How do you handle outliers in a dataset?
Theme: Data Cleaning Role: Data Scientist Function: Technology
Interview Question for Data Scientist: See sample answers, motivations & red flags for this common interview question. About Data Scientist: Analyzes data to extract insights and make data-driven decisions. This role falls within the Technology function of a firm. See other interview questions & further information for this role here
Sample Answer
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
- Identifying outliers: First, I would visually inspect the data using box plots or scatter plots to identify any potential outliers
- Statistical methods: If outliers are identified, I would use statistical methods such as the z-score or modified z-score to detect and quantify outliers based on their deviation from the mean or median
- Handling outliers: There are several approaches to handle outliers, including: 1) Removing outliers: I would consider removing outliers if they are due to data entry errors or measurement errors. However, I would be cautious not to remove outliers that are valid data points. 2) Transforming data: If the outliers are skewing the distribution, I would consider transforming the data using techniques like log transformation or winsorization to reduce the impact of outliers. 3) Treating outliers as a separate group: In some cases, outliers may represent a distinct group or behavior. I would analyze and model the data separately for outliers and non-outliers to capture their unique characteristics. 4) Robust statistical methods: I would use robust statistical methods that are less sensitive to outliers, such as median or trimmed mean, instead of mean or standard deviation
- Impact assessment: I would assess the impact of handling outliers on the overall analysis. It is important to understand how the handling of outliers affects the results and interpretations of the analysis
- Documentation: Lastly, I would document the approach taken to handle outliers, including the rationale behind the chosen method and any assumptions made. This documentation ensures transparency and reproducibility of the analysis
Underlying Motivations
What the Interviewer is trying to find out about you and your experiences through this question
- Technical skills: Assessing your knowledge and understanding of outlier detection techniques and methodologies
- Problem-solving abilities: Evaluating your ability to identify and handle outliers effectively in order to ensure accurate analysis and modeling
- Attention to detail: Determining your meticulousness in data preprocessing and cleaning to maintain data integrity
- Critical thinking: Assessing your ability to make informed decisions on whether to remove, transform, or treat outliers based on the specific context and impact on the analysis
Potential Minefields
How to avoid some common minefields when answering this question in order to not raise any red flags
- Lack of knowledge: Not being able to explain what outliers are and their impact on data analysis
- Inflexibility: Not considering the possibility of outliers and assuming a normal distribution for all datasets
- Overgeneralization: Applying the same outlier handling technique to all datasets without considering the specific characteristics of each dataset
- Ignoring domain knowledge: Not taking into account the domain expertise or subject matter knowledge when handling outliers
- Inadequate techniques: Using outdated or ineffective outlier detection and treatment methods
- Lack of communication: Failing to communicate the impact of outliers on the analysis and the steps taken to handle them effectively