4. **How do you select the appropriate statistical methods or machine learning algorithms for a given data analysis problem?
5. **What are some common data visualization techniques used to effectively communicate insights derived from data analysis?
What are the key steps involved in the data analysis process, and how can each step be effectively implemented to ensure accurate results?
How do you determine which data analysis techniques are most appropriate for a given dataset and business problem?
What are some common challenges encountered during data cleaning, and what strategies can be used to address them effectively?
How can data visualization be used to enhance the understanding of data analysis results and communicate insights to stakeholders?
What role does exploratory data analysis (EDA) play in a data analysis project, and what are some commonly used EDA techniques?
What is the difference between descriptive and inferential statistics in data analysis, and when should each be used?
How can data cleaning and preprocessing impact the outcomes of data analysis, and what are some best practices for these processes?
What role does exploratory data analysis (EDA) play in understanding data sets, and which techniques are commonly used during EDA?
