Statistics

Analyze time series, to explain temporal components such as trend and seasonality. Identify the appropriate model for a time series and according to this make future data predictions.

This course offers an introduction to statistical modeling. Topics include: introduction to statistical inference; confidence intervals for a single sample; test of hypotheses for a single sample; statistical inference for two samples; analysis of variance; goodness of fit tests.

This course examines stochastic processes, including generating functions, branching processes, Markov chains, random walks.

This course is a very practical introduction to quantitative analysis in International Relations. Students learn the elements of causal inference methods, computational skills for R statistical software, and examples in International Relations (ex. conflict studies, among others).

This course examines how to make decisions in business by leveraging a structured approach based on theorizing about possible future scenarios and implementing data-driven actions. The first part focuses on theoretical aspects of decision-making, while the second part focuses on data-driven analysis and interpretation of data from a business point of view. Through concrete and practical applications, students learn how to diagnose business problems, offer appropriate solutions, and generate innovative opportunities. The course focuses on the best practices that a firm can adopt to make rational decisions, namely: a structured course of action to make more rational decisions; a language to describe decisions and distinguish strategies, scenarios, and outcomes; models and statistical techniques; structured descriptive statistics; linear regression model; applications; and real cases using statistical software (Stata).

This course offers a study of quantitative and qualitative data analysis techniques and data analysis computer programs. Topics include: statistical inference; statistical estimation; statistical decision; conformance testing; relationship tests for categorical variables; tests based on the comparison of two population means; analysis of variance (ANOVA); non-parametric tests for comparing two or more distributions; relationship tests based on correlations; linear regression models; nonlinear regression models.

This course introduces the concept of modelling dependence and focuses on discrete-time Markov chains. Topics include discrete-time Markov chains, examples of discrete-time Markov chains, classification of states, irreducibility, periodicity, first passage times, recurrence and transience, convergence theorems and stationary distributions. The course requires students to take prerequisites.

This course introduces common statistical analysis methods used in psychology and sociology research and teaches students when and how to apply these methods to address their research questions.  Students learn how to select the appropriate statistical analysis methods based on their research questions, perform them using R, and present the results in APA style.
 

This course teaches students how to collect and handle date in a hands-on manner. The first few weeks of the course cover theoretical concepts through traditional lectures, but then the format shifts to a practical approach. Live coding demonstrations are used to guide students through the material, which can be followed in real-time. Python is the primary programming language used in staff-led lectures and classes, but students are also permitted to use R for their assignments if they prefer.

The course focuses on the fundamental principles of effective manipulation and visualization of data. It covers the key steps of a data analytics pipeline, starting with formulation of a data science problem, going through manipulation and visualization of data, and, finally, creating actionable insights. The topics covered include methods for data cleaning and transformation, manipulation of data using tabular data structures, relational database models, structured query languages (e.g. SQL), processing of various human-readable data formats (e.g. JSON and XML), data visualization methods for explanatory data analysis, using various statistical plots such as histograms and boxplots, data visualization plots for time series data, multivariate data, and graph data visualization methods.