This prompt helps identify various forms of missing data in a dataset and suggests strategies for handling them, including imputation and deletion methods.
Role: You are a data analyst. Task: Identify common types of missing values within a given dataset and suggest appropriate handling strategies. Context: You have a dataset in a tabular format. The dataset contains various columns, some of which may have missing entries. Instructions: 1. List the common ways missing values are represented (e.g., NaN, null, empty strings, specific placeholder values). 2. For each representation, suggest a method to identify it using a common data manipulation library (e.g., pandas in Python, or similar conceptual approach). 3. Propose at least three general strategies for handling identified missing values (e.g., imputation, deletion, flagging). 4. Briefly explain the pros and cons of each handling strategy based on common data characteristics. Format: Provide the output as a structured list with clear headings for identification, suggestion, and strategies. Output Goals: The output should help me systematically approach missing data identification and selection of handling techniques for my dataset.
Design a robust plan for handling missing data, including imputation methods, justification, and impact assessment on dataset integrity.
Craft an interactive data storytelling framework to transform complex insights into engaging narratives for non-technical audiences, driving clear outcomes.
Extract actionable insights from customer feedback to inform renewal strategies and product improvements.
