5. **How do you select the right statistical methods or algorithms for a given data set, and what factors influence your choice of methodology?
4. **What is the role of data visualization in data analysis, and how do you decide which visualization techniques to use for conveying your findings effectively?
3. **Can you explain the differences between descriptive, diagnostic, predictive, and prescriptive analytics, and provide examples of when each type would be most appropriate to use?
2. **How do you handle missing or incomplete data during an analysis, and what techniques do you use to mitigate any potential biases that missing data might introduce?
**What are the key steps involved in the data analysis process, and how do you ensure the reliability and validity of your data?
5. **What are some best practices for ensuring data quality and integrity throughout the data analysis lifecycle?
4. **How can data visualization enhance the understanding and communication of data analysis results?
3. **What are the most common tools and software used in data analysis, and what are their strengths and weaknesses?
2. **How do different types of data (quantitative vs. qualitative) affect the approach to analysis and the choice of analytical techniques?
**What are the key steps in the data analysis process, and how do they contribute to deriving insights from data?