What role does data visualization play in data analysis, and what are some effective visualization tools and techniques for communicating insights?
How do you determine which statistical tests or machine learning models are most appropriate for analyzing a specific dataset?
Can you explain the concept of data normalization and why it is important in the preprocessing phase of data analysis?
How do you handle missing or incomplete data in a dataset, and what are some common techniques for imputing or managing these gaps?
What are the key differences between descriptive, predictive, and prescriptive analytics, and how are they applied in various industries?
How does exploratory data analysis (EDA) differ from confirmatory data analysis, and when is it appropriate to use each approach in the data analysis workflow?
How can you assess the accuracy and reliability of predictive models in data analysis, and what statistical measures can be used to evaluate model performance?
What are the advantages and disadvantages of using various data visualization techniques (such as histograms, scatter plots, and heat maps) in presenting analytical findings?
How do you handle missing or incomplete data in a dataset, and what are the potential impacts of different imputation methods on the analysis results?
What are the key steps in the data analysis process, and how can each step contribute to generating meaningful insights?