Teaching

Basic definitions and managing different types of data. Introduction to manipulation (indexing, subsetting, reshaping, transforming etc.), visualization, mapping and analysis of data. Dealing with common problems like missing or inconsistent values in datasets. Use of related R and/or Python programming packages. Merging multiple data tables (equivalent to an SQL JOIN).

Region (interval) estimation. Hypothesis testing. Optimality properties for hypothesis testing. Likelihood ratio tests. Sequential tests.

Common theoretical distributions. Sampling distributions. Principles of point estimation. Techniques of estimation. Properties of point estimators. Optimality criteria in estimation. Selected topics from robust inference. Bayesian inference.

Basic definition of insurance. Historical background. Insurance applications in government and private sector, regulations and legislation in insurance. Fundamentals of insurance. Types of insurance, disaster insurance and risk management applications around the world. Turkish catastrophe insurance pool. Definition of risk, probability aspect of risk. Utility theory, claim processes, distribution of claim processes.

Sample mean vector and sample covariance matrix; matrix decomposition; multivariate normal and Wishart distributions; parameter estimation; hypothesis testing; MANOVA; principal components; factor analysis; multivariate classification and clustering; canonical correlation.

Types of data. Graphical and tabular representation of data. Approaches for finding unexpected in data. Exploratory data analyses for large and high-dimensional data. Analysis of categorical data. Elements of robust estimation. Handling missing data. Smoothing methods. Machine Learning and Deep Learning. Data mining.

Time series as a stochastic process. Means, covariances, correlations, stationarity. Moving averages and smoothing. Stationary and nonstationary parametric models. Model specification. Estimation and testing. Seasonality. Some forecasting procedures. Elementary spectral domain analysis. Exponential smoothing methods. Unit root tests.

Time series as a stochastic process. Means, covariances, correlations, stationarity. Moving averages and smoothing. Stationary and nonstationary parametric models. Model specification. Estimation and testing. Seasonality. Some forecasting procedures. Elementary spectral domain analysis. Exponential smoothing methods. Unit root tests.

Sample space, events, basic combinatorial probability, conditional probability, Bayes’ theorem, independence, random variables, distributions, expectation.

Markov Chains (discrete and continuous time), Poisson Processes, Queuing Processes, Birth and Death Processes, Decision Analysis.

Introduction to programming and computation in R. Introduction to computer organization and basic data structures. Advanced R programming with applications to statistical procedures.

Introduction to statistical techniques in R Programming. Managing and analyzing data using statistical database packages. Introduction to MATLAB with applications to matrix algebra.

Readings and projects in areas of current statistical real life application including environmental science, industrial statistics, official statistics, actuarial statistics, business statistics, physical and social sciences, and medical statistics.