input: Write a python function to check for missing values, data types, descriptive statistics, and visualize the data distribution using a histogram and boxplot.
  assert check_data('TimeSeriesData.csv') is None
output: def check_data(file_path):
      import pandas as pd
      import matplotlib.pyplot as plt
      import seaborn as sns
      
      # Load data
      data = pd.read_csv(file_path)
      
      # Check for missing values
      print("Missing Values:")
      print(data.isnull().sum())
      
      # Check data types
      print("\nData Types:")
      print(data.dtypes)
      
      # Descriptive Statistics
      print("\nDescriptive Statistics:")
      print(data.describe())
      
      # Visualize data distribution
      for column in data.select_dtypes(include=['float64', 'int64']).columns:
          plt.figure(figsize=(12, 5))
          
          # Histogram
          plt.subplot(1, 2, 1)
          sns.histplot(data[column], kde=True)
          plt.title(f'Histogram of {column}')
          
          # Boxplot
          plt.subplot(1, 2, 2)
          sns.boxplot(y=data[column])
          plt.title(f'Boxplot of {column}')
          
          plt.show()