SQL calculations and expressions are fundamental tools for data analysts to transform and analyze data within databases. These powerful features allow you to perform mathematical operations, manipulate text, and create derived values from existing data columns.
Arithmetic operators form the founda…SQL calculations and expressions are fundamental tools for data analysts to transform and analyze data within databases. These powerful features allow you to perform mathematical operations, manipulate text, and create derived values from existing data columns.
Arithmetic operators form the foundation of SQL calculations. You can use addition (+), subtraction (-), multiplication (*), and division (/) to compute new values. For example, calculating total revenue by multiplying quantity by price: SELECT quantity * price AS total_revenue FROM sales.
Aggregate functions summarize data across multiple rows. Common functions include SUM() for totals, AVG() for averages, COUNT() for counting records, MIN() and MAX() for finding extreme values. These are essential for creating summary statistics: SELECT AVG(salary) AS average_salary FROM employees.
String expressions allow text manipulation. Functions like CONCAT() join text strings together, LENGTH() measures character count, and UPPER() or LOWER() change text case. These help standardize and format data for analysis.
Conditional expressions add logic to calculations. The CASE statement evaluates conditions and returns specific values based on results. This enables categorization and custom groupings: CASE WHEN score >= 90 THEN 'Excellent' ELSE 'Good' END.
Date calculations help analyze time-based data. Functions vary by database system but typically include extracting parts of dates, calculating differences between dates, and formatting date outputs for reports.
Aliases, created using the AS keyword, give calculated columns meaningful names. This improves readability and makes results easier to interpret in final outputs.
Nested calculations combine multiple operations within a single query. You can layer functions and operators to create complex derived metrics that answer specific business questions.
Mastering these SQL calculations enables analysts to uncover insights, create metrics, and transform raw data into actionable information. Practice with real datasets helps build proficiency in choosing appropriate expressions for different analytical scenarios.
SQL Calculations and Expressions: A Complete Guide
Why SQL Calculations and Expressions Are Important
SQL calculations and expressions are fundamental skills for any data analyst. They allow you to transform raw data into meaningful insights by performing mathematical operations, manipulating text, and creating derived values. In the Google Data Analytics context, these skills enable you to answer business questions, identify trends, and generate reports that drive decision-making.
What Are SQL Calculations and Expressions?
SQL calculations and expressions are formulas and operations you write within SQL queries to compute new values from existing data. They include:
Conditional Expressions: CASE WHEN statements for creating custom categories
Date Functions: DATE_DIFF(), EXTRACT(), DATE_ADD()
How SQL Calculations Work
SQL calculations are performed in the SELECT clause or WHERE clause of your query. Here are key examples:
Basic Arithmetic: SELECT price * quantity AS total_revenue FROM sales;
Aggregate Calculations: SELECT AVG(salary) AS average_salary FROM employees;
Conditional Logic: SELECT product_name, CASE WHEN price > 100 THEN 'Premium' ELSE 'Standard' END AS category FROM products;
String Manipulation: SELECT CONCAT(first_name, ' ', last_name) AS full_name FROM customers;
Common Use Cases
• Calculating profit margins: (revenue - cost) / revenue * 100 • Finding percentage changes between periods • Creating age groups or price tiers using CASE statements • Combining text fields for cleaner reporting • Computing running totals and averages
Exam Tips: Answering Questions on SQL Calculations and Expressions
1. Read the Question Carefully: Identify what calculation is being asked for—is it a sum, average, percentage, or conditional categorization?
2. Know Your Operators: Remember the order of operations (PEMDAS) applies in SQL. Use parentheses to control calculation order.
3. Understand Aliases: The AS keyword creates readable column names. Expect questions about proper alias usage.
4. Watch for NULL Values: Aggregate functions handle NULLs differently. COUNT(*) counts all rows, while COUNT(column) excludes NULLs.
5. CASE Statement Structure: Memorize the syntax: CASE WHEN condition THEN result ELSE default END
6. Division Considerations: Be aware that dividing integers may truncate decimals in some SQL dialects. Questions may test this knowledge.
7. Function Placement: Know that aggregate functions cannot be used in WHERE clauses—use HAVING instead for filtering aggregated results.
8. Practice Common Scenarios: Year-over-year comparisons, percentage of total calculations, and creating categorical variables from continuous data are frequent exam topics.
9. Check Data Types: Ensure you understand when type conversion is necessary, such as converting strings to numbers for calculations.
10. Eliminate Wrong Answers: Look for syntax errors in answer options, such as missing commas, incorrect function names, or improper clause ordering.