diff --git a/README.md b/README.md
index e64c24c..a8ebd6a 100644
--- a/README.md
+++ b/README.md
@@ -19,18 +19,25 @@
 
 </p>
 
-
-
-
-Exploratory Data Analysis (EDA) is the initial task a data scientist or data
-analyst undertakes when they obtain new data. EDA refers to the critical
-process of conducting preliminary investigations on data to uncover patterns,
-spot anomalies, test hypotheses, and verify assumptions with the help of
-summary statistics and graphical representations.
-
-The Effective Data Visualization and Reporting Tool (Edvart for short) is a
-tool that generates a report in the form of a Jupyter notebook, containing
-various analyses of the input data.
+Edvart is an open-source Python library designed to simplify and streamline
+your exploratory data analysis (EDA) process.
+
+## Key features
+* **One-line Reports**: Generate a comprehensive set of pandas DataFrame
+visualizations using a single Python statement.
+Edvart supports:
+    - Data overview,
+    - Univariate analysis,
+    - Bivariate analysis,
+    - Multivariate analysis,
+    - Grouped analysis
+    - Time series analysis.
+* **Customizable Reports**: Produce, iterate, and style detailed reports
+    in Jupyter notebooks and HTML formats.
+* **Flexible API**: From high-level simplicity in a single line of code
+    to detailed control, choose the API level that fits your needs.
+* **Interactive Visualizations**: Many of the visualizations are interactive
+    and can be used to explore the data in detail.
 
 ## Installation
 
@@ -46,6 +53,23 @@ See the notebook
 [api-example.ipynb](https://nbviewer.org/github/datamole-ai/edvart/blob/main/api-example.ipynb)
 for usage examples.
 
+### Creating a default report
+
+```python
+import edvart
+
+# Load a dataset to a pandas DataFrame
+dataset = edvart.example_datasets.dataset_titanic()
+# Create a default report
+report = edvart.DefaultReport(dataset)
+# Show the report in the current Jupyter notebook
+report.show()
+# Export the report to an HTML file
+report.export_html("report.html")
+# Export the code generating the report to a Jupyter notebook
+report.export_notebook("report.ipynb")
+```
+
 ## User documentation
 
 The user documentation is available at https://datamole-ai.github.io/edvart/.