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 covers web scraping, recommender systems, dimensionality reduction techniques, and machine learning models for regression, classification, and clustering. Emphasis is placed on hands-on projects and case studies to apply these concepts in real-world scenarios while elevating ethics, visuals, and data handling from the introductory course.

This course covers deep neural network architectures and natural language processing and their applications in tasks such as sequence modeling, image classification, machine translation, and sentiment analysis. Students will learn to preprocess text data and explore cutting-edge techniques like transfer learning. The course emphasizes practical applications, ethical considerations such as data privacy and bias, and recent research trends.

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.