- [Instructor] If you have access to the Exercise Files for this course, you can follow along. The Exercise Files are organized into folders, one for each chapter that has an Exercise File. Each chapter folder is further organized into videos. Each video that has an Exercise File has a folder. Within that folder, you'll see one or more SQL files. These files contain SQL commands we will use throughout the course. I will type most of the commands through this course, but we will execute one file to create our database schema and load data.
I'd like to point out that the dataset you'll be working with is going to be set up in Chapter Two. Now because of that, the subsequent Exercise Files aren't start states, rather they're files with faux queries that we'll be running. Also, there are some actions in the middle of the course that you'll have to follow along with so that the later queries work.
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
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.Cancel
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.