5. **What role does machine learning play in data analysis, and how can it be integrated into a data-driven decision-making process?
4. **How can data visualization tools and techniques enhance the interpretability and communication of data analysis findings to stakeholders?
3. **What are some common statistical techniques used in data analysis, and when is it appropriate to use them?
2. **How do you handle missing or incomplete data in a dataset, and what strategies can be employed to ensure data accuracy and integrity?
**What are the main differences between descriptive, predictive, and prescriptive analytics, and how do they apply to solving business problems?
- Looks into practices such as data cleaning, validation, normalization, and verification processes. 5. **Can you explain the use of statistical significance in hypothesis testing during data a...
- Emphasizes the role of tools and techniques to present data findings clearly and effectively. 4. **How do you ensure data quality before conducting an analysis?
- It's important to understand techniques like data imputation, ignoring or filling missing values, and using algorithms that handle missing data well. 3. **What is the significance of data vis...
- This question explores the various methodologies like descriptive, diagnostic, predictive, and prescriptive analysis. 2. **How do you handle missing or incomplete data in a dataset?
**What are the different types of data analysis, and how do they differ from one another?