Join Dan Sullivan for an in-depth discussion in this video Extracting strings from character data, part of Advanced SQL for Data Scientists.
- [Instructor] In addition to matching and reformatting…strings, we sometimes need to take them apart…and extract pieces of stings.…SQL provides some general purpose functions for extracting…and overriding strings.…Let's start with a simple string that's easy to experiment.…We'll use the first twelve letters of the alphabet,…and I can do that by simply saying,…SELECT, and then typing out the first 12 letters,…and we'll call this test_string,…and I can execute that, and we'll see I receive back…a simple 12 character string.…Now, one of the things we can do with SQL is replace parts…of a string, and we can use, for example,…the substring function.…
The substring function takes a string and a range,…and returns the characters from the string…that are in that range.…So, I'll specify that I want to use the substring function,…and I want to apply this to this 12 character string,…and I want to specify that I want to start extracting from…position one for a total of three characters,…and if I execute this, I will see that I get three…
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
SQL: Data Reporting and Analysiswith Emma Saunders2h 16m Intermediate
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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