- [Narrator] Sometimes we want to group rows into…some number of buckets or ordered groups.…We can use the ntiles function to assign…buckets to rows.…This allows us to easily calculate statistics…like core tiles over sets of rows.…Here's an example that labels each salary with a…core tile of one to four with one being…the group with the largest salaries,…and four being the smallest set of salaries.…So let's select department, last name, salary.…
And now let's introduce the ntile function.…Ntile takes a number, and it will be the number…of buckets that we want or ordered groups that we want.…So we're going to use core tiles or four groups…so I'll specify four and I would like these ntiles…over a group that is partitioned by department.…And I'd like this ordered by salary,…and we'd like that in descending order.…Then we'll query this from the staff table.…Now when we execute, what we'll notice is,…we have a list of departments, each employee…in that department and then there's salary…and this employees are ordered with the highest salary…
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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