Join Dan Sullivan for an in-depth discussion in this video Filtering and grouping data, part of Advanced SQL for Data Scientists.
- [Instructor] Two common tasks in data science work…are filtering data so we can work with just the data…we're interested in, and the other is grouping data…so we can calculate values for an entire group…such as a product category or a department.…Let's start with filtering data using numeric values.…We'll use the staff table for this example.…Let's assume we want to list everything with a salary…greater than $100,000.…So I'll use the select command, and for this example,…we'll include the last name, department,…and of course, salary.…And we will select this from the staff table.…Now, we only want to include…a certain subset of the employees, so we use a WHERE clause.…
We're going to say WHERE salary is greater than 100,000,…and if we execute that statement,…we'll get a list of employees who work in various…departments, but all of whom earn salaries over $100,000.…Filtering based on character strings works in similar ways.…Now, let's generate a list of employees…who work in the tools department,…and we'll keep the columns the same.…
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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