- Another useful function is the Rank function.…It works with the partition operation…to order results and assign a Rank value…based on the way the partition data is sorted.…Let's look at an example.…Let's select department, last name, salary,…and now let's introduce the Rank function.…And it works with the over partition by operator.…And we're going to say we're going to partition by,…recruit by department.…And we're going to order that by salary.…
And I'd like to use descending again.…So I'll add that.…And we'll select this from staff.…Now let's execute and see what kind of values we get.…Now what we'll notice here is that…we have a familiar set of data.…We have a listing by department,…with the employees last name and the employees salary.…The salaries are sorted in descending order.…So the highest is first.…And then we also have this column called Rank.…And that lists the number one through the…number of employees in the department.…So Rank tells us where each salary falls in the department.…
And we'll notice as we move into the next department,…
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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