Join Dan Sullivan for an in-depth discussion in this video Tips for using SQL for data science, part of Advanced SQL for Data Scientists.
- [Instructor] The goal of data science is…to tell stories using data.…The stories we tell start with a business problem,…like customers switching to competitors…or products not selling as well as they use to.…We need data to tell these stories.…This data is often stored in multiple databases…and file systems so our first task is to pull data…from these different data sources…and prepare them like a cook preparing ingredients…for a dish.…As with cooking, if you spend the time getting your data…organized and properly prepared,…the actual analysis is more enjoyable…and goes more smoothly.…
Here are some tips as you work with SQL for data science.…First, data collection and preparation will likely…take more time than any other part…of the data science project.…Be sure to count all data sources when estimating…and add extra time if you'll be joining data…across different data sources.…You may have inconsistent coding schemes…or other data quality problems…that will require additional time to correct.…Aggregate and statistical functions in SQL…
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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