The role of a data analyst is to collect, clean, and interpret data to help organizations make data-driven decisions. This involves using statistical tools, analyzing datasets to identify trends, and presenting findings through reports and visualizations.

"INNER JOIN returns rows where there is a match in both tables. OUTER JOIN (LEFT, RIGHT, or FULL) returns matched rows and also includes unmatched rows from one or both tables, filling with NULLs where there is no match."

"Data normalization organizes data to reduce redundancy and dependency by dividing a large table into smaller tables and defining relationships among them. This improves data consistency and database efficiency."

 Tableau, Power BI, and Python libraries like Matplotlib and Seaborn for creating data visualizations. Use them to build dashboards, highlight key insights, and present complex data in a clear and interactive way."

"Regression analysis is used to identify relationships between a dependent variable and one or more independent variables. It predicts trends and outcomes, for example, predicting sales based on marketing spend. Linear regression is a common example."

A/B testing compares two versions (A and B) to see which performs better based on a metric, such as conversion rate. Would split a user group randomly, expose each group to a different version, and measure outcomes to identify the better-performing option.

A p-value measures the probability that an observed result occurred by chance, assuming the null hypothesis is true. A lower p-value (typically < 0.05) indicates strong evidence against the null hypothesis, suggesting statistical significance.

Ensure accuracy by validating data sources, cleaning data meticulously, performing cross-checks, and using consistent methodologies. I also use visualization and summary statistics to identify potential anomalies and test the reproducibility of my analyses.