- [Narrator] When working with SQL,…we will sometimes need to retrieve…data from multiple tables.…For example, the Staff table…includes a department for each employee.…Departments are organized into divisions.…Since we don't keep division information…in the staff table,…we have to look it up somewhere else.…In our example data set,…we have a table called Company Divisions.…Let's take a look at that.…We'll do that by typing Select + *,…to get all the columns…From, company divisions…and that's plural.…And when we execute it,…we'll notice that first of all,…21 rows are returned…and the table has two columns.…
One listed departments that we are familiar with,…we've seen those before.…And the other column lists…a higher level grouping or division.…Which include things like Auto and Hardware,…Domestic and Electronic Equipment.…Now, let's join the Staff and Company Divisions tables.…So, let's replace the Select + *…with a Select.…Oh, let's select from the Staff table…a last name…and a department…and let's from the Company Divisions table…
The course begins with a brief overview of SQL. Then the five major topics a data scientist should understand when working with relational databases: basic statistics in SQL, data preparation in SQL, advanced filtering and data aggregation, window functions, and preparing data for use with analytics tools.
- Data manipulation
- ANSI standards
- SQL and variations
- Statistical functions in SQL
- String, numeric, and regular expression functions in SQL
- Advanced filtering techniques
- Advanced aggregation techniques
- Windowing functions for working with ordered data sets
Skill Level Advanced
1. SQL as a Tool for Data Science
SQL data definition features5m 32s
2. Basic Statistics with SQL
3. Data Munging with SQL
4. Filtering, Joins, and Aggregation
5. Window Functions and Ordered Data
6. Preparing Data for Analytics Tools
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