Jeffrey Holt

Topics will include systems of linear equations, matrix operations and inverses, vector spaces and subspaces, determinants, eigenvalues and eigenvectors, matrix factorizations, inner products and orthogonality, and linear transformations. Emphasis will be on applications, with computer software integrated throughout the course. The target audience for MATH 3350 is non-math majors from disciplines that apply tools from linear algebra. Credit is not given for both MATH 3350 and 3351.

Main designs & estimation techniques used in sample surveys; including simple random sampling, stratification, cluster sampling, double sampling, post-stratification, ratio estimation; non-response problems, measurement errors. Properties of sample surveys are developed through simulation procedures. Uses SUDAAN software package for analyzing sample surveys.

This course provides an introduction to data analysis using the Python programming language. Topics include using the IPython development environment; data analysis packages NumPy and pandas; data loading, storage, cleaning, merging, transformation, and aggregation; data plotting and visualization and time series data. No prior experience with programming or statistics is required.