Master Product Data Analytics

Many SQL interview questions fall into common patterns. Here are a few you should be prepared for:

• Data Aggregation and Filtering: These questions test your ability to use `GROUP BY`, `HAVING`, `WHERE`, and aggregate functions (e.g., `SUM`, `AVG`, `COUNT`) to summarize data.

  Example: "Find the top 10 users with the highest total order value."

• JOINs: These questions test your ability to combine data from multiple tables using various types of JOINs (`INNER`, `LEFT`, `RIGHT`, `FULL OUTER`).

  Example: "Calculate the average order value for customers in each country."

• Subqueries and CTEs: These questions test your ability to write nested queries or use Common Table Expressions to break down complex problems into smaller parts.

  Example: "Find the users who have made more orders than the average number of orders per user."

• Window Functions: These questions test your ability to use window functions (e.g., `RANK`, `ROW_NUMBER`, `LAG`, `LEAD`) to perform calculations across a set of rows related to the current row.

  Example: "Calculate the 7-day rolling average of daily active users."

• Data Cleaning and Transformation: These questions test your ability to handle missing values, convert data types, and manipulate strings using SQL functions.

  Example: "Clean a messy dataset by handling NULL values, converting date strings to the correct format, and removing duplicates."

Practice Resources:

• StrataScratch: Offers a large number of real SQL interview questions from top companies, including Meta.
• LeetCode: Has a database section with many SQL problems of varying difficulty.
• HackerRank: Provides a platform to practice SQL and other programming languages.

Let's put your SQL skills to the test with some practice problems. (Remember, these are just examples. You'll need to practice a wide range of problems to be fully prepared.)

Example Problem 1:

Given a table of user activity with columns `user_id`, `event_date`, and `event_type`, write a SQL query to find the percentage of users who had at least one 'login' event type for each day.

Solution and Explanation:

                                
                                    WITH DailyLogins AS (
                                        SELECT event_date, user_id
                                        FROM user_activity
                                        WHERE event_type = 'login'
                                        GROUP BY event_date, user_id
                                    ),
                                    DailyUsers AS (
                                        SELECT event_date, COUNT(DISTINCT user_id) AS total_users
                                        FROM user_activity
                                        GROUP BY event_date
                                    )
                                    SELECT 
                                        dl.event_date, 
                                        (COUNT(DISTINCT dl.user_id) * 100.0 / du.total_users) AS login_percentage
                                    FROM DailyLogins dl
                                    JOIN DailyUsers du ON dl.event_date = du.event_date
                                    GROUP BY dl.event_date, du.total_users
                                    ORDER BY dl.event_date;
                                
                            

Explanation:

1. The `DailyLogins` CTE selects the `event_date` and `user_id` for events where `event_type` is 'login'. The `GROUP BY` clause ensures that we count each user only once per day, even if they logged in multiple times.
2. The `DailyUsers` CTE calculates the total number of distinct users for each day.
3. The final `SELECT` statement joins these two CTEs on `event_date`. It then calculates the percentage of users who logged in on each day by dividing the number of distinct users who logged in (from `DailyLogins`) by the total number of distinct users on that day (from `DailyUsers`). The result is multiplied by 100.0 to get a percentage.
4. The results are then grouped by `event_date` and `total_users` to get the percentage for each day and ordered by `event_date` to show the trend over time.

Example Problem 2:

Given a table `orders` with columns `order_id`, `customer_id`, `order_date`, and `amount`, write a SQL query to find the top 5 customers who have spent the most money in total.

Solution and Explanation:

                                
                                    SELECT customer_id, SUM(amount) AS total_spent
                                    FROM orders
                                    GROUP BY customer_id
                                    ORDER BY total_spent DESC
                                    LIMIT 5;
                                
                            

Explanation:

1. The `GROUP BY` clause groups the rows by `customer_id`, so we get one row for each customer.
2. The `SUM(amount)` function calculates the total amount spent for each customer.
3. The `ORDER BY total_spent DESC` sorts the results in descending order of the total amount spent.
4. The `LIMIT 5` clause restricts the output to the top 5 rows.

More Practice: I highly recommend working through as many practice problems as you can. The more you practice, the more comfortable you'll become with writing SQL queries.

In an interview setting, it's not just about getting the right answer. It's also about writing clean, efficient, and readable code. Here are some tips:

• Use meaningful aliases: Give your tables and columns meaningful aliases to make your queries easier to understand.
• Format your code consistently: Use consistent indentation and spacing to make your code more readable.
• Add comments: Explain your logic and the purpose of each part of your query.
• Optimize for performance: Think about the most efficient way to write your query. Use appropriate JOINs, filter data early, and avoid using `SELECT *` when you only need specific columns.

Being able to explain your thought process is just as important as writing the code itself. Here's how to do it effectively:

• Start with the problem: Briefly restate the problem you're trying to solve.
• Explain your approach: Describe the steps you're going to take to solve the problem.
• Walk through your code: Explain each part of your query and why you wrote it that way.
• Justify your choices: Explain why you chose a particular JOIN type, aggregate function, or filtering condition.
• Consider alternatives: If there are other ways to solve the problem, mention them and explain why you chose the approach you did.
• Be prepared to answer follow-up questions: The interviewer may ask you to modify your query, optimize it further, or handle edge cases.

Here are some common data manipulation tasks you might be asked to perform using Python and Pandas:

• Filtering data: Selecting rows based on certain conditions.
• Sorting data: Ordering rows based on one or more columns.
• Adding/removing columns: Creating new columns or dropping existing ones.
• Grouping and aggregating data: Similar to SQL's `GROUP BY`.
• Joining/merging DataFrames: Similar to SQL JOINs.
• Handling missing values: Imputing or removing missing data.
• Reshaping data: Pivoting, melting, or stacking data.

Let's work through a few examples to solidify your understanding of these concepts.

Example Problem 1:

Given a Pandas DataFrame `df` with columns 'user_id', 'name', and 'age', filter the DataFrame to include only users who are older than 25.

Solution and Explanation:

                                
                        import pandas as pd
                        
                        # Sample DataFrame
                        data = {'user_id': [1, 2, 3, 4, 5],
                                'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
                                'age': [24, 30, 22, 35, 27]}
                        df = pd.DataFrame(data)
                        
                        # Filtering the DataFrame
                        filtered_df = df[df['age'] > 25]
                        
                        print(filtered_df)
                                
                            

Explanation: We use boolean indexing to select rows where the 'age' column is greater than 25. The expression `df['age'] > 25` creates a boolean Series, which is then used to filter the DataFrame.

Example Problem 2:

Given a Pandas DataFrame `df` with columns 'product_id', 'category', and 'sales', find the top 3 product categories with the highest total sales.

Solution and Explanation:

                                
                        import pandas as pd
                        
                        # Sample DataFrame
                        data = {'product_id': [1, 2, 3, 4, 5, 6],
                                'category': ['A', 'B', 'A', 'C', 'B', 'A'],
                                'sales': [100, 150, 200, 50, 120, 180]}
                        df = pd.DataFrame(data)
                        
                        # Grouping by category and calculating total sales
                        category_sales = df.groupby('category')['sales'].sum()
                        
                        # Sorting in descending order and selecting the top 3
                        top_3_categories = category_sales.sort_values(ascending=False).head(3)
                        
                        print(top_3_categories)
                                
                            

Explanation:

1. We group the DataFrame by the 'category' column using `groupby()`.
2. We calculate the sum of 'sales' for each category using `sum()`.
3. We sort the resulting Series in descending order using `sort_values(ascending=False)`.
4. We select the top 3 categories using `head(3)`.

Similar to SQL, writing clean and efficient Python code is important in an interview setting:

• Use meaningful variable names: Make your code self-explanatory.
• Comment your code: Explain your logic and the purpose of each step.
• Use built-in functions and libraries: Leverage the power of Pandas and NumPy for common data manipulation tasks.
• Avoid unnecessary loops: Pandas and NumPy are optimized for vectorized operations, which are often much faster than explicit loops.
• Break down complex tasks: Use functions and helper variables to make your code more modular and easier to understand.

• LeetCode: Offers a wide range of coding problems, including SQL and Python.
• HackerRank: Provides coding challenges and mock interviews.
• StrataScratch: Focuses specifically on data science interview questions, including SQL and Python.
• Pramp: A peer-to-peer mock interview platform where you can practice with other candidates.
• Interviewing.io: Offers anonymous technical mock interviews with experienced engineers.