This class is designed as an introductory study of the theory and practice of data science. Topics covered include fundamentals of data science, practical libraries to handle data, data collection and cleaning, data visualization and analysis, learning algorithms for classification and regression, unsupervised learning, validation metrics, applications in computer vision, natural language processing, and recommendation systems.

• Time and location:
  • Tue & Thu, 2:20pm-3:35pm, Science Learning Center 0345
  • Wed, 3:00pm-3:50pm, Conner Hall 0210
• Textbooks:
  • An introduction to statistical learning
  • Python data science handbook
• Syllabus: PDF
• Learning outcomes
  1. Demonstrate understanding of data science pipeline fundamentals.
  2. Familiar with relevant data science libraries and software packages.
  3. Ability to formulate a learning problem from data.
  4. Ability to evaluate a learning model.
  5. Gain experience deploying learning models in computer vision, natural language processing, and other application domains.
• Contact: Annoucements will be made on eLC. You can also send an email to me at jinsun@uga.edu.