Ozancan Ozdemir

Ozancan Ozdemir

PhD Candidate at Bernoulli Institute for for Mathematics, Computer Science and Artificial Intelligence. Machine Learning and Deep Learning Enthisuasist.

Courses which I worked as Teaching Assistant

Department of Statistics, Middle East Technical University, 2017

  • STAT 112-INTRODUCTION TO DATA PROCESSING AND VISUALIZATION (Fall 2022)

Course web page: https://metustat112.github.io/

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)

  • STAT 304- MATHEMATICAL STATISTICS II (Spring 2022)

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

  • STAT 303- MATHEMATICAL STATISTICS I (Fall 2021)

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.

  • STAT 487- INSURANCE AND ACTUARIAL ANALYSİS (Spring 2021)

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.

  • STAT 467- MULTIVARIATE STATISTICS (Fall 2020, Fall 2022)

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.

  • STAT 412- STATISTICAL DATA ANALYSIS (Spring 2020, Spring 2021)

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.

  • IAM 526- TIME SERIES APPLIED TO FINANCE (Fall 2019, Fall 2020)

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

  • STAT 497- TIME SERIES ANALYSIS (Fall 2019, Fall 2020)

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

  • STAT 203- PROBABILITY (Fall 2019)

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

  • STAT 471 - INTRODUCTION TO FINANCIAL ENGINEERING (Fall 2019)

  • STAT 376- STOCHASTIC PROCESS (Spring 2019)

Theory Markov Chains. Discrete and Continuous time Markov Chains. Poisson Processes. Queuing Processes. Birth and Death Processes. Decision Analysis.

  • STAT 292- STATISTICAL COMPUTING II (Spring 2018, Spring 2019)

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

  • STAT 291 - STATISTICAL COMPUTING I (Fall 2017, Fall 2018)

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

  • STAT 111 - STATISTICS BY REAL LIFE EXAMPLES (Spring 2018, Spring 2020)

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.

*Course descriptions are taken from the web page of Department of Statstics, METU.