feat: 15 funciones datascience — estadística, DSP e IO de datos

12 funciones puras con implementación real:
Standardize, MinMaxScale, Clip, RollingWindow, ZipSlices, GroupBy,
Histogram, Pearson, Autocorrelation, FFT (Cooley-Tukey), DetectOutliers, Impute

3 funciones impuras (stubs):
LoadCSV, LoadParquet, FetchDataFrame

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

functions/datascience/detect_outliers.go

@@ -0,0 +1,38 @@
+package datascience
+
+import "math"
+
+// DetectOutliers devuelve un []bool donde true indica que el valor es un outlier
+// según z-score. Un valor es outlier si |z-score| > threshold.
+func DetectOutliers(data []float64, threshold float64) []bool {
+	n := len(data)
+	if n == 0 {
+		return []bool{}
+	}
+
+	var sum float64
+	for _, v := range data {
+		sum += v
+	}
+	mean := sum / float64(n)
+
+	var sqSum float64
+	for _, v := range data {
+		d := v - mean
+		sqSum += d * d
+	}
+	stddev := math.Sqrt(sqSum / float64(n))
+
+	result := make([]bool, n)
+	if stddev == 0 {
+		return result
+	}
+	for i, v := range data {
+		z := (v - mean) / stddev
+		if z < 0 {
+			z = -z
+		}
+		result[i] = z > threshold
+	}
+	return result
+}