Exploratory Data Analysis
In statistics, exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task. Exploratory data analysis was promoted by John Tukey to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments. EDA is different from initial data analysis (IDA), which focuses more narrowly on checking assumptions required for model fitting and hypothesis testing, and handling missing values and making transformations of variables as needed. EDA encompasses IDA.
See full entry on Wikipedia
These are the most commonly used hashtags on social media when including Exploratory Data Analysis. The top three related terms are datascience, machinelearning, and data.
Exploratory Data Analysis is most commonly found in Data Scientist job descriptions. To learn more about the role, click the button below.Explore the role