Required stack
필요 기술
PythonPandasNumPyData ProcessingData AnalysisData MiningData VisualizationMatplotlibSeabornJupyter NotebookSciPySoftware Architecture
Project brief
프로젝트 내용
I have a raw CSV export of customer-level transactions from our retail platform and I need clear, data-driven insight into which products are really driving revenue. The goal is simple: surface product-level sales trends so I can see which SKUs consistently outperform and which ones lag behind, all viewed through the lens of revenue generated rather than just unit counts.
You’re free to structure the workflow as you see fit, but I expect the core analysis to happen in Python, leveraging pandas and NumPy for data wrangling and aggregation. I’ll supply the dataset along with a brief data dictionary; you return a well-commented Jupyter notebook (or .py script) that:
• Cleans and normalises the raw data
• Calculates revenue by product over selectable time windows
• Highlights statistically significant up- or down-trends
• Presents the findings in clear tables and a few concise Matplotlib or Seaborn visuals
Deliverables are complete when the notebook runs end-to-end on my machine, reproduces the same figures, and includes a short executive summary (markdown cell is fine) I can lift straight into a slide deck.
You’re free to structure the workflow as you see fit, but I expect the core analysis to happen in Python, leveraging pandas and NumPy for data wrangling and aggregation. I’ll supply the dataset along with a brief data dictionary; you return a well-commented Jupyter notebook (or .py script) that:
• Cleans and normalises the raw data
• Calculates revenue by product over selectable time windows
• Highlights statistically significant up- or down-trends
• Presents the findings in clear tables and a few concise Matplotlib or Seaborn visuals
Deliverables are complete when the notebook runs end-to-end on my machine, reproduces the same figures, and includes a short executive summary (markdown cell is fine) I can lift straight into a slide deck.