diff --git a/laser_echo_analysis/src/laser_echo_analysis/core.py b/laser_echo_analysis/src/laser_echo_analysis/core.py
new file mode 100644
index 0000000..5537ef3
--- /dev/null
+++ b/laser_echo_analysis/src/laser_echo_analysis/core.py
@@ -0,0 +1,103 @@
+from __future__ import annotations
+
+import logging
+import math
+from dataclasses import dataclass
+from typing import List, Dict, Any
+
+import pandas as pd
+
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+class DataValidationError(Exception):
+    """Custom exception for data validation errors."""
+    pass
+
+
+@dataclass
+class LaserMeasurement:
+    timestamp: pd.Timestamp
+    pixel_value: int
+    delta_t: float
+
+
+@dataclass
+class AnalysisResult:
+    peak: int
+    average_noise: float
+    signal_to_noise_ratio: float
+
+
+
+def _validate_input_data(data: List[Dict[str, Any]]) -> pd.DataFrame:
+    """Validate and convert input data into pandas DataFrame."""
+    if not isinstance(data, list):
+        raise DataValidationError("Input data must be a list of dictionaries.")
+    if not data:
+        raise DataValidationError("Input data list is empty.")
+
+    df = pd.DataFrame(data)
+    required_columns = {"timestamp", "pixel_value", "delta_t"}
+    missing = required_columns - set(df.columns)
+    if missing:
+        raise DataValidationError(f"Missing required fields: {', '.join(missing)}")
+
+    try:
+        df["timestamp"] = pd.to_datetime(df["timestamp"], errors="raise")
+        df["pixel_value"] = df["pixel_value"].astype(int)
+        df["delta_t"] = df["delta_t"].astype(float)
+    except Exception as e:
+        raise DataValidationError(f"Data type conversion failed: {e}")
+
+    return df
+
+
+def analyze_data(data: List[Dict[str, Any]]) -> Dict[str, Any]:
+    """
+    Analysiert Rohmessdaten aus einer CSV-Datei und berechnet Kennzahlen wie Spitzenwert,
+    durchschnittliches Rauschen und Signal-zu-Rausch-Verhältnis.
+
+    Args:
+        data (list[dict]): Liste der Messdatensätze, eingelesen aus der CSV-Datei.
+
+    Returns:
+        dict: Analyseergebnisse inklusive peak, average_noise und signal_to_noise_ratio.
+    """
+    logger.info("Starting laser data analysis on %d records", len(data))
+
+    df = _validate_input_data(data)
+
+    if df.empty:
+        raise DataValidationError("DataFrame is empty after validation.")
+
+    peak = int(df["pixel_value"].max())
+
+    noise_values = df.loc[
+        (df["pixel_value"] < peak * 0.1) & (df["pixel_value"] > 0), "pixel_value"
+    ]
+    average_noise = float(noise_values.mean()) if not noise_values.empty else 0.0
+
+    if average_noise <= 0:
+        signal_to_noise_ratio = math.inf
+    else:
+        signal_to_noise_ratio = float(peak / average_noise)
+
+    result = AnalysisResult(
+        peak=peak,
+        average_noise=average_noise,
+        signal_to_noise_ratio=signal_to_noise_ratio,
+    )
+
+    result_dict = {
+        "peak": result.peak,
+        "average_noise": result.average_noise,
+        "signal_to_noise_ratio": result.signal_to_noise_ratio,
+    }
+
+    logger.info("Analysis completed: %s", result_dict)
+    assert all(k in result_dict for k in ("peak", "average_noise", "signal_to_noise_ratio"))
+
+    return result_dict