Statistics

This course introduces the subject of calculus, including the study of limits and derivatives, and their applications, which is essential and crucial for advanced statistic courses, such as probability theory, mathematical statistics. Specific topics include the limits of functions, derivatives of algebraic, trigonometric, exponential and logarithmic functions and their inverses and the definite integral and its application to area problems. Also, included are applications of the derivative including maximum and minimum problems, and curve sketching using calculus, techniques of integration, indeterminate forms and infinite series.

This course provides an introduction to the quantitative analysis of data, blending classical statistical methods with recent advances in computational, and machine learning. Students cover key topics such as the challenges of analyzing big data using statistical methods, and how machine learning and data science can aid in knowledge generation and improve decision-making. Students also explore quantitative methods of text analysis, including mining social media and other online resources.

This course covers statistical analysis and modelling methods that are commonly used in the finance industry. Major topics include statistical properties of returns, regression analysis with applications to single and multi-factor pricing models, multivariate analysis with applications in Markowitz's portfolio management, modelling and estimation of volatilities, calculation of value-at-risk, nonparametric methods with applications to option pricing and interest rate markets.

This course offers an introduction to statistical methods and the means by which to obtain information from data. Topics include: descriptive statistics; probability; random variables; distribution models; statistical inference; quality control; linear regression. Pre-Requisites: Linear Algebra, Calculus, Programming.

This course covers linear models, regression analysis, analysis of variance, with applications in various fields. Students use a specialized statistical software package to analyze linear models. 

Statistical learning is the process of extracting regularities from data using statistical models with the goal of finding a predictive function based on existing data to be able to make prediction on unseen data of similar type. The course introduces the concepts and analytical tools of statistical learning, it emphasizes “learning by doing“ with the use of R programming language to perform analysis on empirical data. The first part of the course starts with a refresher on the fundamentals of statistics—mean, variance, distribution, probabilities—before proceeding to more specialized topics. The first part of this course also gives a gentle introduction to R programming, during which issues of dimensionality and balance are discussed with their diagnostic and preprocessing tasks implemented in R. The second part of the course introduces families of binary, penalized, discriminant, and mixture models, along with performance evaluation metrics. The course concludes with the trendy topic on text mining, that is, drawing inference from text data.