# Analyze data
URL: https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/index.html

> Interpret the findings and visualizations created by DataRobot.

# Analyze data

These sections help you interpret the findings and visualizations created by DataRobot.

| Topic | Description |
| --- | --- |
| Data quality reference | Read descriptions of each data quality check, as well as the logic DataRobot applies to detect, and often repair, common data quality issues. |
| Post data ingest analysis (EDA1) |  |
| Assess data quality with EDA | Assess the quality of your data during each phase of Exploratory Data Analysis (EDA). |
| Data Quality Assessment | Automatically detect and often handle data quality issues such as outliers, leading or trailing zeros, target leakage, and many more. |
| Analyze features using histograms | Analyze the distribution of a feature's values and investigate outliers. |
| Analyze frequent values | Look into the values that appear most and least frequently for a feature. |
| Feature details | Interpret histograms, frequent values charts, and transformations. |
| EDA1 | View summary statistics based on a sample of your data. |
| ESDA | Interactively visualize, explore, and aggregate target, numeric, and categorical features on a map. |
| Fast EDA for large datasets | Understand Fast Exploratory Data Analysis (EDA) for large datasets, and how to apply early target selection. |
| Over Time chart | Review time-aware visualizations of how a feature changes over time feature (time-aware only). |
| Post modeling analysis (EDA2) |  |
| EDA2 | View summary statistics based on the portion of the data used for EDA1, excluding rows that are also in the holdout data and rows where the target is N/A. |
| Feature Associations | Interpret feature correlations. |
| Use data pipelines for ingest and transformation | Learn how to create a reusable data pipeline to keep datasets cataloged inside DataRobot up-to-date and ready for experimentation, batch predictions, or automated retraining jobs. |

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