CIS 5833 - Quantitative Methods

Credits: 3
This course provides a comprehensive introduction to essential quantitative methods and statistical techniques used in modern data science. Students will develop a strong foundation in probability theory, statistical inference, and advanced analytical approaches crucial for analyzing complex datasets. Key topics include probability distributions and their applications, hypothesis testing and confidence intervals, linear and nonlinear regression analysis, time series analysis and forecasting, Bayesian statistics and inference, dimensionality reduction techniques, clustering and classification methods, and resampling methods and bootstrapping. Through a combination of lectures, hands-on exercises, and real-world case studies, students will learn to apply these quantitative methods using popular data science tools and programming languages. The course emphasizes both theoretical understanding and practical implementation, preparing students to tackle complex data analysis challenges in various industries.