Understanding the Question

When you're faced with the question, "How do you handle missing or corrupted data in a dataset?" during an interview for an Applied Data Scientist position, it's important to recognize that the interviewer is probing your practical knowledge and skills in data cleaning and preprocessing. Missing or corrupted data is a common issue in real-world datasets, and how you deal with these problems can significantly impact the performance of your data models. It's not just about knowing various techniques but understanding when and why to use them.

Interviewer's Goals

The interviewer aims to assess several key areas through this question:

• Technical Knowledge: Your familiarity with different methods for handling missing or corrupted data, such as imputation, deletion, or correction techniques.
• Problem-solving Skills: Your ability to apply appropriate strategies based on the context or nature of the data and the specific requirements of the project.
• Critical Thinking: How you weigh the pros and cons of each method in different scenarios, indicating your capacity to make informed decisions.
• Practical Experience: Examples or experiences you share that demonstrate your capability in dealing with such issues effectively in real-world datasets.

How to Approach Your Answer

Your answer should reflect a structured approach, starting with how you identify missing or corrupted data, followed by an evaluation of the implications of these issues on your dataset and concluding with the specific strategies you employ to address them. It's crucial to emphasize the rationale behind your choices and, if possible, mention the outcomes of these strategies in your past projects.

Example Responses Relevant to Applied Data Scientist

Here are example responses that could form the basis of an effective answer:

1. Identification and Assessment: "When I encounter missing or corrupted data in a dataset, my first step is to assess the extent and nature of the problem. For missing data, I use visualization tools and summary statistics to understand the pattern of missingness. Is it random, or is there a pattern that suggests a deeper issue? For corrupted data, I perform data validation checks, looking for values outside feasible ranges or inconsistent with other records."

2. Strategies for Handling Missing Data: "Based on my assessment, I choose an appropriate strategy. For data missing completely at random, I might use listwise deletion for a small percentage of missing data or imputation methods like mean/mode imputation, K-nearest neighbors (KNN), or Multiple Imputation by Chained Equations (MICE) for larger datasets. I prefer model-based imputation techniques like MICE when the data is not missing at random, as it uses the relationships between variables to estimate missing values."

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3. Dealing with Corrupted Data: "For corrupted data, I first try to correct the errors when possible, especially if there's a way to validate against a reliable source. If correction isn't feasible, I might treat it as missing data and apply suitable imputation methods, or in extreme cases, remove the corrupted entries if they represent a small portion of the dataset and their exclusion won't introduce bias."

4. Real-world Example: "In a recent project, I dealt with a dataset where 20% of the values in a crucial variable were missing. Given the variable's significance in predicting the target outcome, I used MICE to impute missing values, which preserved the variable's distribution and relationships with other variables. This approach significantly improved our model's accuracy compared to listwise deletion or simple mean imputation."

Tips for Success

• Be Specific: Use technical terms appropriately and describe specific methods or techniques you've used, highlighting your expertise.
• Context Matters: Always relate your answer to the specific context of the data or project. What works for one scenario might not be suitable for another.
• Balance Depth with Clarity: While it's important to show depth of knowledge, ensure your answer remains clear and understandable to a non-specialist audience.
• Reflect on Outcomes: Whenever possible, share the results or impact of your approach to handling missing or corrupted data in past projects, as this demonstrates the effectiveness of your methods.

By structuring your response to showcase your technical knowledge, problem-solving skills, and real-world experience, you'll effectively communicate your value as an Applied Data Scientist candidate.