Exploratory Data Analysis
Expert-defined terms from the Professional Certificate in Regression Analysis in Human Resources course at London School of Planning and Management. Free to read, free to share, paired with a globally recognised certification pathway.
Exploratory Data Analysis #
Exploratory Data Analysis (EDA) refers to the process of analyzing data sets to… #
It is a crucial step in the data analysis process to understand the data and detect patterns, anomalies, and relationships that may exist within the data. EDA helps in identifying trends, outliers, and potential relationships between variables, guiding further analysis and modeling. This process involves the use of statistical graphics and summary statistics to explore and understand the data before formal modeling.
Example #
Before building a regression model to predict employee performance based on various factors, conducting exploratory data analysis can help identify any outliers, patterns, or relationships in the data that might influence the model's accuracy.
Practical Application #
In human resources, EDA can be used to analyze employee performance data to identify factors that may impact productivity, such as working hours, training programs attended, or job satisfaction levels.
Challenges #
Some challenges of EDA include dealing with missing data, handling outliers, selecting appropriate visualization techniques, and determining which variables are most relevant for analysis. It is essential to strike a balance between exploring the data thoroughly and not getting lost in the details during the EDA process.