**What are the key steps involved in the data analysis process, and how do they contribute to generating insights?
2. **How do you handle missing or incomplete data during analysis, and what are the best practices for ensuring data quality and integrity?
3. **What is the difference between descriptive, diagnostic, predictive, and prescriptive analytics, and when is each type most appropriately used?
4. **How do you choose the right statistical methods and tools for a specific data analysis project, and what factors influence this decision?
5. **What role does data visualization play in data analysis, and what are some effective techniques for presenting data insights to stakeholders?
What are the key steps involved in the data analysis process, and how do they contribute to making data-driven decisions?
How can data visualization techniques enhance the interpretation of complex data sets in a business context?
What are the main differences between descriptive, predictive, and prescriptive analytics, and how can each be applied to solve real-world problems?
How can data quality and integrity impact the results of data analysis, and what strategies can be implemented to ensure high-quality data?
What role do statistical techniques play in data analysis, and how can they be used to uncover insights and trends within a data set?