Data science agents are being developed using LLMs to automate the complex data analysis workflow. Current agents struggle with the diverse data formats found in real-world data science problems and lack robust verification methods. DS-STAR is a new data science agent designed to overcome these limitations through three key innovations. It features a data file analysis module for diverse data formats and incorporates an LLM-based verification stage. A sequential planning process iteratively refines plans utilizing feedback, improving performance on complex analytical tasks. DS-STAR excels in analyzing heterogeneous data from multiple sources, as demonstrated on benchmarks. It outperforms state-of-the-art methods like AutoGen and DA-Agent on challenging datasets. Ablation studies confirmed the importance of each component, including the Data File Analyzer and Router agent. DS-STAR's modular design allows for use with multiple LLMs, showcasing its adaptability. The iterative refinement process is more extensive for complex tasks, requiring more rounds to generate solutions.