Chapter 1: Introduction

• Basics of SQL querying in 15 minutes

• Introduction to vitamin BI, business intelligence for beginners

• SQL querying essential for becoming a business intelligence analyst

• Focus on querying data, not creating or deleting databases

• BI tools can write SQL queries for requesting data for charts, graphs, and tables

• Views are created to pre-aggregate and filter data for efficiency

• Knowing SQL querying is a useful skill

• Working with a SQLite database containing FIFA video game data

• Using Navicat as the tool to query the database

• Connected to the soccer DB connection with a database called main

• Working mainly with the player table

Chapter 2: Querying Data

• Using SQL queries to ask the database for specific data from the player table

Chapter 2: Called Player

• We use a select statement to ask for data from a table called player

  • Example: SELECT * FROM player

  • The database returns all data from the table

• Beautifying the SQL query

  • Capitalizing select and from makes queries easier to read

  • Not obligatory, but helpful for larger queries

• Selecting specific fields

  • Instead of *, write out the field names separated by commas

  • Example: SELECT player_name, birthday

• Creating aliases for selected fields

  • Use the AS function

  • Example: player_name AS name

  • Aliases can contain spaces if put in quotes

  • Example: "full name"

• Using a where clause to select specific rows

  • Use different operators

  • Example: weight = 190

  • Example: weight > 190

  • Example: weight >= 190

• Specifying multiple conditions with AND or OR

  • AND: Both conditions must be met

  • OR: Either condition must be met

  • Example: weight > 190 AND height > 190

  • Example: weight > 190 OR height > 190

• Selecting rows based on text values

  • Use the equals operator with single quotes

  • Example: player_name = 'Aaron Galindo'

Chapter 3: Find Any Player

• Using the like operator to select rows with specific player names

  • Using a percent character after the text to look for names starting with "Aaron"

  • Using a percent character before the text to look for names ending with "Aaron"

  • Using a percent character before and after the text to look for names containing "Aaron"

  • Using the percent character in the middle of the text to look for names starting with "a" and ending with "n"

• Using the underscore as a wildcard to represent a single character

  • Example: "t_mpercent" matches names starting with "t", followed by any character between "t" and "m", and ending with "rum"

• Using the "in" operator for exact matches in text fields

  • Example: "in (Cristiano Ronaldo, Lionel Messi)" matches rows with player names "Cristiano Ronaldo" or "Lionel Messi"

• Using the "between" operator for integer values

  • Example: "where weight between 180 and 190" matches rows with weights between 180 and 190

• Using the "is null" and "is not null" operators to check for empty values

  • Example: "where home player 1 is null" matches rows where the "home player 1" field is empty

• Sorting results using the "order by" clause

  • By default, rows are sorted by the ID field in ascending order

  • Example: "order by weight" sorts rows by weight in ascending order

  • Adding "DESC" after the field name sorts the rows in descending order

Chapter 4: Use The Player

• Sorting data using orderby is not important for our use case

• Views are usually connected to a BI tool for building individual chart queries

• Joining data from different tables is necessary to create a view with player name and overall rating

• Specify table name dot field name to indicate which field comes from which table

• Specify the joining of tables using one of the four join types

• Use inner join and specify the fields to create the join

• Use the as function to create aliases for tables and fields

• Aggregate data using the sum aggregator to add up values for each player

Chapter 5: Use The Group

• Grouping data together using the GROUP BY clause

  • Specify all fields to group, except overall rating

• Combining ratings for different dates

  • Remove "AND run" from the query

• Aliasing the overall rating as "rating"

  • Sort the result in descending order by rating

• Exploring why Ronaldo and Messi are not at the top

  • Adding a count of player name to see if there are more entries for some players

  • Replacing "SUM" with "AVG" to calculate the average rating

• Filtering the result using the HAVING clause

  • Applying the HAVING clause after the GROUP BY clause and before the ORDER BY clause

  • Filtering for ratings above 85

• Finalizing the query

  • Beautifying the query

Conclusion

• Capitalization of SQL keywords

• Covered a small part of SQL, more to learn

• Provided a good foundation for diving deeper into SQL

Chapter 6: Conclusion

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• As always, thanks for watching.

• I've been Adam Feiner.

• Until the next time, stay behind curious.