**What are the main steps in the data analysis process and why is each step important?
- Understanding the key stages—data collection, cleaning, exploration, modeling, and interpretation—helps ensure accurate and meaningful insights. 2. **How do you handle missing or incomplete d...
- Discussing strategies such as imputation, deletion, or using algorithms that can handle missing values is essential for maintaining data integrity. 3. **What is the role of exploratory data a...
- EDA is crucial for understanding the dataset's characteristics, detecting outliers, and uncovering patterns using tools like visualizations, summary statistics, and Python libraries like Panda...
- This involves evaluating model accuracy, complexity, the nature of the data, and project goals, while considering overfitting and underfitting. 5. **What are some common pitfalls in data anal...
What are the different types of data analysis methods, and how do you determine which method to use for a specific dataset?
How can you ensure the accuracy and reliability of the data used in your analysis?
What role do data visualization tools play in data analysis, and which tools are most effective for communicating findings?
How do you handle missing or incomplete data during the analysis process?
What are the ethical considerations one must take into account when conducting data analysis, especially concerning data privacy and consent?