Can you describe a situation where the initial analysis results were misleading, and explain how you identified and corrected the issue?
How do you determine the appropriate statistical methods or machine learning models to apply to a particular dataset, and what factors influence your choice?
What is the role of exploratory data analysis (EDA) in data analysis, and what tools and techniques do you use to perform EDA?
How do you handle missing or incomplete data in your analysis, and what are some common techniques to mitigate the impact of such issues?
What are the key steps involved in the data analysis process, and how do you ensure the accuracy and reliability of your data sources?
5. **What are the key ethical considerations to keep in mind during data analysis, particularly with regard to data privacy and security?
4. **How can machine learning techniques be integrated into data analysis workflows to enhance insights, and what are some common pitfalls to avoid when using these techniques?
3. **What role do data visualization tools play in the data analysis process, and what are some best practices for creating effective visualizations?
2. **How can outliers in a dataset be identified and what are some of the strategies to handle them without compromising the integrity of the analysis?
**What is the difference between descriptive, predictive, and prescriptive data analysis, and in what scenarios is each type most effectively used?