Introduction to Data Science
The objective of this course is to introduce first-year students to the fundamentals of data science. It begins by covering the basics of essential data science techniques, including supervised learning methods such as linear regression, decision trees, and logistic regression. The course also explores unsupervised methods like clustering, and discusses techniques for addressing model overfitting and underfitting. In addition, students will learn advanced methods such as boosting and bagging to enhance model performance.
Feb 10, 2024