Join Dan Sullivan for an in-depth discussion in this video SQL data definition features, part of Advanced SQL for Data Scientists.
- [Instructor] SQL has two types of commands:…data manipulation commands, and data definition commands.…We're going to discuss…data definition commands in this video.…Data definition commands are used to define structures…for organizing data in a relational database.…Imagine you need to manage…thousands of paper reports for your job.…Now, you could just leave this in a pile…and search through the pile…every time you need to find a particular report.…However, it's much easier to find a paper report…when they're organized in boxes by type of report…and then organized within a box by date.…
The same idea applies…to organizing data in a relational database.…We structure data using tables,…which are collections of related data records,…indexes, which are sets of data…about the location of records,…views, which are used when we repeatedly want to access…the same set of data from one or more tables,…and then finally schemas, which are collections…of tables, indexes, views, and other data structures.…Tables are used to organize related sets of data,…
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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