1. Report or Presentation: A clear, concise, and visually appealing report or presentation summarizing key findings, insights, and recommendations.

2. Data Visualizations: Interactive or static dashboards, charts, graphs, or other visualizations that effectively communicate complex data insights.

3. Data Models or Algorithms: Documented and tested data models, machine learning algorithms, or statistical models used to analyze and interpret data.

4. Data Sets or Files: Cleaned, transformed, and formatted data sets or files used for analysis, often with accompanying data dictionaries.

5. Code or Scripts: Well-documented and reusable code or scripts written in languages like Python, R, SQL, or others, used for data extraction, manipulation, and analysis.

6. Insights and Recommendations: Actionable insights and recommendations based on data analysis, including identification of trends, opportunities, and challenges.

7. Stakeholder-Specific Outputs: Tailored outputs for various stakeholders, such as business leaders, product managers, or marketing teams, highlighting relevant findings and implications.

8. Project Documentation: Comprehensive documentation of the project, including methodologies, assumptions, data sources, and limitations.

9. Follow-up and Maintenance: Ongoing support and maintenance of the project, including updates, refinements, and additional analysis as needed.

10. Knowledge Sharing: Sharing knowledge and expertise with colleagues, including training, mentoring, or contributing to internal knowledge bases.