**What are the key steps involved in the data analysis process, and how do they contribute to making informed decisions?
2. **How do you handle missing or incomplete data in a dataset, and what impact can this have on the results of your analysis?
3. **Can you explain the difference between descriptive, predictive, and prescriptive analytics, and provide examples of how each is used in data analysis?
4. **What are some common statistical methods and tools used in data analysis, and how do they help in interpreting data trends and patterns?
5. **How can data visualization techniques enhance the communication of analysis results, and what tools are commonly used to create these visualizations?
What are the key differences between descriptive and inferential statistics in data analysis?
How can data cleaning and preprocessing impact the outcomes of a data analysis project?
What are some common data visualization techniques used to present insights effectively?
How do you handle missing or incomplete data during analysis, and what techniques can be used to mitigate its impact?
What role does machine learning play in enhancing traditional data analysis methods?