Data Cleaning Reports: Reports about errors in the data, near misses, and missing data. Description of data cleaning methods and transformations used. Data Profiling Reports: Summary of overall statistics of the data (e.g.: mean, median, mode, distribution, etc.). Characteristics of various features of the data and their distribution. Analytical Reports: Answers to the project objectives and analytical questions. Findings and interpretations based on the information. Data driven insights for business/decision making. Data Visualizations: Charts, graphs, and dashboards that clearly represent data and findings. Visual representation of key statistics related to the analysis (e.g.: bar charts, pie charts, line graphs, heatmaps, etc.). Modeling Reports (if applicable): Description of the models used (e.g.: regression, clustering, classification, etc.). Performance parameters of the model (e.g.: accuracy, F1 score, RMSE, etc.) and analysis. Comparison and interpretation of model results. Code and Scripts: The code, database connectivity, and analytical scripts used during the project. Along with documentation of the code so that the client can use it in the future. Data Sets and Sources: Providing the client with the final set of data, cleaned data if needed, and information about the data sources. Recommendations and Next Steps: Recommendations based on the analysis and suggestions for business decisions. Thoughts on further possibilities and what could be the next step. Presentation: A concise and engaging presentation to the client that includes the key findings and recommendations. These presentations are usually shared at board meetings or with decision-makers. Project Summary: A summary of the key aspects of the project from start to finish, objectives, methodology, results, and conclusions. Custom Reports and Dashboards: Custom reports or interactive dashboards created according to specific client needs. These reports help clients easily perform real-time analysis of data. The list of these deliverables will depend on the client's requirements, complexity and type of project, but in general these deliverables are provided at the end of a data analysis project.