A few years ago my co-worker Will, was on the brink of disaster.
The top-tier financial services firm we worked at had a spot open that was perfect for him, and he knew it. But there was a problem – his perfectionism was in the way. He’d spend hours refining his answers to the most basic interview questions, obsessing over the details until he felt paralyzed.
His friends saw it:
- Overthinking the process
- Doubting his skills and abilities
- Nervous about every single answer
It was excruciating to watch. More than that, it was costing Will his dream job.
The self-doubt began to multiply.
Not only was he struggling with the interview prep, but his current job performance started to suffer. He was frozen, stuck in a loop of self-doubt, perfectionism, and frustration.
Interview day was approaching fast. The pressure was mounting. Friends and colleagues were rooting for him, but he felt like he was losing himself.
But then, something incredible happened.
Will got into a routine of practicing beginner, intermediate, and advanced SQL problems. Little by little, he started practicing, each problem boosting his confidence and breaking down the perfectionism wall he’d built around himself.
Was it possible? Could these SQL problems be the answer to his problems?
Will continued to practice, and the transformation was astounding. His confidence grew. His answers were crisp, clear, and concise. The obsession with perfection was replaced with a focus on practical problem-solving.
No more late nights overthinking.
No more anxiety about his abilities.
A 20% pay increase and his dream job within reach.
Will found success through the simple, structured approach of practicing SQL problems. He turned his struggle into strength.
You too can follow in Will’s footsteps. No need to be held back by perfectionism or doubt. This guide includes SQL problems that can boost your confidence and skills, whether you’re interviewing for your dream job or looking to grow in your current role.
How this guide works:
- Use the database diagram below to write the SQL that answers each question
- The questions get more difficult and are based on real-world business problems.
- If you get stuck, all of the SQL code you need can be found here.
- I’ve also listed all of the answers below. But try it on your own first!
Use this database diagram to answer the questions.
Questions
Question 1: Sales by Category (Beginner)
Can you write a SQL query to find the total sales for the “Epic Novels” category during the last quarter?
This one is straightforward – just sum the sales and limit your results to the “Epic Novels” product category.
This question tests the understanding of basic SQL queries and aggregation functions. In a real-world marketing context, understanding product sales trends is fundamental for planning future marketing strategies.
Try it on your own and then check your answers against the online SQL code found here.
Question 2: Customer Segment Analysis (Intermediate)
Can you write a SQL query to identify the top 3 customer segments that contributed the most revenue in the past year, along with their growth compared to the previous year?
This question delves into more complex SQL features like JOINs, subqueries, and ranking functions. In marketing, recognizing and analyzing customer segments is crucial for targeted advertising and promotions.
Try it on your own and then check your answers against the online SQL code found here.
Question 3: Cohort Analysis (Advanced)
Can you write a SQL query to create a cohort analysis that groups customers by the month they first purchased, and calculates their retention rate over a 12-month period?
Cohort analysis is an advanced marketing analytical technique.
This question requires an understanding of window functions, CTEs, and other advanced SQL features in PostgreSQL to track customer behavior over time, a key concept in customer lifecycle management.
Try it on your own and then check your answers against the online SQL code found here.
Answers
Answer 1: Sales by Category (Beginner)
Here’s my SQL. View the online version here.
-- Question 1: Beginner Level
-- Query to find total sales for a specific category during the last quarter
SELECT SUM(s.quantity_sold * s.sale_price) AS total_sales
FROM sales s
JOIN product_categories p ON s.product_id = p.product_id
WHERE p.category_name = 'Epic Novels'
AND s.sale_date >= DATE_TRUNC('quarter', NOW()) - INTERVAL '3 months';
This code calculates the total sales for a specific product category in the last quarter by joining the sales and product_categories tables and filtering by date and category.
The JOIN operation connects sales and product categories based on product_id.
The DATE_TRUNC and INTERVAL functions are used to target the last quarter’s sales.
Answer 1 Results
| total_sales |
|---|
| 266.50 |
Answer 2: Customer Segment Analysis (Intermediate)
Here’s my SQL. View the online version here.
-- Question 2: Intermediate Level -- Query to identify the top 3 customer segments by revenue growth SELECT segment_name, revenue, growth_rate FROM customer_segments ORDER BY growth_rate DESC LIMIT 3;
This query selects the top 3 customer segments based on revenue growth, by ordering the customer_segments table by growth_rate in descending order and limiting the results to 3.
The ORDER BY clause arranges the segments by the growth rate in descending order.
The LIMIT clause restricts the output to the top 3 segments.
Answer 2 Results
| segment_name | revenue | growth_rate |
|---|---|---|
| Jordan Jumpers | 100000.00 | 25.00 |
| Hamm Hitters | 110000.00 | 22.00 |
| Curry Cookers | 120000.00 | 20.00 |
Answer 3: Cohort Analysis (Advanced)
Here’s my SQL. View the online version here.
-- Question 3: Advanced Level
-- Query for cohort analysis, grouping customers by first purchase month
WITH cohorts AS (
SELECT DATE_TRUNC('month', first_purchase_date) AS cohort_month, customer_id
FROM customers
)
SELECT cohort_month, COUNT(customer_id) AS cohort_size
FROM cohorts
GROUP BY cohort_month;
This query performs a cohort analysis, grouping customers by their first purchase month. It uses a common table expression (CTE) named “cohorts” to truncate the purchase dates to the month level, and then groups and counts the customers by cohort month.
The WITH clause is used to create a CTE for more readable and organized code.
The DATE_TRUNC function is employed to truncate the purchase dates to a monthly granularity.
Answer 3 Results
| cohort_month | cohort_size |
|---|---|
| 2023-01-01 | 5 |
| 2023-02-01 | 6 |
| 2023-03-01 | 9 |
Perfectionism almost cost Will his dream job.
Fortunately, he found that SQL problems got him on the right track.
Remember, you don’t have to be held back by doubt or fear. The right resources and a structured approach can unlock your potential and lead you to success. What’s holding you back, and how will you overcome it today?
Takeaways:
- The best way to overcome perfectionism is through structured SQL practice.
- A clear pathway with SQL can transform doubt into personal and professional growth.
- How Will secured a 20% pay increase and a position at a top-tier firm.
- Practicing SQL problems leads to a confidence boost and job success.
So, what SQL problem are you going to tackle next?