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SQL Basics
course content

Course Content

SQL Basics

SQL Basics

1. Selecting
The very first meet with the databaseSELECTing certain columnsLIMITing resultsUnique values from columnUnique values from multiple columnsHow many?How many unique?AliasesTables aliases
2. Filtering
Word filteringNumber filteringCombining conditionsFiltering by numeric intervalMultiple filtering on one columnLIKE filteringNOT like this!
3. Aggregating
MINimal and MAXimal valuesSUMming valuesMean valuesROUNDing
4. Sorting
Sorting by one columnSorting in DESCending orderThe cheapest carSorting by multiple columns
5. Grouping
What is grouping?More aggregate statistics!Grouping by multiple columnsThe cheapest combinationHAVING some conditionsLeast consuming carsFiltering group size
6. Practicing
Challenge: Unique combinationsChallenge: Average Q-series cars priceChallenge: The cheapest new A-series carsChallenge: Relation between engine size and fuel consumptionChallenge: Mileage and manufacture year - is there relation?Challenge: Average price for each model and yearChallenge: The most expensive transmission + engine size combinations

bookFiltering group size

Sometimes in real life, you can meet the situation where you have significant differences between two characteristics. For example, the average rate for one item is 5, and for another is 4.6. Without any further analysis looks like that the first item is better, but what if the first item received 2 rates and the second more than a thousand? We need to be ready to this effect.

For example, let's calculate the average price for each model having at least 10 cars in each group.


              
1234

            
SELECT model, AVG(price)
FROM audi_cars
GROUP BY model
HAVING COUNT(*) > 10
copy

To calculate the number of observations in each group you can use the known COUNT(*) statement.

Task

From audi_cars calculate the average price for each engine size (enginesize column). Take into account only groups containing at least 7 observations and sort the resulting table by average price in ascending order.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 5. Chapter 7
toggle bottom row

bookFiltering group size

Sometimes in real life, you can meet the situation where you have significant differences between two characteristics. For example, the average rate for one item is 5, and for another is 4.6. Without any further analysis looks like that the first item is better, but what if the first item received 2 rates and the second more than a thousand? We need to be ready to this effect.

For example, let's calculate the average price for each model having at least 10 cars in each group.


              
1234

            
SELECT model, AVG(price)
FROM audi_cars
GROUP BY model
HAVING COUNT(*) > 10
copy

To calculate the number of observations in each group you can use the known COUNT(*) statement.

Task

From audi_cars calculate the average price for each engine size (enginesize column). Take into account only groups containing at least 7 observations and sort the resulting table by average price in ascending order.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 5. Chapter 7
toggle bottom row

bookFiltering group size

Sometimes in real life, you can meet the situation where you have significant differences between two characteristics. For example, the average rate for one item is 5, and for another is 4.6. Without any further analysis looks like that the first item is better, but what if the first item received 2 rates and the second more than a thousand? We need to be ready to this effect.

For example, let's calculate the average price for each model having at least 10 cars in each group.


              
1234

            
SELECT model, AVG(price)
FROM audi_cars
GROUP BY model
HAVING COUNT(*) > 10
copy

To calculate the number of observations in each group you can use the known COUNT(*) statement.

Task

From audi_cars calculate the average price for each engine size (enginesize column). Take into account only groups containing at least 7 observations and sort the resulting table by average price in ascending order.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Sometimes in real life, you can meet the situation where you have significant differences between two characteristics. For example, the average rate for one item is 5, and for another is 4.6. Without any further analysis looks like that the first item is better, but what if the first item received 2 rates and the second more than a thousand? We need to be ready to this effect.

For example, let's calculate the average price for each model having at least 10 cars in each group.


              
1234

            
SELECT model, AVG(price)
FROM audi_cars
GROUP BY model
HAVING COUNT(*) > 10
copy

To calculate the number of observations in each group you can use the known COUNT(*) statement.

Task

From audi_cars calculate the average price for each engine size (enginesize column). Take into account only groups containing at least 7 observations and sort the resulting table by average price in ascending order.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 5. Chapter 7
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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