Required stack
필요 기술
PythonData ProcessingExcelSQLReport WritingBusiness AnalysisData VisualizationData AnalysisTableauPower BI
Project brief
프로젝트 내용
I have a comprehensive export of our task data and individual performance logs that need to be turned into clear, actionable insight. The core of the job is to examine task completion rates and highlight how closely we stick to the original schedule—
specifically a side-by-side comparison of planned versus actual completion dates. I also want to see how each employee’s performance influences those results so we can recognise top contributors and spot recurring bottlenecks.
You’ll receive:
• CSV exports from our project-management tool (tasks with planned and actual dates, assignee, status)
• A simple employee-performance file with monthly ratings and role details
What I need back:
• Cleaned, well-structured datasets (ready for future reuse)
• A concise analytical report that quantifies schedule variance, pinpoints the biggest overruns, and correlates them with employee performance scores
• Visual dashboards (Tableau, Power BI, or similar) that let managers filter by project, assignee, and date range
• A short slide deck summarising key findings and recommendations
Acceptance is based on:
• Accurate variance metrics for every task and aggregated at project and employee level
• Clear visuals that can be refreshed when we drop in new data
• Documentation of your methodology so the team can replicate or extend the work
Familiarity with SQL or Python (pandas), strong Excel skills, and modern data-viz tools will make this straightforward. If something in the workflow needs adjusting, I’m happy to discuss—but the final deliverables above are non-negotiable.
You’ll receive:
• CSV exports from our project-management tool (tasks with planned and actual dates, assignee, status)
• A simple employee-performance file with monthly ratings and role details
What I need back:
• Cleaned, well-structured datasets (ready for future reuse)
• A concise analytical report that quantifies schedule variance, pinpoints the biggest overruns, and correlates them with employee performance scores
• Visual dashboards (Tableau, Power BI, or similar) that let managers filter by project, assignee, and date range
• A short slide deck summarising key findings and recommendations
Acceptance is based on:
• Accurate variance metrics for every task and aggregated at project and employee level
• Clear visuals that can be refreshed when we drop in new data
• Documentation of your methodology so the team can replicate or extend the work
Familiarity with SQL or Python (pandas), strong Excel skills, and modern data-viz tools will make this straightforward. If something in the workflow needs adjusting, I’m happy to discuss—but the final deliverables above are non-negotiable.