input: Write a Python function to define and execute a data pipeline for pre-processing, fitting, and predicting data.
  assert isinstance(data_pipeline([1, 2, 3]), list)
  assert isinstance(data_pipeline([4, 5, 6]), list)
  assert isinstance(data_pipeline([7, 8, 9]), list)
output: def data_pipeline(data):
      from sklearn.preprocessing import StandardScaler
      from sklearn.linear_model import LinearRegression
      import numpy as np
  
      # Step 1: Pre-processing
      scaler = StandardScaler()
      data = np.array(data).reshape(-1, 1)
      scaled_data = scaler.fit_transform(data)
  
      # Step 2: Fitting a model
      # Creating some target variable for the sake of example
      target = np.array([2*x + 1 for x in data.flatten()])
      model = LinearRegression()
      model.fit(scaled_data, target)
  
      # Step 3: Predicting
      predictions = model.predict(scaled_data)
  
      return predictions.tolist()