Example response for question delving into Data Cleaning with the key points that need to be covered in an effective response. Customize this to your own experience with concrete examples and evidence

•  Understanding the missing data: I start by understanding the nature and patterns of missing data in the dataset. This involves identifying the missing data points, determining if the missingness is random or systematic, and assessing the potential reasons for missing data
•  Missing data imputation: If the missing data is deemed to be missing at random, I employ various imputation techniques to fill in the missing values. This can include methods like mean imputation, regression imputation, or using machine learning algorithms to predict missing values
•  Dealing with non-random missing data: If the missing data is determined to be non-random, I explore strategies such as creating a separate category for missing values, conducting sensitivity analysis, or using advanced techniques like multiple imputation or maximum likelihood estimation
•  Assessing the impact of missing data: I evaluate the potential impact of missing data on the analysis or modeling task at hand. This involves examining the proportion of missing data, assessing the potential bias introduced by missing data, and considering the implications for statistical power and generalizability of results
•  Documenting & reporting: I document the steps taken to handle missing data, including the imputation methods used and any assumptions made. I also report the results of the analysis or modeling task with appropriate caveats and limitations related to missing data
•  Iterative approach: I understand that handling missing data is an iterative process, and I continuously review and refine my approach based on the specific dataset and analysis requirements. I also stay updated with the latest research and best practices in missing data handling
•  Software & tools: I am proficient in using software and tools like Python, R, and SQL to handle missing data. I leverage libraries and packages such as pandas, scikit-learn, and statsmodels to implement missing data imputation techniques and perform data analysis
•  Communication & collaboration: I collaborate with domain experts and stakeholders to gain insights into the missing data mechanisms and ensure the imputation methods align with the context of the data. I also communicate the findings and limitations related to missing data effectively to non-technical audiences

  What the Interviewer is trying to find out about you and your experiences through this question

•  Problem-solving skills: Ability to handle missing data effectively and make informed decisions
•  Technical knowledge: Understanding of various techniques and algorithms for handling missing data
•  Attention to detail: Ability to identify missing data patterns and implement appropriate strategies
•  Data integrity: Ensuring accuracy and reliability of analysis by addressing missing data

  How to avoid some common minefields when answering this question in order to not raise any red flags

•  Lack of knowledge: Not being aware of different techniques to handle missing data such as imputation or deletion
•  Overconfidence: Claiming that missing data is not a problem or can be ignored without providing a valid justification
•  Inconsistency: Providing inconsistent or contradictory approaches to handle missing data
•  Limited experience: Not being able to provide examples or practical experiences of handling missing data in previous projects
•  Ignoring potential biases: Failing to address the potential biases that can arise from handling missing data inappropriately
•  Lack of communication skills: Not being able to explain the chosen approach to handle missing data clearly and effectively

 Related

  Other questions asked for the Data Scientist in Technology function. View details for the Data Scientist here

• What is the difference between supervised and unsupervised learning?
• What is the curse of dimensionality?
• How do you handle imbalanced datasets?
• Explain the concept of overfitting and how to prevent it.
• How do you select the optimal number of clusters in K-means clustering?
• What is the purpose of a validation set in machine learning?
• Explain the concept of A/B testing.
• Explain the bias-variance tradeoff.
• What is feature engineering and why is it important?
• Explain the difference between bagging and boosting.
• What is the difference between classification and regression?
• What is the difference between precision and recall?
• What is the purpose of dimensionality reduction techniques?
• Explain the difference between L1 and L2 regularization.
• How do you handle outliers in a dataset?
• How do you deal with multicollinearity in regression?
• What is the purpose of cross-validation?
• What is the difference between bag-of-words and TF-IDF?
• What is regularization and why is it important?