Abstract
In this study, we explore the roles of AI-assisted ChatGPT (Generative Pre-trained Transformer) in the field of data science. AI-assisted ChatGPT, a powerful language model, is fine-tuned using domain-specific data for specialised data science tasks, such as sentiment analysis and named entity recognition (NER). The results reveal significant reductions in model size and memory usage with minor trade-offs in inference time, providing valuable resource-efficient deployment. Various data augmentation methods, including back-translation, synonym replacement, and contextual word embeddings, are employed to augment the training dataset. The study's results are subjected to rigorous statistical analysis, including paired t-tests and ANOVA tests, to determine the significance of the findings. The research concludes with insightful suggestions and future scope, including advanced fine-tuning strategies, model optimization techniques, and ethical considerations.
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