Exploratory Data Analysis (EDA) is an approach to data analysis for summarizing and visualizing the important characteristics of a data set. Promoted by John Tukey, EDA focuses on exploring data to understand the data’s underlying structure and variables, to develop intuition about the data set, consider how that date set came into existence, and decide how it can be investigated with more formal statistical methods.
- understand EDA as a journey and a way to explore data.
- explore data at multiple levels using appropriate visualizations.
- acquire statistical knowledge for summarizing data.
- demonstrate curiosity and skepticism when performing EDA.
- develop intuition for how a data set manifested.
Prerequisites and Requirements:
A background in statistics is helpful but not required. Consider taking Intro to Descriptive Statistics and Intro to Inferential Statistics prior to taking this course. Relevant topics include:
- Mean, median, mode
- Normal, uniform, and skewed distributions
- Histograms and box plots
Familiarity with the following CS and Math topics will help students:
- Variable assignment
- Comparison and logical operators ( , =, ==, &, | )
- If else statements
- Square roots, logarithms, and exponentials