Data Cleaning and Preprocessing: This involves ensuring the data is accurate, complete, and formatted in a way that can be easily analyzed. The data analyst may use tools such as Excel or Python to clean and preprocess the data.

Statistical Analysis: Use statistical techniques such as regression analysis, hypothesis testing, and clustering to identify patterns or relationships in the data.

Data Visualization: Create visual representations of the data using tools such as Tableau or Power BI. These visualizations can help clients better understand the data and make more informed decisions.

Reporting and Presentations: Create reports or presentations that summarize their findings and communicate the insights gained from the data analysis. These reports can be customized to the specific needs of the client and may include recommendations for action based on the analysis.

Data Modeling: Create predictive models based on the data, such as machine learning models or decision trees, to help clients make more accurate predictions or forecasts.