📌 What You Need:

• ChatGPT (Free or Plus works, but Plus recommended for larger scripts)
• Python or R IDE (Jupyter, VS Code, RStudio)
• Sample datasets (CSV or database access)
• Optionally: pandas, seaborn, matplotlib, sklearn, plotly installed

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🧹 1. Data Cleaning Scripts

Goal: Automate generation of Python/R scripts for cleaning messy datasets.

Example Prompt:

“Generate a Python script to clean a dataset with missing values, inconsistent date formats, and duplicate rows.”

Output Snippet:

import pandas as pd

df = pd.read_csv("data.csv")
df.drop_duplicates(inplace=True)
df['date'] = pd.to_datetime(df['date'], errors='coerce')
df.fillna(method='ffill', inplace=True)

🎯 Follow-up:

“Add outlier removal using IQR for numerical columns.”

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📐 2. Statistical Analysis

Goal: Understand, choose, and apply correct statistical methods.

Prompt Examples:

“What statistical test should I use to compare means between 3 groups?”

ChatGPT responds with:

• ANOVA explanation
• When to use it
• Python/R code snippet

✨ Advanced:

“Explain p-values, confidence intervals, and Type I/II errors with examples.”

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📊 3. Visualization Code

Goal: Generate fast visual insights using Seaborn, Matplotlib, or Plotly.

Prompt Example:

“Create a Seaborn plot showing the distribution of sales by region, colored by year.”

Output:

import seaborn as sns
import matplotlib.pyplot as plt

sns.histplot(data=df, x='sales', hue='year', kde=True)
plt.title('Sales Distribution by Region and Year')
plt.show()

🖼️ Modify easily:

“Make it interactive using Plotly.”

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🤖 4. Model Selection

Goal: Choose the right ML algorithm based on problem type.

Prompt Example:

“What ML model should I use for a classification task with imbalanced data?”

Output:

• Recommends Random Forest, XGBoost, or SMOTE with Logistic Regression
• Explains pros/cons
• Includes setup code for class_weight and evaluation metrics (ROC AUC, F1-score)

🔍 Also try:

“Compare regression models for time series forecasting.”

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🧠 5. Feature Engineering

Goal: Brainstorm new features that improve model performance.

Prompt Example:

“Suggest feature engineering strategies for a dataset with timestamps, prices, and user IDs.”

Output:

• Rolling averages
• Lag features
• Price volatility metrics
• User-level aggregations

💡 Follow-up:

“Generate code to create lag-3 and rolling mean features over past 7 days.”

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📄 6. Report Generation

Goal: Convert code and results into human-readable reports.

Prompt Example:

“Summarize this regression analysis for an executive audience.”

Provide:

R^2 = 0.72, RMSE = 12.5, key predictors = 'marketing_spend', 'seasonality'

Output:

“The regression model explains 72% of the variance in sales. Key drivers include marketing spend and seasonal patterns. The RMSE indicates a typical prediction error of $12.5K.”

📝 Technical version also available if prompted.

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🧮 7. SQL Query Writing

Goal: Write complex SQL for data extraction, aggregation, and joins.

Prompt Example:

“Write a SQL query to calculate average spend per customer by region over the last 6 months.”

Output:

SELECT region, customer_id, AVG(spend) AS avg_spend
FROM transactions
WHERE transaction_date >= CURRENT_DATE - INTERVAL '6 months'
GROUP BY region, customer_id;

🚀 Also works for:

• CTEs
• Window functions
• Performance optimization

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🧪 8. A/B Testing Design

Goal: Plan robust experiments and calculate statistical significance.

Prompt Example:

“Design an A/B test for two versions of a product page and explain how to analyze results.”

Output:

• Explains control/treatment groups
• Defines success metric (e.g. click-through rate)
• Recommends sample size calculator
• Gives Python code to run t-test or proportion z-test

🔍 Bonus:

“Generate a summary report if p < 0.05.”

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🧭 Summary Cheatsheet

Task	What ChatGPT Can Do
🧹 Data Cleaning	Generate robust scripts (missing values, dates, etc)
📐 Statistical Analysis	Explain tests, provide code, pick right method
📊 Visualization Code	Matplotlib, Seaborn, Plotly visualizations
⚙️ Model Selection	Suggest models based on task, dataset, constraints
🧠 Feature Engineering	Brainstorm ideas + generate transformation code
📄 Report Generation	Write technical + executive summaries
🧮 SQL Query Writing	Create complex joins, CTEs, aggregations
🧪 A/B Testing Design	Plan tests and analyze significance