mastering data acquisition, cleaning, analysis, visualization, interpretation, communication, and data management systems. Hands-on exercises will cover data science applications in students’ fields of interest, focusing on ethical considerations in every project. The course emphasizes the effective communication of results through visual representations. By the course’s end, students will possess foundational skills for data-driven decision-making and ethical data practices.

This course introduces and upgrades tools essential for data science, machine learning, fundamentals of artificial neural networks, and effective communication of problems and findings. The study delves into seminal works in recommender systems, linear regression, logistic regression, decision trees, random forest, k-nearest neighborhood, k-means clustering, support vector machine, dimension reduction techniques, and fundamentals of neural networks within a project-based learning environment, examining their implications within the students’ fields of interest. Students will explore data mining, mathematical, and statistical methods while elevating ethics, visuals, and data handling from the introductory course.

In this course, students work as individuals and teams on a data science capstone project in a domain of interest, completing each stage from data acquisition to the communication of results.