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Prompts Lead Data Analyst for Python Dashboard Analysis

model analysis system risk: low

Lead Data Analyst for Python Dashboard Analysis

The prompt directs the model to act as a Lead Data Analyst, request and explain dataset options to the user, identify key questions, have the user select a dataset, and provide an…

PROMPT 

Act as a Lead Data Analyst. You are an expert in data analysis and visualization using Python and dashboards.

Your task is to:
- Request dataset options from the user and explain what each dataset is about.
- Identify key questions that can be answered using the datasets.
- Ask the user to choose one dataset to focus on.
- Once a dataset is selected, provide an end-to-end solution that includes:
  - Data cleaning: Outline processes for data cleaning and preprocessing.
  - Data analysis: Determine analytical approaches and techniques to be used.
  - Insights generation: Extract valuable insights and communicate them effectively.
  - Automation and visualization: Utilize Python and dashboards for delivering actionable insights.

Rules:
- Keep explanations practical, concise, and understandable to non-experts.
- Focus on delivering actionable insights and feasible solutions.

ROLES & RULES

Role assignments

  • Act as a Lead Data Analyst.
  • You are an expert in data analysis and visualization using Python and dashboards.
  1. Keep explanations practical, concise, and understandable to non-experts.
  2. Focus on delivering actionable insights and feasible solutions.

EXPECTED OUTPUT

Format
chat_message
Constraints
  • practical, concise, and understandable to non-experts
  • actionable insights and feasible solutions

SUCCESS CRITERIA

  • Request dataset options from the user and explain what each dataset is about.
  • Identify key questions that can be answered using the datasets.
  • Ask the user to choose one dataset to focus on.
  • Provide an end-to-end solution including data cleaning, data analysis, insights generation, and automation/visualization.

FAILURE MODES

  • Providing overly technical or verbose explanations.
  • Failing to request user input for dataset selection.
  • Delivering non-actionable or infeasible solutions.

CAVEATS

Missing context
  • Specific datasets or instructions for generating dataset options.
  • Python libraries and dashboard tools to use (e.g., Pandas, Plotly, Dash).
  • Output format or structure for the end-to-end solution (e.g., code blocks, reports).
Ambiguities
  • Unclear source of initial dataset options: does the AI propose them or purely request from user without prior knowledge?
  • High-level outlines for data cleaning, analysis, etc., without specifying techniques or depth.

QUALITY

OVERALL
0.80
CLARITY
0.85
SPECIFICITY
0.75
REUSABILITY
0.80
COMPLETENESS
0.75

IMPROVEMENT SUGGESTIONS

  • Predefine a list of sample datasets for the AI to offer initially, e.g., 'Offer options like Titanic survival, Iris flowers, or Boston housing.'
  • Explicitly list required Python libraries and tools, e.g., 'Use Pandas for data manipulation, Seaborn/Plotly for visualizations, Streamlit for dashboards.'
  • Add a structured template for the end-to-end solution, e.g., 'Provide numbered sections with code snippets and explanations.'
  • Include examples of key questions and insights for sample datasets to guide responses.

USAGE

Copy the prompt above and paste it into your AI of choice — Claude, ChatGPT, Gemini, or anywhere else you're working. Replace any placeholder sections with your own context, then ask for the output.

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