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
- [Dan] Hi, I'm Dan Sullivan. And in this course, I'll be describing how to use SQL for data science. We'll start by reviewing the basics of SQL data manipulation, and data definition commands. We'll cover how to use SQL queries to collect and prepare data for analysis, and introduce basic statistical functions, to help you better understand your data. We'll explore the rich set of options for constructing SQL queries. Including operations for filtering, joining, and aggregating data. We'll also delve into more advanced functions, for rollups and cubes, along with window functions that can greatly simplify complex operations, especially those involving time series.
So let's take a deep dive into SQL for data science.
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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