By Team foundit
March 28, 2025
Prepare for your data analyst interview with these top technical questions in SQL, Python, Excel, and data analysis. Stay ahead in 2025!
Use DELETE with ROW_NUMBER() or DISTINCT to remove duplicates.
INNER JOIN returns matching records from both tables. OUTER JOIN returns all records from one table, with NULLs for non-matching data.
Use IQR (Interquartile Range) or Z-Score. IQR identifies outliers outside 1.5x IQR range. Z-Score flags points beyond ±3 standard deviations.
Normalization reduces redundancy by dividing data into tables. Denormalization combines tables for faster read performance.
Use indexes, avoid SELECT *, optimize joins, and analyze execution plans with EXPLAIN for performance tuning.
Use Pandas functions like dropna(), fillna(), and replace() to handle missing data, duplicates, and inconsistent values.
Pivot Tables summarize data by grouping, filtering, and calculating totals. Useful for creating reports and analyzing large datasets.
Use Matplotlib for basic charts, Seaborn for statistical plots, and Plotly for interactive visualizations. Choose based on data complexity.
Calculate metrics for control and test groups. Use statistical tests like t-test or chi-square to check significance at a 95% confidence level.
Perform cross-validation, compare with benchmarks, check for data consistency, and visualize results for anomalies.