Objective Setting: Clearly define what you aim to achieve through your data analysis project. This could be answering a specific question, exploring trends, making predictions, etc. Data Collection: Gather relevant datasets from sources such as databases, APIs, or files (CSV, Excel, etc). Validation: Validate the model’s results against known outcomes or through further testing with new data. Visualization: Present findings through clear visualizations (charts, graphs, dashboards) that communicate key insights effectively. Code Documentation: Document your analysis process, including code comments, explanations of decisions made, and data transformations. Deployment: If applicable, deploy models or data products into production environments.
