Author
Dan Sullivan
Released
5/22/2017The 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
Duration
Views
- [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.
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Introduction
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Welcome42s
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Exercise files50s
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1. SQL as a Tool for Data Science
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SQL data definition features5m 32s
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2. Basic Statistics with SQL
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Installing PostgreSQL2m 37s
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CREATE TABLE and INSERT DATA2m 28s
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Statistical functions4m 21s
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3. Data Munging with SQL
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Reformatting character data4m 41s
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Reformatting numeric data3m 25s
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4. Filtering, Joins, and Aggregation
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Subqueries in SELECT clauses2m 57s
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Subqueries in FROM clauses1m 59s
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Subqueries in WHERE clauses1m 38s
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Joining tables3m 42s
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Creating a view2m 23s
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5. Window Functions and Ordered Data
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Window functions: RANK1m 25s
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LAG and LEAD3m 3s
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NTILE functions2m 20s
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6. Preparing Data for Analytics Tools
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Video: Welcome