**What are the key steps involved in the data analysis process, and how can each step impact the outcome of a study?
2. **How do you choose the right statistical techniques or models to apply in a given data analysis project?
3. **What are the common challenges faced when cleaning and preparing data for analysis, and how can they be effectively addressed?
4. **How can data visualization be used to enhance the interpretation and communication of analysis results?
5. **What ethical considerations should be taken into account when collecting, analyzing, and interpreting data?
What are the key differences between descriptive, predictive, and prescriptive data analysis, and how are each used in decision-making processes?
How do you handle missing or incomplete data in a dataset, and what impact can this have on the accuracy of your analysis?
What are the common statistical methods used in data analysis, and how do you choose the appropriate method for a specific dataset?
How can data visualization tools enhance the interpretation and communication of data analysis findings?
What role does machine learning play in modern data analysis, and how can it be integrated into traditional analytical workflows to improve outcomes?