MATH/COSC 3570: Introduction to Data Science

This course introduces main aspects of doing a practical data science project, from importing data to deploying what is learned from data. We start with learning popular data science tools such as basic R and Python programming, Git and GitHub, and interactive documenting systems RMarkdown and Quarto. Then we learn data importing, data visualization and data wrangling using both R and Python. The second half of the course focuses on several basic simulation and machine learning methods, including Monte Carlo simulation, linear regression, K-nearest neighbors, logistic regression, principal component analysis, and K-means clustering. We learn R tidyverse and tidymodels packages. For Python, Pandas and Scikit-Learn libraries are introduced.