Teaching

School of Mathematics, University of Edinburgh, UK
Lecturer in Statistics

Organised

• Introduction to Data Science (UG):

  This is an introductory level course on data science and statistical thinking. During 2023-24, I have been the Course organiser. This course nominated by students for the outstanding course teaching award.

• Machine Learning in Python (PGT):

  This course is intended to provide an introduction to machine learning techniques. After delivering two weeks in 2021, I have been the Course organiser for academic year 2022-2023 and course nominated by students for the outstanding course teaching award.

• Advanced Statistics (PGT):

  This Year 2 PGT course for the MSc in Epidemiology offered by Edinburgh Medical School: Usher Institute. I have been one of the organisers for 2024 cohort and responsible for the course material redesign/creation.

Delivered

• Representing Data (PGT):

  This course will introduce students to practical data representation. It will enable students to understand data visualisation theory and practice, while simultaneously inviting them to challenge and extend these concepts throughout the course. Students will examine a range of different methodologies and practices for representing data in a variety of formats including physical and embodied formats.

• Insights Through Data (PGT):

  This course will introduce students to the practical aspects of data science. It will provide a basic background in data science skills and methodologies via direct interaction with data through programming, statistics, and machine learning. The goal of the course is to facilitate a spectrum of insights by analysing different data sets and interpreting the results.

• Statistics (UG):

  This course provides an introduction to the basic concepts of Statistics including the examples with R programming language. I have been co-delivering the course focusing on the regression model and Anova.

• Facets of Mathematics (UG):

  This course is based around three key themes from different areas of mathematics, and develops important skills in basic Python, mathematical writing, and document creation using LaTeX. I have been responsible for the third theme about linear models.

• Regression and Simulation Methods (PhD):

  This course is based on the key topics which lie at the heart of research in statistical methods and form a basis for more advanced and sophisticated ideas. For two semester, I delivered second half of the course, by covering topics such as Generalised linear models and Simulation and bootstrapping.

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OTHER TEACHING

• Data Science MSc, University of London, UK
  Online Tutor (Remote), April. 2025 -
  Tutored: Statistics and Statistical Data Mining, Financial data modelling

• Department of Management Information Systems, Istinye University, Istanbul, Turkey
  Part Time Lecturer (Remote), Sep. 2022 -
  Designed and Delivered: Data, AI and Ethics (22-23 Fall), Introduction to Generative AI (23-24 Fall), Data viz with R (23-25), Introduction to Deep Learning (24-25 Fall), Business Intelligence (24-25 Spring)
  Delivered: Data Science (22-23 Spring)

• Department of Business Administration, TED University, Ankara, Turkey
  Part Time Lecturer (Remote), Oct. 2021 - Dec. 2022
  Designed and Delivered: Statistical Learning with R (UnderGrad and Grad level)

• Department of Mathematics, Istanbul Bilgi University, İstanbul, Turkey
  Part Time Lecturer (Remote), Febr. 2021 - June. 2021
  Delivered: Computational and Mathematical Numeracy for Social Scientists II

• Department of Mathematics, Atılım University, Ankara, Turkey
  Lecturer , 2019 - 2020 Academic year
  Delivered: Introduction to Statistics and Probability I