5. **How can data visualization techniques enhance the communication of analysis results, and what tools are commonly used to create these visualizations?
4. **What are some common statistical methods and tools used in data analysis, and how do they help in interpreting data trends and patterns?
3. **Can you explain the difference between descriptive, predictive, and prescriptive analytics, and provide examples of how each is used in data analysis?
2. **How do you handle missing or incomplete data in a dataset, and what impact can this have on the results of your analysis?
**What are the key steps involved in the data analysis process, and how do they contribute to making informed decisions?
5. **What role does machine learning play in data analysis, and how can it be integrated into a data-driven decision-making process?
4. **How can data visualization tools and techniques enhance the interpretability and communication of data analysis findings to stakeholders?
3. **What are some common statistical techniques used in data analysis, and when is it appropriate to use them?
2. **How do you handle missing or incomplete data in a dataset, and what strategies can be employed to ensure data accuracy and integrity?
**What are the main differences between descriptive, predictive, and prescriptive analytics, and how do they apply to solving business problems?