Join Dan Sullivan for an in-depth discussion in this video ANSI standard SQL and variants, part of Advanced SQL for Data Scientists.
- [Instructor] SQL is a language that has changed over time.…Databases offer different versions of SQL,…so when we talk about SQL in general,…we usually refer to the ANSI standards SQL.…The American National Standards Institute, known as ANSI,…defined a standard version of SQL in the mid-1980s.…Since then, the standard has been updated seven times…to add features, many of which are useful…to data scientists.…With changes over time in the standard…and database management systems…offering different versions of SQL,…what does this mean for data scientists?…It means the SQL implementation that you use…may be different from the current ANSI standard…and it may be different from the SQL used in this course.…
The foundational SQL commands like insert, delete,…and most create commands will be the same…across implementations,…but you should check your database documentation,…especially for missing data types and functions.…Some databases like MySQL…do not support windowing functions,…which are really quite useful…for some types of data science analysis.…
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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