How to Answer Data Preprocessing and Quality Questions

Data preprocessing is a critical step that helps data scientists validate the quality of a dataset by using statistical techniques to understand, clean, and transform raw data into reliable information. This step ensures high data quality standards, which help build trust in data-driven decisions and avoid costly errors or misinterpretations.

Data preprocessing skills can be tested in multiple interviews. During the statistics interview round, you’re expected to verbally discuss the different preprocessing steps you’d take, rather than actually writing code. Data preprocessing questions in the statistics interview round are usually given in the following formats:

1. Conceptual. (e.g. “What is the difference between correlation and covariance?)
2. Applied. (e.g. “You're given a dataset of Amazon sales data by seller. What do you think the distribution looks like?”)

Key concepts in data preprocessing and quality that you should know include:

• Descriptive statistics
• Data cleaning
• Data transformation
• Sampling
• Bias

This lesson will cover how to prepare for and answer questions about data preprocessing. In the rest of the lessons in this module, we’ll discuss the key concepts mentioned above.

How to answer

To answer data preprocessing questions effectively, follow these tips:

• Proactively mention data preprocessing and quality best practices. Discuss the importance of data integrity, consistency, and reproducibility in data science projects. It is also good practice to proactively mention data preprocessing and quality steps when presenting a past data science project or analyzing a take-home assignment.
• Provide examples. Whenever possible, illustrate your points with concrete examples or scenarios from your experience or from relevant case studies. Sharing real-world examples can help demonstrate your practical understanding of data preprocessing and quality improvement.
• Discuss tradeoffs. Acknowledge any tradeoffs or considerations associated with the preprocessing techniques you propose. For example, you could discuss the tradeoff between data completeness and the risk of introducing bias when imputing missing values.

How to prepare

First, review the lessons in this module to gain a conceptual understanding of data types, preprocessing techniques, and common data quality issues and treatments.

Then, apply this knowledge by doing the following exercises:

1. Compare your intuition about real-world data distributions (e.g. customer support response times) with the actual, publicly available distribution. Oftentimes your analysis steps depend on the data distribution, so you should have a good intuition about what the distribution looks like for the dataset you're analyzing.
2. Use Python packages like Pandas Profiling, which automatically gives and performs important preprocessing steps to consider when analyzing a dataset. Assess the reports generated from these packages and apply these techniques to sample Kaggle datasets. While you’re not actually writing code in statistics interviews, working with packages like Pandas Profiling helps familiarize yourself with the different preprocessing steps in a practical setting.