The field of data science is witnessing a significant shift towards the integration of Large Language Models (LLMs) to enhance various tasks and workflows. Researchers are exploring the potential of LLM-based agents to improve data preprocessing, model development, and evaluation, among other processes. A key area of focus is the development of innovative frameworks and toolkits that can bridge the gap between LLMs and databases, enabling more efficient and secure data management. Another notable trend is the application of LLM-powered agents in time series forecasting, where they can leverage metadata to clean data and optimize forecasting performance. Furthermore, there is a growing interest in adapting LLMs to general machine learning tasks, with promising results in text-to-SQL generation and other areas. Noteworthy papers include: BridgeScope, a universal toolkit that enables LLMs to interact with databases more efficiently and securely. Empowering Time Series Forecasting with LLM-Agents, which proposes a data-centric approach to improve forecasting performance. Mockingbird, a framework that adapts LLMs to general machine learning tasks, and Lightweight Transformers for Zero-Shot and Fine-Tuned Text-to-SQL Generation Using Spider, which evaluates the potential of compact transformers in resource-scarce environments.