Data Science Project Assistance
Budget: ₹12,500 – ₹37,500 INR
I’m looking for a data-savvy partner to guide a full data science workflow from raw information all the way to clear insights. The exact business question is still being refined, so I need someone comfortable helping me explore possibilities, shape hypotheses, and choose the right analytical path.
You’ll start by assessing the data I have on hand—some of it structured in spreadsheets, some gathered from public sources, and additional streams that may arrive later. From there, you’ll recommend and implement the best combination of cleaning, preprocessing, exploratory analysis, feature engineering, and modeling techniques. Python, pandas, scikit-learn, and visualization libraries such as seaborn or Plotly will likely be the core tool-set, though I’m open to R if that suits the problem better.
Once we’ve extracted meaningful patterns, I’d like the results packaged in a way that a non-technical stakeholder can understand at a glance: interactive dashboards, concise slide decks, or well-annotated Jupyter notebooks are all acceptable. Throughout the engagement, please keep your code modular, reproducible, and version-controlled (GitHub repo preferred).
Deliverables
• Cleaned and documented dataset(s)
• Reproducible analysis/modeling notebook(s) with explanatory comments
• Visual summary of key findings (dashboard or slides)
• Brief hand-off call or recorded walk-through so I can maintain and extend your work
If you’re comfortable owning an end-to-end data science cycle and can start soon, I’d love to hear how you would tackle an open-ended project like this.
You’ll start by assessing the data I have on hand—some of it structured in spreadsheets, some gathered from public sources, and additional streams that may arrive later. From there, you’ll recommend and implement the best combination of cleaning, preprocessing, exploratory analysis, feature engineering, and modeling techniques. Python, pandas, scikit-learn, and visualization libraries such as seaborn or Plotly will likely be the core tool-set, though I’m open to R if that suits the problem better.
Once we’ve extracted meaningful patterns, I’d like the results packaged in a way that a non-technical stakeholder can understand at a glance: interactive dashboards, concise slide decks, or well-annotated Jupyter notebooks are all acceptable. Throughout the engagement, please keep your code modular, reproducible, and version-controlled (GitHub repo preferred).
Deliverables
• Cleaned and documented dataset(s)
• Reproducible analysis/modeling notebook(s) with explanatory comments
• Visual summary of key findings (dashboard or slides)
• Brief hand-off call or recorded walk-through so I can maintain and extend your work
If you’re comfortable owning an end-to-end data science cycle and can start soon, I’d love to hear how you would tackle an open-ended project like this.