Data Analyst interview questions

Best practices for advanced data cleaning in large, multi-source datasets include standardizing formats, handling missing or inconsistent values, deduplicating records, implementing validation rules, cross-referencing with trusted data sources, and using automated scripts or tools for repetitive tasks. Senior analysts also perform exploratory data analysis to identify anomalies and ensure data quality throughout the ETL process.

Statistical analysis in complex business data involves using hypothesis testing, regression modeling, correlation analysis, and multivariate techniques to identify patterns, trends, and relationships. A seasoned data analyst leverages advanced statistical software, ensures the assumptions of statistical tests are met, and interprets results in a way that aligns with business objectives.

Effective approaches include imputing missing values using statistical methods like mean, median, or model-based imputation, flagging or excluding outliers through Z-score or IQR methods, and using domain knowledge to distinguish between true anomalies and data entry errors. Senior analysts document these choices to maintain reproducibility.

The most impactful visualization techniques include interactive dashboards, heatmaps, boxplots, network graphs, and time-series plots. Experienced analysts tailor visualizations to the audience's level of expertise, employ storytelling principles, and ensure clarity and accessibility to drive decision-making.

Data normalization and transformation improve analysis by scaling features to a comparable range, correcting skewed distributions, and making variables suitable for statistical modeling. Techniques such as log transformation, min-max scaling, or z-score standardization help models converge faster and yield more reliable insights.

To ensure robustness and validity, senior data analysts use cross-validation, residual diagnostics, sensitivity analysis, and regularization techniques. They also assess multicollinearity, leverage bootstrapping, and thoroughly test models on different subsets of data to guard against overfitting.

Automation involves writing reusable scripts or employing ETL tools to parse, clean, and transform data programmatically. Steps include identifying repetitive cleaning tasks, developing modular code, integrating error handling, scheduling regular jobs, and monitoring pipelines to ensure data integrity.

Appropriate selection depends on dataset complexity, interactivity requirements, integration needs, and audience. A senior analyst evaluates tools like Tableau, Power BI, matplotlib, ggplot2, or D3.js for their strengths and ensures compatibility with data pipelines and reporting platforms.

Advanced techniques include logistic and multiple regression, time-series forecasting, principal component analysis (PCA), clustering, survival analysis, and Bayesian inference. These are chosen based on the analytical question, data type, and project goals.

Quality assurance measures include implementing data validation checks, automated unit tests, regular audit trails, peer code reviews, dashboard stress-testing, and maintaining thorough documentation. Senior data analysts frequently review outcomes with data owners to catch errors early and ensure trustworthy visualizations.