The first data exploration exercise introduces students to python and, specifically, an open-source library called Pandas. This library is built to work intuitively with both relational data and labeled data. Spreadsheets, while standard tools for undergraduate students in chemistry, cannot accommodate the memory required to process big data sets that are being generated in modern chemistry and biology labs. Pandas accommodates large datasets and can offer students additional processing and analysis capabilities. In this workflow, students learn basic commands, such as how to understand the difference of Pandas series and pandas DataFrames, how to construct and select parts of a DataFrame, and how to analyze and graphically plot data within a DataFrame. The workflow used for the course was adapted from a pre-existing workflow titled "Python Pandas Tutorial: A Complete Introduction for Beginners" from the Git repository of LearnDataSci (Figure ). For the purposes of this class, we converted this introductory article into a Jupyter notebook and created exercises for the students to test their knowledge and understanding of the material. For example, in one of the lessons, students downloaded a movie DataFrame which listed various attributes describing 1,000 films, including the genre, the title, the rating, the year created, etc. The students learned how to use python syntax to access specific data and create new attributes using a scoring function. In the exercise, the students changed the scoring function to test their understanding of how code can be used to quickly and systematically analyze many rows of data. All course workflows are deposited on the Git repository: Brunk-lab.