Intermediate SQL for Data Scientists

Intermediate SQL for Data Scientists

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 2h 38m | 434 MB

There is an increasing need for data scientists and analysts to understand relational data stores. Organizations have long used SQL databases to store transactional data as well as business intelligence related data. This course was designed for data scientists who need to work with SQL databases. Specifically, it was designed to help these professionals learn how to perform common data science tasks, including exploration and extraction of data within relational databases.

Instructor Dan Sullivan kicks off the course with a brief overview of SQL data manipulation and data definition commands. He then focuses on how to use SQL queries to prepare data for analysis; leverage statistical functions to better understand that data; and work with aggregates, window operations, and more.

Table of Contents

Introduction
1 The need for SQL in data science
2 What you should know

1. Foundations of SQL for Data Science
3 Overview of data science operations
4 Data manipulation commands
5 Data definition commands
6 SQL standards
7 Installing PostgreSQL

2. Basic Statistics with SQL
8 Loading data
9 Basic aggregate functions
10 Statistical aggregate functions
11 Grouping and filtering data
12 Joining and filtering data
13 Challenge Test an attribute for normal distribution
14 Solution Test an attribute for normal distribution

3. Data Munging with SQL
15 Reformat character data
16 Extract strings from character data
17 Filter with regular expressions
18 Reformat numeric data
19 Use SOUNDEX with misspelled text
20 Challenge Prepare a data set for analysis
21 Solution Prepare a data set for analysis

4. Filtering and Aggregation
22 Use the HAVING clause to find subgroups
23 Subqueries for column values
24 Subqueries in FROM clauses
25 Subqueries in WHERE clauses
26 Use ROLLUP to create subtotals
27 Use CUBE to total across dimensions
28 Use Top-N queries to find top results
29 Challenge Filter and aggregate a data set
30 Solution Filter and aggregate a data set

5. Window Functions and Ordered Data
31 Introduction to window functions
32 NTH VALUE and NTILE
33 RANK, LEAD, and LAG
34 WIDTH BUCKET and CUME DIST
35 Challenge Segment a data set using Window functions
36 Solution Segment a data set using Window functions

6. Common Table Expressions
37 Introduction to common table expressions (CTEs)
38 Multiple table common table expressions
39 Hierarchical tables
40 Recursive common table expressions
41 Challenge Rewrite a complex query to use CTEs
42 Solution Rewrite a complex query to use CTEs

Conclusion
43 Next steps

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