- [Narrator] Data scientists often use statistics…to better understand data sets.…SQL includes functions for describing data sets,…so let's see how to use some of the most commonly-used…statistic functions, to better understand our data.…I'm going to start by selecting from the staff column,…I will select star, from staff,…and I'll limit this to 10,…cause I'm just trying to get a feel for the data.…And we'll notice that this table includes…information about employees,…which includes some string characters and some dates,…and some integer values to work with.…
Now, one of the things I might like to know is,…how much does the company pay in salary…to all of its staff, across a given year?…Well, the way to do that is to first of all,…we'll remove the limit clause,…cause we want to look at everyone,…and we want to use the sum function,…which is one of those statistic functions,…and it's applied to a column, like salary,…and I'll execute the query, and notice…that the result is approximately…$97.3 million is paid per year,…
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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