diff --git a/examples/data_analysis_example/main.v b/examples/data_analysis_example/main.v
new file mode 100644
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--- /dev/null
+++ b/examples/data_analysis_example/main.v
@@ -0,0 +1,160 @@
+module main
+
+import vsl.ml
+import vsl.plot
+
+fn main() {
+	// Example data: two features (X1 and X2) and a label (y)
+	mut data := ml.data_from_raw_xy([
+		[1.0, 2.0, 0.0],
+		[2.0, 3.0, 0.0],
+		[3.0, 3.0, 0.0],
+		[2.0, 1.0, 0.0],
+		[6.0, 7.0, 1.0],
+		[8.0, 6.0, 1.0],
+		[7.0, 8.0, 1.0],
+		[8.0, 7.0, 1.0],
+		[4.0, 5.0, 0.0],
+		[5.0, 6.0, 0.0],
+		[6.0, 5.0, 0.0],
+		[5.0, 4.0, 0.0],
+		[9.0, 10.0, 1.0],
+		[10.0, 9.0, 1.0],
+		[11.0, 10.0, 1.0],
+		[10.0, 11.0, 1.0],
+		[12.0, 11.0, 1.0],
+		[11.0, 12.0, 1.0],
+		[13.0, 14.0, 1.0],
+		[14.0, 13.0, 1.0],
+		[15.0, 14.0, 1.0],
+		[14.0, 15.0, 1.0],
+		[16.0, 15.0, 1.0],
+		[15.0, 16.0, 1.0],
+		[17.0, 18.0, 1.0],
+		[18.0, 17.0, 1.0],
+		[19.0, 18.0, 1.0],
+		[18.0, 19.0, 1.0],
+		[3.0, 4.0, 0.0],
+		[4.0, 3.0, 0.0],
+		[7.0, 6.0, 1.0],
+		[6.0, 8.0, 1.0],
+		[9.0, 8.0, 1.0],
+		[8.0, 9.0, 1.0],
+		[11.0, 12.0, 1.0],
+		[12.0, 13.0, 1.0],
+		[15.0, 14.0, 1.0],
+		[14.0, 16.0, 1.0],
+		[18.0, 17.0, 1.0],
+		[17.0, 19.0, 1.0],
+		[5.0, 5.0, 0.0],
+		[10.0, 10.0, 1.0],
+		[15.0, 15.0, 1.0],
+		[4.0, 8.0, 1.0],
+		[8.0, 4.0, 0.0],
+		[12.0, 16.0, 1.0],
+		[16.0, 12.0, 1.0],
+		[20.0, 5.0, 0.0],
+		[5.0, 20.0, 0.0],
+		[12.0, 8.0, 1.0],
+		[8.0, 12.0, 1.0],
+		[18.0, 16.0, 1.0],
+		[16.0, 18.0, 1.0],
+	])!
+
+	// Visualize data in a 3D scatter plot
+	mut plt_3d := plot.new_plot()
+
+	x1 := data.x.get_col(0)
+	x2 := data.x.get_col(1)
+	y := data.y
+
+	// Split data into two classes for visualization
+	mut x1_class0 := []f64{}
+	mut x2_class0 := []f64{}
+	mut x1_class1 := []f64{}
+	mut x2_class1 := []f64{}
+
+	for i in 0 .. data.nb_samples {
+		if y[i] == 0 {
+			x1_class0 << x1[i]
+			x2_class0 << x2[i]
+		} else {
+			x1_class1 << x1[i]
+			x2_class1 << x2[i]
+		}
+	}
+
+	// Add traces for each class in the 3D plot
+	plt_3d.add_trace(
+		trace_type: .scatter3d
+		x: x1_class0
+		y: x2_class0
+		z: [][]f64{len: x1_class0.len, init: [0.0]}
+		mode: 'markers'
+		marker: plot.Marker{
+			size: []f64{len: x1_class0.len, init: 8.0}
+			color: []string{len: x1_class0.len, init: 'blue'}
+		}
+		name: 'Class 0'
+	)
+
+	plt_3d.add_trace(
+		trace_type: .scatter3d
+		x: x1_class1
+		y: x2_class1
+		z: [][]f64{len: x1_class1.len, init: [0.0]}
+		mode: 'markers'
+		marker: plot.Marker{
+			size: []f64{len: x1_class1.len, init: 8.0}
+			color: []string{len: x1_class1.len, init: 'red'}
+		}
+		name: 'Class 1'
+	)
+
+	// Configure the layout of the 3D plot
+	plt_3d.set_layout(
+		title: 'Two-class Data'
+		xaxis: plot.Axis{
+			title: plot.AxisTitle{
+				text: 'X1'
+			}
+		}
+		yaxis: plot.Axis{
+			title: plot.AxisTitle{
+				text: 'X2'
+			}
+		}
+	)
+
+	// Show the 3D plot
+	plt_3d.show()!
+
+	// Basic statistics analysis
+	mut stat := ml.stat_from_data(mut data, 'Example Data')
+	stat.update()
+
+	// Visualize statistics in a bar chart
+	mut plt_bars := plot.new_plot()
+
+	plt_bars.add_trace(
+		trace_type: .bar
+		x_str: []string{len: stat.mean_x.len, init: 'Class ${index}'}
+		y: stat.mean_x
+		name: 'Mean'
+	)
+
+	plt_bars.add_trace(
+		trace_type: .bar
+		x_str: []string{len: stat.sig_x.len, init: 'Class ${index}'}
+		y: stat.sig_x
+		name: 'Standard Deviation'
+	)
+
+	// Configure the layout of the bar chart
+	plt_bars.set_layout(
+		title: 'Feature Statistics'
+	)
+
+	// Show the bar chart
+	plt_bars.show()!
+}