Join Dan Sullivan for an in-depth discussion in this video SQL data manipulation features, part of Advanced SQL for Data Scientists.
- [Instructor] SQL has two types of commands.…Data manipulation commands and data definition commands.…We'll look at data manipulation commands in this video…and discuss data definition commands in the next.…Before discussing specific commands, let's understand…why data scientist need to know these commands…to do their work.…A majority of data science work is about collecting,…cleaning, and restructuring data.…Only after that preparation can we move ahead…with analysis work.…A common assumption is that about 70% to 80% of time…on a data science project is spent on data manipulation,…much of that time is spent working with SQL.…
INSERT is a SQL command for putting data into a table.…INSERT statements include the name of the table,…the name of the columns with values being inserted,…and a list of values to insert.…A simple INSERT command puts one row into a table.…This set of three INSERT commands…adds three rows to the table.…The UPDATE command is used to change data…that is already in a table.…The UPDATE command includes the name of the table to update,…
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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