Data preparation and cleaning are crucial steps in the market research process. Properly prepared and cleaned data ensures the accuracy and reliability of your analysis, leading to more informed and effective decision-making.

1. Data Preparation: The process of collecting, combining, structuring, and organizing data so it can be analyzed.
2. Data Cleaning: The process of detecting and correcting (or removing) corrupt or inaccurate records from a dataset.

• Gather Data: Collect data from various sources, including surveys, interviews, databases, and online sources.
• Combine Data: Merge data from different sources to create a comprehensive dataset.

• Format Data: Ensure that all data is in a consistent format (e.g., dates in the same format, consistent units of measurement).
• Organize Data: Arrange data in a structured format, such as tables or databases, to facilitate analysis.

• Remove Duplicates: Identify and remove duplicate records to avoid skewing results.
• Handle Missing Data: Address missing data by either imputing values, removing incomplete records, or using statistical methods to handle gaps.
• Correct Errors: Identify and correct errors in the data, such as typos, incorrect values, or outliers.
• Standardize Data: Ensure consistency in data entries, such as standardizing categories or units of measurement.

• Consistency Checks: Verify that data is consistent across different sources and within the dataset.
• Range Checks: Ensure that data values fall within expected ranges.
• Logic Checks: Confirm that data follows logical rules (e.g., age cannot be negative).

Let's consider a dataset collected from a customer satisfaction survey. The dataset includes columns for CustomerID, Date, Age, SatisfactionScore, and Comments.

1. Remove Duplicates

   import pandas as pd
   
   # Load dataset
   df = pd.read_csv('customer_satisfaction.csv')
   
   # Remove duplicate rows
   df = df.drop_duplicates()
   

2. Handle Missing Data

   # Check for missing values
   missing_data = df.isnull().sum()
   
   # Fill missing values in 'Age' with the mean age
   df['Age'] = df['Age'].fillna(df['Age'].mean())
   
   # Drop rows with missing 'SatisfactionScore'
   df = df.dropna(subset=['SatisfactionScore'])
   

3. Correct Errors

   # Correct typos in 'SatisfactionScore' (e.g., scores should be between 1 and 5)
   df['SatisfactionScore'] = df['SatisfactionScore'].apply(lambda x: 5 if x > 5 else (1 if x < 1 else x))
   

4. Standardize Data

   # Standardize date format
   df['Date'] = pd.to_datetime(df['Date'], format='%Y-%m-%d')
   

5. Data Validation

   # Check for consistency in 'Age' (e.g., age should be a positive number)
   df = df[df['Age'] > 0]
   
   # Ensure 'SatisfactionScore' is within the expected range
   df = df[(df['SatisfactionScore'] >= 1) && (df['SatisfactionScore'] <= 5)]
   

You are given a dataset sales_data.csv with columns OrderID, Date, Product, Quantity, Price, and Total. Perform the following tasks:

1. Remove duplicate rows.
2. Handle missing values in the Quantity and Price columns by filling them with the median value.
3. Correct any errors in the Total column (ensure Total = Quantity * Price).
4. Standardize the date format to YYYY-MM-DD.
5. Validate that Quantity and Price are positive numbers.

• Ignoring Missing Data: Always address missing data; ignoring it can lead to biased results.
• Overlooking Duplicates: Duplicate records can distort your analysis, so always check for and remove them.
• Incorrect Data Types: Ensure data types are appropriate for analysis (e.g., dates should be in datetime format).
• Not Validating Data: Always validate your data to ensure it meets logical and consistency checks.

Data preparation and cleaning are essential steps in ensuring the quality and reliability of your market research. By following structured processes to collect, structure, clean, and validate your data, you can significantly improve the accuracy of your analysis and the insights derived from it. In the next section, we will delve into statistical analysis techniques to further process and interpret your cleaned data.