Program Structure

Key topics of probability and distribution theory and to place particular emphasis on statistical inference. 

Basic principles of simulations and its usage in modern statistical analyses.

Introduction to statistical modeling, exponential family of distributions, part of a GLM, binomial data, logit models, contingency tables, log-linear models, Poisson models, overdispersion, normal data, Gamma data, polynomial-ordinal regression models, linear mixed models, GEE models, GLMM models. All applications include the use of the R language.

Understanding and applications of statistical methods in real life problems of various scientific fields such as Management, Marketing, Psychology, Medicine, Sports and Social Sciences.

Basic concepts in survival analysis, definitions, hazard and survival functions, relationships, parametric methods, likelihood function, Exponential and Weibull Models, applications in R.

The problem of inference for survey populations adopting the design-based approach.

Definition of quality. Basics on quality and statistical quality control. An introduction to Acceptance sampling and Design of Experiments. Cause and effect chart and Pareto chart. The philosophy of statistical process control. Control charts for variables and attributes. Individual control charts. EWMA and CUSUM charts Capability indices. Control charts for autocorrelated data. Introduction to multivariate control charts. Basics of six sigma methodology.

Modern biology is a data-rich science. This course will expose the students to high-throughput biological datasets (such as microarrays, RNA-Seq, ChIP-Seq) and present the main inferential tools to deal with challenges they impose to the statistician.

Α range of statistical and machine learning methods will be described for supervised and unsupervised learning problems. Unsupervised learning: clustering (hierarchical, partition clustering, k-means and its variants, model-based clustering ), data reduction methods. Model Assessment and Selection. Supervised learning: Methods of Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), k‐nn, decision trees, random forests, SVM, naïve Bayes and others. Cross-validation methods. Statistics for big data problems, new approaches. Regularizations. Statistical methods for networks. Smoothing approaches in regression.

Introduction to the Bayesian approach in statistics both from the theoretic and the computational perspective using R and WinBUGS

Presentation of modern statistical methods and associated theory for the construction, fitting and evaluation of statistical stochastic models.

The course lays proper foundations in Data Engineering with emphasis on Statistical and Data Science applications.

A broad introduction to the theory and empirical analysis of econometric models to financial applications. Statistics/Econometrics is concerned with the systematic study of empirical financial problems using observed data. The aim of the course is to develop the relevant econometric tools for analyzing empirical problems in finance.

Introduction to stochastic modeling in finance and the use of stochastic models in the description and forecast of prices of various assets such as stocks and indices, pricing of derivative products and bonds as well as their use in portfolio selection and risk management, focusing on models which are widely used in theory and practice. T