📊
Data Analyst
Roadmap 2026
Dashboard
Roadmap
Practice
Projects
Interview
Planner
Salary
Search
⌘K
🧠 Practice
186 questions with hints, answers and explanations. Solved:
0
🗄️
SQL
(101)
📗
Excel
(18)
🐍
Python
(20)
📈
Power BI
(14)
📐
Statistics
(12)
🧮
Aptitude
(10)
🎤
Interview
(11)
All levels
🟢 Easy
🟡 Medium
🔴 Hard
0/101 solved in SQL
🟢 Easy
1. Select all columns from the customers table.
🟢 Easy
2. Select only name and city from customers.
🟢 Easy
3. List unique cities of customers.
🟢 Easy
4. Find orders with amount greater than 5000.
🟢 Easy
5. Find customers from Delhi or Mumbai.
🟢 Easy
6. Show orders sorted by amount descending.
🟢 Easy
7. Show the 10 biggest orders.
🟢 Easy
8. Count all rows in orders.
🟢 Easy
9. Count how many orders have a non-null discount.
🟢 Easy
10. Find total revenue from orders.
🟢 Easy
11. Find the average order value.
🟢 Easy
12. Find the smallest and largest order amount.
🟢 Easy
13. Find customers whose name starts with 'A'.
🟢 Easy
14. Find customers whose email is missing.
🟢 Easy
15. Find orders between 1000 and 5000.
🟢 Easy
16. Rename the amount column to revenue in output.
🟢 Easy
17. Show orders placed in 2025.
🟢 Easy
18. Count customers per city.
🟢 Easy
19. Total revenue per region.
🟡 Medium
20. Regions with revenue above 1,00,000.
🟡 Medium
21. Average order value per customer, highest first.
🟡 Medium
22. Number of distinct customers who ordered.
🟡 Medium
23. Show each order with its customer name.
🟡 Medium
24. List all customers including those with no orders.
🟡 Medium
25. Find customers who never placed an order.
🟡 Medium
26. Revenue per customer name.
🟡 Medium
27. Show employees with their manager's name.
🟡 Medium
28. Label orders as High/Low above or below 10000.
🟡 Medium
29. Count High vs Low orders.
🟡 Medium
30. Find orders above the overall average amount.
🟡 Medium
31. Find customers who ordered in 2025.
🟡 Medium
32. Rewrite a subquery as a CTE for monthly revenue.
🟡 Medium
33. Combine active and archived customers into one list.
🟡 Medium
34. Same as above but keep duplicates.
🟡 Medium
35. Replace NULL discount with 0.
🟡 Medium
36. Avoid divide-by-zero when computing margin.
🟡 Medium
37. Extract the month from order_date.
🟡 Medium
38. Monthly revenue trend.
🟡 Medium
39. Clean whitespace and casing from city.
🟡 Medium
40. Get the first 3 characters of a pincode.
🟡 Medium
41. Concatenate first and last name.
🟡 Medium
42. Find duplicate emails in customers.
🟡 Medium
43. Count orders per status including zero-count statuses.
🟡 Medium
44. Percentage of total revenue by region.
🔴 Hard
45. Rank customers by revenue.
🔴 Hard
46. Find the 2nd highest salary.
🔴 Hard
47. Top 3 products by revenue in each region.
🔴 Hard
48. Month-over-month revenue growth %.
🔴 Hard
49. Days until each customer's next order.
🔴 Hard
50. Running total of revenue by date.
🔴 Hard
51. Keep only the latest order row per customer.
🔴 Hard
52. Delete duplicate rows keeping one.
🔴 Hard
53. Repeat customer rate.
🔴 Hard
54. Monthly new vs returning customers.
🔴 Hard
55. Customers who bought A but not B.
🔴 Hard
56. Median order value.
🔴 Hard
57. 3-month moving average of revenue.
🔴 Hard
58. Department-wise employees earning above their department average.
🔴 Hard
59. Pivot monthly revenue into columns.
🔴 Hard
60. Customer lifetime value with first/last order.
🟡 Medium
61. Find the top city by number of customers.
🟡 Medium
62. Orders placed on weekends.
🟡 Medium
63. Average days between order and delivery.
🟢 Easy
64. Show orders that are not cancelled.
🟢 Easy
65. Count orders by status.
🟡 Medium
66. Revenue excluding cancelled orders.
🟡 Medium
67. Share of orders that were returned.
🔴 Hard
68. Funnel: visited → added to cart → purchased.
🔴 Hard
69. Retention: customers active in month 1 and month 2.
🟡 Medium
70. Product categories with no sales this year.
🟢 Easy
71. Round revenue to 2 decimals.
🟢 Easy
72. Show the number of columns' distinct product names.
🟡 Medium
73. Customers with more than 5 orders.
🟡 Medium
74. Total revenue by region and category.
🔴 Hard
75. Add region subtotals and a grand total.
🟡 Medium
76. Show the earliest order per customer.
🔴 Hard
77. Average revenue per active day.
🟢 Easy
78. Find products whose name contains 'pro'.
🟡 Medium
79. List orders with amounts above their customer's average.
🟢 Easy
80. Return only 5 rows for a quick look.
🟡 Medium
81. Second page of results, 20 per page.
🟡 Medium
82. Convert text dates to real dates.
🟡 Medium
83. Flag orders in the last 30 days.
🔴 Hard
84. Rank regions by revenue within each quarter.
🔴 Hard
85. Find gaps in a daily sales series.
🟡 Medium
86. Total revenue per weekday name.
🟢 Easy
87. Count NULL emails.
🟡 Medium
88. Update wrong city spellings.
🟡 Medium
89. Create a summary table of monthly revenue.
🟢 Easy
90. Alias a table for shorter joins.
🟡 Medium
91. Find customers sharing the same phone number.
🔴 Hard
92. Compute AOV trend by cohort month.
🟡 Medium
93. Show top 5 customers and their share of revenue.
🟢 Easy
94. Filter rows where quantity equals 1.
🟡 Medium
95. Difference between WHERE and HAVING?
🟡 Medium
96. Difference between UNION and JOIN?
🟡 Medium
97. Difference between DELETE, TRUNCATE and DROP?
🟡 Medium
98. What is an index and when does it hurt?
🔴 Hard
99. Explain a slow query and how you'd fix it.
🟡 Medium
100. What is a primary key vs a foreign key?
🟢 Easy
101. Get today's date in SQL.
Dashboard
Roadmap
Practice
Projects
Interview