Data cleaning, quality handling, and governance: Worked Example — Data Manipulation and Preparation (NVIDIA-Certified Associate: Accelerated Data Science)

Data Cleaning, Quality Handling, and Governance: Worked Example

In the NVIDIA-Certified Associate: Accelerated Data Science exam, data cleaning, quality handling, and governance are critical skills that ensure datasets are reliable and ready for GPU-accelerated analysis. This worked example demonstrates a step-by-step approach to cleaning and governing a realistic dataset using cuDF and RAPIDS tools.

Scenario

You are given a large customer transaction dataset containing missing values, inconsistent entries, and duplicate records. Your task is to prepare this data for model training by cleaning, validating quality, and applying governance principles to maintain data integrity.

Step 1: Load Data Using cuDF

First, load the dataset into a GPU DataFrame for accelerated processing.

Code snippet

import cudfdf = cudf.read_csv('customer_transactions.csv')

Step 2: Identify Missing and Inconsistent Data

Check for missing values and inconsistent entries in key columns such as transaction_amount and customer_id.

Code snippet

missing_counts = df.isnull().sum()print(missing_counts)

For categorical columns like payment_method, identify inconsistent labels (e.g., 'Credit Card' vs 'credit card').

Step 3: Handle Missing Values

Apply appropriate strategies depending on the column type:

• Numerical columns: Impute missing values using the median or mean.
• Categorical columns: Fill missing entries with a placeholder such as 'Unknown'.

Code snippet

df['transaction_amount'] = df['transaction_amount'].fillna(df['transaction_amount'].median())df['payment_method'] = df['payment_method'].fillna('Unknown')

Step 4: Standardize Categorical Data

Normalize categorical values to ensure consistency. For example, convert all payment_method entries to lowercase.

Code snippet

df['payment_method'] = df['payment_method'].str.lower()

Step 5: Remove Duplicate Records

Duplicates can bias model training. Remove exact duplicates based on all columns or a subset of key identifiers.

Code snippet

df = df.drop_duplicates(subset=['customer_id', 'transaction_date', 'transaction_amount'])

Step 6: Validate Data Quality

Implement checks to ensure data falls within expected ranges and formats. For example, verify that transaction_amount is positive.

Code snippet

invalid_amounts = df[df['transaction_amount'] <= 0]print(f'Invalid transactions: {len(invalid_amounts)}')

Remove or flag invalid entries as per governance policies.

Step 7: Apply Data Governance Principles

Document cleaning steps, maintain audit trails, and enforce data access controls to ensure compliance and reproducibility.

• Use metadata to track data lineage.
• Store cleaned data securely in Parquet format for efficient processing and sharing.

Code snippet

df.to_parquet('cleaned_customer_transactions.parquet')

Summary

This example illustrates how to leverage GPU-accelerated cuDF for efficient data cleaning and quality handling, combined with governance best practices to prepare data for accelerated data science workflows. Mastery of these steps is essential for success in the NVIDIA-Certified Associate: Accelerated Data Science exam.

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