I have extensive experience with data visualization tools such as Tableau and Power BI. I've used these tools to create interactive dashboards and reports that help stakeholders understand complex data sets. For example, I developed a dashboard for a retail client that tracked sales performance against KPIs, which significantly improved their decision-making process.

I approach data cleaning systematically, starting with an assessment of the data quality. I identify missing values, outliers, and inconsistencies, then apply appropriate techniques like imputation, outlier treatment, and normalization. This ensures the data is accurate and reliable for analysis, which is crucial for deriving meaningful insights.

I utilize a variety of statistical methods depending on the analysis requirements. These include descriptive statistics for summarizing data, inferential statistics for hypothesis testing, and predictive modeling techniques like regression analysis and time series forecasting. Each method is chosen to answer specific questions and support data-driven decisions.

Ensuring accuracy involves rigorous validation processes. I cross-check findings with multiple data sources, perform sensitivity analyses, and involve peer reviews. Additionally, I maintain transparency in methodologies and assumptions to ensure the insights are reproducible and credible. This approach builds trust in the insights provided.

In a previous role, I analyzed customer feedback data for an e-commerce platform. By applying text analytics, I identified key pain points and opportunities for improvement. The insights led to targeted marketing campaigns and UX enhancements, resulting in a 15% increase in customer satisfaction and a 10% rise in repeat purchases.