Teaching
Courses taught, guest lectures, and educational contributions across academia and industry platforms.
Academic Teaching
Tashkent University of Information Technologies (TATU), Tashkent, Uzbekistan
2019 – 2026
Delivered graduate-level courses for the Master’s programme in Computer Engineering (5А330501) and Data Science (5A3300504), Faculty of Computer Engineering. Courses were conducted in Uzbek and Russian, with a total load of 180 academic hours per course (30 lectures + 15 practical sessions + 135 independent study hours).
- Data Analytics (Маълумотлар анализи) — Introduction to machine learning methods, supervised and unsupervised learning, regression, classification, decision trees, neural networks, and practical data analysis using Python.
- Machine Learning (Машинали ўқитиш) — Mathematical foundations of ML, algorithm complexity, linear models, metric methods, probabilistic models, and hands-on implementation of ML algorithms.
- Data Engineering — Cloud platforms (AWS, GCP, Alibaba Cloud, Azure), Docker, workflow orchestration, data warehousing (Hologres, MaxCompute), analytics engineering (AnalyticDB PostgreSQL), batch processing (Spark), streaming (Kafka, Apache Flink), and end-to-end project work. Syllabus available in English, Russian, and Uzbek.
Emperor Alexander I St. Petersburg State Transport University (PGUPS), St. Petersburg, Russia
2014 – 2015
Assistant to the Professor, Department of Information Technology. Delivered approximately 100 hours of lectures covering:
- Data link layer network optimization algorithms
- Physical hypervisor networks
- Data centre network architectures and computing network systems
Tashkent State Transport University (TSTU), Tashkent, Uzbekistan
September 2010 – July 2012
Assistant to the Professor. Conducted laboratory and practical classes for:
- Programming in C/C++
- Network and communication systems on the railway
- Information systems in railway transport
Also assisted the professor in research work, including creating application and simulation components of analytical models.
Online & Industry Teaching
DeepSchool — LLM Course
Contributed to the LLM specialisation track on DeepSchool, a Russian-language professional AI education platform, covering large language model concepts and practical deployment.
OTUS — Professional Education Platform
2019 – 2022
Taught two professional development courses on the OTUS platform:
- MLOps — End-to-end machine learning operations, model deployment, monitoring, and CI/CD for ML pipelines.
- Big Data Engineering — Distributed data processing, Spark, Hadoop ecosystem, and large-scale data pipeline design.