AI Prompts for ChatGPT for Data Analysis

I have a dataset with these columns: [LIST COLUMNS AND SAMPLE VALUES]. Help me understand: what each column likely represents, what questions this data can answer, and what to explore first.

I need to analyze [DESCRIBE THE DATA] to answer this question: [YOUR QUESTION]. What analysis steps should I take, in what order? What will each step tell me?

Here is a sample of my data: [PASTE SAMPLE ROWS]. What data quality issues should I check for? Include: missing values, outliers, duplicates, inconsistent formats, and impossible values.

I want to understand [DESCRIBE WHAT YOU WANT TO LEARN] from this data: [DESCRIBE THE DATA]. What type of analysis is most appropriate: descriptive, diagnostic, predictive, or prescriptive? What specific methods?

Write [PYTHON PANDAS / R / SQL] code to clean this dataset: [DESCRIBE OR PASTE THE DATA]. Handle: missing values, duplicate rows, inconsistent casing, and data type corrections.

Write Python/pandas code to do exploratory data analysis on this dataset: [DESCRIBE THE DATA]. Include: summary statistics, distribution plots, correlation matrix, and the top 5 questions to investigate further.

I have this data: [PASTE SAMPLE OR DESCRIBE]. What patterns, trends, or anomalies should I look for? How do I identify which patterns are meaningful versus random?

Walk me through analyzing [METRIC NAME]: [DESCRIBE THE METRIC AND CONTEXT]. What affects it, how should it trend, and what would count as a significant change?

I got these statistical results: [PASTE RESULTS]. Explain what they mean in plain language: what is significant, what is the practical effect size, and what conclusions can I draw?

I want to compare [GROUP A] and [GROUP B] on [METRIC]. What is the right statistical test? Write the code and explain how to interpret the result.

I want to visualize [DESCRIBE WHAT YOU WANT TO SHOW]. What chart type should I use: bar, line, scatter, histogram, heatmap, box plot, or other? Explain why.

Write Python code using [MATPLOTLIB / SEABORN / PLOTLY] to create a [CHART TYPE] showing [DESCRIBE WHAT TO SHOW] from this data: [DESCRIBE THE DATA]. Make it publication-quality.

I want to build a dashboard showing [DESCRIBE METRICS]. What visualizations should I include, what layout works best, and what filters should users have?

Review this chart and suggest how to make it clearer: [DESCRIBE THE CHART]. Focus on: axis labels, title, legend, color choice, and removing visual clutter.

I have these findings: [LIST YOUR KEY FINDINGS]. How do I sequence them into a narrative that leads logically from data to insight to recommendation? Outline the story arc.

Write an executive summary of this data analysis: [PASTE OR DESCRIBE FINDINGS]. Keep it to 3-5 bullet points, lead with the most important finding, and end with a clear recommendation.

Translate these data findings for a non-technical audience: [PASTE FINDINGS]. Remove jargon, use plain language, and focus on what the findings mean for the business decision at hand.

Write a data report on [TOPIC] covering: background/objective, methodology, key findings with supporting data, limitations, and recommendations. Audience: [DESCRIBE AUDIENCE].

I'm presenting this analysis to [AUDIENCE]: [DESCRIBE YOUR FINDINGS]. What questions will they ask? Prepare answers to the most likely 5-7 questions, including skeptical challenges.

Based on these findings: [LIST FINDINGS], write 3 specific, actionable recommendations. Each recommendation should clearly link to the data that supports it.