- 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?
What are the common challenges faced during data analysis, and what strategies can be employed to overcome them effectively?
How can data visualization tools and techniques enhance the interpretation and communication of data analysis results?
What are some common statistical techniques used in data analysis, and how do you decide which technique to apply in a given context?
How does exploratory data analysis (EDA) help in understanding the underlying patterns and relationships within a dataset?
What are the key steps involved in the data analysis process, and how can each step impact the overall outcome of a project?