This workshop will get students started in data analysis using the pandas Python package. It will briefly cover different components of data analysis and connect them with the goal of extracting meaning from data. We will go over an example to illustrate the data analysis process from beginning to end.

Workshop format: Presentation and hands-on

Target audience: This workshop is ideal for those who are at the initial stages of doing independent research requiring quantitative analysis (term paper, dissertation, junior paper, senior thesis).

Knowledge prerequisites: None.

Hardware/software prerequisites: Participants should install the Anaconda Python distribution -- which includes Jupyter notebooks, Numpy, and Pandas --  on their laptops in advance. Instructions can be found in this guide. (https://researchcomputing.princeton.edu/learn/workshops-live-training/r…)

Learning objectives: Participants will walk away with 1) a basic knowledge of how to prepare the data for working with pandas in a Jupyter notebook; 2) how to use pandas for data analysis with a basic understanding of descriptive statistics, data visualization, ANOVA, and regression; 3) the idea that data analysis is not just about running statistical models.