Data Science for Practical Economic Research

This course is designed to help students use their time efficiently when performing economic data analysis.

Topics include: Data manipulation: dataset transformation, visualization, data cleaning, web data scraping, conversion of data for the purposes of econometric estimation. Supervised machine learning: under-fitting and over-fitting, regularization, cross-validation, data augmentation. Unsupervised machine learning: clustering, factor analysis, principal component analysis, independent component analysis. Semi-supervised learning. Distributed data representation: word embedding. Nonlinear dimensionality reduction. Computational graphs and functional programming. Practical aspects of high-performance computing: GPU computing, cloud computing, model parallelism, and data parallelism.

The course will include a first introduction to Python, TensorFlow, R, Scala, and Mathematica. For specialized tasks other software will be introduced. The students are encouraged to bring to the class their own datasets, which could then be used for the purposes of instruction and practical demonstration.