Data science is a field with goals overlapping with many disciplines, in particular, mathematics, statistics, algorithms, engineering, or optimization theory. It also has wide applications to a number of scientific areas such as natural sciences, social sciences, life sciences, business, or medicine. Data science has become an integral part of many research projects and started affecting social science reaches. The promise of the “big data” revolution is that in these data are the answers to fundamental questions of businesses, governments, and social sciences such as political science and sociology. Most importantly, these quantitative techniques provide ``better predictions'' across different systems. Many of the most astonishing results come from computational fields, which have little experience with the difficulty of social scientific inquiry. As social scientists, we have an extensive experience and observations of our own research fields and we can utilize the advance of these new computational methods to our studies.
The course objective is to study the theory and practice of constructing algorithms that learn from data. This is an applied graduate level course for social scientists. Students will learn practical ways to build machine learning solutions for their own researches. While some mathematical/statistical details are needed, we will have an overview of the quantitative tools we need and emphasize the methods with their conceptual underpinnings rather than their theoretical properties. Specifically, the course will cover: k-nearest neighbors methods, the naive Bayes method, decision trees, random forests, boosting, k-means clustering and nearest neighbors, kernels, scaling, and ensemble learning. We will also discuss topics related to best practices, including overfitting/underfitting of data, error rates, cross-validation, and the use of bootstrapping methods to develop uncertainty estimates.
Statistical Software:
R is a programming language and free software environment for statistical compu