- [Narrator] Regular expressions are patterns for describing…how to match strings in a WHERE clause.…Many programming languages support regular expressions…that use slightly different syntax from what is used…with the SQL LIKE operator.…In this course, when we refer to regular expressions,…we're referring to the patterns…used with the SQL LIKE operator.…Let's start by creating a list of job titles…that have the word assistant in them.…So to do that, we'll SELECT job_title from the staff table,…WHERE job_title LIKE, and I want to search for the pattern…where the word Assistant appears anywhere in the job title.…
So I'll put % wildcards before and after the word assistant.…Then when we execute, you'll notice we have…some titles where the word assistant is at the end,…and some where the word assistant is in the beginning,…and some where it is in the middle.…Now, we'll notice there are different levels of assistant.…Let's select just assistants at levels three or four.…To do that, we're going to change the LIKE operator…
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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