I have substantial experience teaching Data Engineering concepts to undergraduate and postgraduate students, focusing on building scalable data pipelines and efficient data management systems. My teaching approach covers foundational and advanced topics such as data ingestion, ETL processes, data warehousing, distributed computing, and big data architectures.
I provide hands-on training using industry-relevant tools and platforms including Apache Hadoop, Apache Spark, and Apache Airflow. Students gain practical exposure to designing data pipelines, managing structured and unstructured datasets, and implementing real-time and batch processing systems.
I also mentor students in data engineering projects, enabling them to design robust, scalable, and industry-ready data solutions aligned with modern enterprise requirements.
I have actively engaged in teaching Natural Language Processing (NLP) at the undergraduate and postgraduate levels, integrating both foundational theories and current research trends. My instruction covers key topics such as text preprocessing, language modeling, syntactic and semantic analysis, sentiment analysis, and sequence-to-sequence models. I emphasize hands-on learning using Python libraries like NLTK, spaCy, and Hugging Face Transformers, allowing students to implement real-world NLP applications. I also guide students in NLP-based projects and research initiatives, fostering critical thinking and innovation in the field.
https://drive.google.com/drive/folders/1EJXH3WRYHBzY03HlNhf3FGId4SgMCejm?usp=sharing