Note: The prompts in this cheat sheet are for reference only. Use them as inspiration to create your own, and refer to our article for guidance on writing effective prompts. Since these were not written by professional developers, please review and validate before applying in practice.
Data Cleaning & Preparation
1. Clean a dataset with missing values using imputation methods.
2.
Remove duplicate rows from a customer
transaction dataset.
3.
Standardize inconsistent date formats in a sales
dataset.
4.
Normalize numerical columns in a dataset for
better analysis.
5.
Detect and remove outliers in a dataset.
6.
Convert categorical variables into numerical
form for analysis.
7.
Handle null values in multiple columns
efficiently.
8.
Merge two datasets with different column names but
same data.
9.
Split a column containing full names into first
and last names.
10.
Convert all currency values in a dataset to USD.
Exploratory Data Analysis (EDA)
11.
Summarize key statistics of a dataset (mean,
median, mode).
12.
Create a correlation matrix for all numeric
variables.
13.
Plot a histogram of customer ages in a dataset.
14.
Visualize the distribution of sales by region.
15.
Identify top 5 most frequent product categories.
16.
Compare male vs female purchase patterns.
17.
Find the top 10 most profitable customers.
18.
Identify seasonal trends in time-series data.
19.
Detect skewness in the dataset.
20.
Create a boxplot for sales across product
categories.
SQL Queries
21.
Write an SQL query to find top 5 selling
products.
22.
Find customers who made more than 10 purchases.
23.
Retrieve total sales for each region.
24.
Get the average transaction value by customer.
25.
Find employees who earn above the company
average salary.
26.
List all customers who never placed an order.
27.
Write a query to find the second-highest salary.
28.
Join orders and customer tables to display
customer names with their purchases.
29.
Calculate month-over-month growth in sales.
30.
Find duplicate records in a table.
31.
Retrieve customers who purchased both Product A
and Product B.
32.
Write a query to calculate cumulative sales.
33.
Find the percentage of total revenue contributed
by each product category.
34.
Identify the top 3 employees by sales
performance.
35.
Get the list of inactive customers in the last 6
months.
Python for Data Analysis
36.
Write Python code to import a CSV file using
pandas.
37.
Create a bar chart of sales by product category.
38.
Calculate moving average of sales over 7 days.
39.
Use pandas to group data by region and calculate
sum of sales.
40.
Implement linear regression on a dataset.
41.
Create a scatter plot of advertising spend vs
sales.
42.
Write Python code to detect outliers using IQR.
43.
Build a heatmap of correlation using seaborn.
44.
Automate data cleaning with a function.
45.
Generate a time-series plot of monthly revenue.
46.
Merge customer and order datasets using pandas.
47.
Create a pivot table using pandas.
48.
Write Python code to find the most common words
in product reviews.
49.
Automate a weekly sales report in Python.
50.
Build a dashboard using Plotly/Dash.
Excel/Google Sheets
51.
Create a pivot table for sales by region and
category.
52.
Write a VLOOKUP formula to fetch customer
details.
53.
Use conditional formatting to highlight top 10%
sales.
54.
Create a line chart for monthly revenue trends.
55.
Build a dashboard showing KPIs (sales, profit,
customer count).
56.
Write an IF formula to categorize customers as
high/medium/low spenders.
57.
Calculate CAGR (compound annual growth rate) in
Excel.
58.
Use INDEX-MATCH instead of VLOOKUP for better
performance.
59.
Create a histogram of customer age distribution.
60.
Automate monthly reporting using Excel macros.
Visualization & Storytelling
61.
Build a sales performance dashboard in Power
BI/Tableau.
62.
Create a funnel chart for customer journey
stages.
63.
Visualize customer churn rate over time.
64.
Create a heatmap of sales by region and product.
65.
Build a tree map of revenue contribution by
product category.
66.
Show YOY (year-over-year) growth using line
charts.
67.
Create an interactive dashboard with slicers.
68.
Use storytelling with charts to explain sales
decline.
69.
Build a cohort analysis visualization.
70.
Create a KPI dashboard showing targets vs
actuals.
Business Problem Solving
71.
Analyze why sales dropped in Q3 compared to Q2.
72.
Find the most profitable customer segment.
73.
Identify factors contributing to customer churn.
74.
Recommend products for cross-selling
opportunities.
75.
Analyze the effectiveness of marketing
campaigns.
76.
Calculate customer lifetime value (CLV).
77.
Measure ROI of digital ad campaigns.
78.
Identify bottlenecks in supply chain data.
79.
Analyze employee performance based on KPIs.
80.
Forecast next quarter’s revenue using past data.
Advanced Analytics & ML
81.
Train a logistic regression model to predict
churn.
82.
Build a decision tree to classify customer
behavior.
83.
Perform clustering to segment customers.
84.
Run sentiment analysis on customer reviews.
85.
Apply ARIMA model for sales forecasting.
86.
Build a recommendation system for products.
87.
Detect anomalies in transaction data.
88.
Train a random forest model for fraud detection.
89.
Use NLP to categorize customer complaints.
90.
Predict stock prices using machine learning.
Career & Portfolio
91.
Write a case study of your analysis project.
92.
Create a portfolio dashboard in Tableau/Power
BI.
93.
Share your Python EDA notebook on GitHub.
94.
Build a blog post explaining one of your data
projects.
95.
Present a storytelling case using PowerPoint.
96.
Analyze a Kaggle dataset and publish insights.
97.
Document best practices in SQL queries.
98.
Record a walkthrough video of your project.
99.
Write a LinkedIn post summarizing your project
insights.
100.
Create a resume-ready project demonstrating
end-to-end data analysis.
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