  Conventional processes for analyzing datasets and extracting meaningful
information are often time-consuming and laborious. Previous work has
identified manual, repetitive coding and data collection as major obstacles
that hinder data scientists from undertaking more nuanced labor and high-level
projects. To combat this, we evaluated OpenAI's GPT-3.5 as a "Language Data
Scientist" (LDS) that can extrapolate key findings, including correlations and
basic information, from a given dataset. The model was tested on a diverse set
of benchmark datasets to evaluate its performance across multiple standards,
including data science code-generation based tasks involving libraries such as
NumPy, Pandas, Scikit-Learn, and TensorFlow, and was broadly successful in
correctly answering a given data science query related to the benchmark
dataset. The LDS used various novel prompt engineering techniques to
effectively answer a given question, including Chain-of-Thought reinforcement
and SayCan prompt engineering. Our findings demonstrate great potential for
leveraging Large Language Models for low-level, zero-shot data analysis.
