Statistics

This course covers the fundamental algorithms for statistical computations and R packages that implement some of these algorithms or are useful for developing novel implementations. It develops the ability to implement, test, debug, benchmark, profile and optimize statistical software; select appropriate numerical algorithms for statistical computations; and evaluate implementations in terms of correctness, robustness, accuracy and memory, and speed efficiency. Topics include: maximum-likelihood and numerical optimization; the EM-algorithm; Stochastic optimization algorithms; simulation algorithms and Monte Carlo methods; nonparametric density estimation; bivariate smoothing; numerical linear algebra in statistics, sparse and structured matrices; practical implementation of statistical computations and algorithms; R/C++ and RStudio statistical software development. 

This course examines mathematical concepts for stochastic calculus. The topics include: introduction to continuous time stochastic processes; definition and properties of Brownian motion; semimartingales; Stochastic integration; Itô (change of variable) formula; theorems for applications (e.g., Girsanov’s theorem).

This course introduces regression analysis, one of the most widely used statistical techniques. Topics include simple and multiple linear regression, nonlinear regression, analysis of residuals and model selection, one-way and two-way factorial experiments, random and fixed effects models.

The course teaches the implementation of basic data management and statistical/econometric analysis methods using Stata. 

Course Prerequisite: Completion of Introductory Econometrics or a more advanced course in econometrics.

This course focuses on the importance of transforming large volumes of data into relevant information for decision-making and business development for companies and individuals. It offers a study of the basic techniques of preprocessing and visualization of data, working with missing and atypical data, use of dimension reduction techniques, methods of supervised learning in regression and their usefulness in prediction problems, distinguishing between linear and non-linear models, and model selection methods.

This course is designed to provide students with the tools necessary to design and implement surveys and survey experiments. The course discusses issues of questionnaire design; sampling; respondent recruitment, and data collection. The course also explores causal inference and experiment designs to conduct social science research.

Topics in this Statistics for Journalism course include: the concept and uses of statistics; terminology; types of variables; analysis of univariate data; analysis of bivariate data; probability and probability models; statistical inference.

This course introduces the modelling and analysis of time series data. A computer package is used to analyze real data sets. Topics include stationary time series, ARIMA models, estimation and forecasting with ARIMA models. The statistical software R is used to implement these methods on real-world data sets. The course requires students to take prerequisites.

This course examines methods for using data to assist in decision making in business and industrial applications. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models, simulation, analytics and visualization will be considered.