What burns more calories?

You're interesting in uncovering a bit of high-level information around workouts. Values like the total and average number of calories burned could provide insight to gym members who are looking to get the most out of their workouts. Luckily, you have the tools to do so!

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Data Types and Functions in Snowflake

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Instructions

Find the total sum of calories_burned for each workout_type.
Define a new column that stores the average number of calories_burned for each workout_type.
Create a column that stores the average number of calories_burned, rounded to two decimal places.
Aggregate the records by workout_type to ensure an error is not thrown.

Exercice interactif pratique

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SELECT
    workout_type,
    
    -- Find the total number of calories burned
    ___ AS total_calories_burned,
    
    -- Store the average calories burned
    ___ AS avg_calories_burned,
    
    -- Round the average calories burned to 2 decimal points
    ___(___, ___) AS rounded_avg_calories_burned
    
FROM CORE_GYM.visits

-- Aggregate records by workout_type
___;

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Cet exercice fait partie du cours

Data Types and Functions in Snowflake

IntermédiaireNiveau de compétence

4.9+

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Before you can change the world using Snowflake, you need to know the type of data you'll be working with. You'll start with text data, learning to use it in queries and define columns, plus discover how to retrieve column details from any table. Then you'll master numeric data types and date/time data, understanding precision, scale, and how to convert strings to timestamps.

Exercise 1: Text data types Exercise 2: Is that text data?Exercise 3: Working out in the big city Exercise 4: Defining text data Exercise 5: Numeric data types Exercise 6: Go with the FLOAT Exercise 7: Creating a retail table Exercise 8: Datetime data types Exercise 9: Building datetime values Exercise 10: Which timezone?Exercise 11: When did you check in?

Now that you know your Snowflake data inside-and-out, it's time to manipulate and transform it. You'll begin with text data manipulation using TRIM, SPLIT, and CONCAT, then master numeric operations like arithmetic and summary measures with SUM and AVG. You'll explore timestamp transformations by extracting components like year or hours, calculating intervals between dates, and create your own custom Snowflake functions (UDFs) to implement personalized logic.

Exercise 1: Manipulating text data Exercise 2: Managing display length Exercise 3: Transforming text data Exercise 4: Motivating member marketing Exercise 5: Numeric calculations Exercise 6: Comparison operators Exercise 7: Adjusting membership prices Exercise 8: What burns more calories?

Exercice en cours

Exercise 9: Manipulating datetime data Exercise 10: Breaking down a timestamp Exercise 11: How long were you at the gym?Exercise 12: Making appointments Exercise 13: User-defined functions in Snowflake Exercise 14: Creating your own Snowflake function Exercise 15: Ranking gym members by efficiency

In this final chapter, you'll crank the heat up with Snowflake's more advanced functionality. You'll start with VARIANT data types (semi-structured data), learning to parse top-level and nested data using dot-notation and bracket-notation, then master common table expressions (CTEs) by building and using multiple CTEs. Finally, you'll wrap up by creating pivoted tables using the PIVOT keyword to transform data into an easy-to-digest format, ready for reports and analysis.

Exercise 1: Flattening Semi-Structured Data Exercise 2: Retrieving nested data Exercise 3: Working with personal info Exercise 4: Building a gym member description Exercise 5: Multiple common table expressions Exercise 6: Identifying high-performers Exercise 7: Comparing senior workouts Exercise 8: Pivoting Data Exercise 9: Evaluating workout length Exercise 10: Is CrossFit a popular workout?Exercise 11: Which workouts burn the most calories?Exercise 12: Congratulations!