Graduate Certificate in Data Analytics

This course is designed to introduce students to the concepts and data-based tools of statistical analysis commonly employed in Applied Economics. In addition to learning the basics of statistical and data analysis, students will learn to use the statistical software package Stata to conduct various empirical analyses. Our focus will be on learning to do statistical analysis, not just on learning statistics. The ultimate goal of this course is to prepare students well for ADEC 7320.01, Econometrics.

This course focuses on the application of statistical tools used to estimate economic relationships. The course begins with a discussion of the linear regression model, and examination of common problems encountered when applying this approach, including serial correlation, heteroscedasticity, and multicollinearity. Models with lagged variables are considered, as is estimation with instrumental variables, two-stage least squares, models with limited dependent variables, and basic time-series techniques.

Prerequisite: ADEC 7310 Data Analysis or department approval.

This course demonstrates how to merge economic data analysis and applied econometric tools with the most common machine learning techniques, as the rapid advancement of computational methods provides unprecedented opportunities for understanding big data. This course will provide a hands-on experience with the terminology, technology and methodologies behind machine learning with economic applications in marketing, finance, healthcare and other areas. The main topics covered in this course include: advanced regression techniques, resampling methods, model selection and regularization, classification models (logistic regression, Naive Bayes, discriminant analysis, k-nearest neighbors, neural networks), tree-based methods, support vector machines, and unsupervised learning (principal components analysis and clustering). Students will apply both supervised and unsupervised machine learning techniques to solve various economics-related problems with real-world data sets. No prior experience with R or Python is necessary.

Pre-requisite: ADEC 7310 Data Analysis and ADEC 7320 Econometrics, or department approval.

This course will expose students to the most popular forecasting techniques used in industry. We will cover time series data manipulation and feature creation, including working with transactional and hierarchical time series data as well as methods of evaluating forecasting models. We will cover basic univariate Smoothing and Decomposition methods of forecasting including Moving Averages, ARIMA, Holt-Winters, Unobserved Components Models and various filtering methods (Hodrick-Prescott, Kalman Filter). Time permitting, we will also extend our models to multivariate modeling options such as Vector Autoregressive Models (VAR). We will also discuss forecasting with hierarchical data and the unique challenges that hierarchical reconciliation creates. The course will use the R programming language though no prior experience with R is required.

Pre-requisite: ADEC 7310 Data Analysis and ADEC 7320 Econometrics, or department approval.