PostgreSQL: Advanced Queries

Course details

In this course, database expert Adam Wilbert introduces you to a number of advanced query techniques that you can use to better understand your data. Statistical analyses involve understanding the shape and size of the full data set, and deeper insights can be gained by grouping data together to obtain some very useful metrics. Adam shows you how to create basic groups and apply aggregate calculations, then moves into window functions that create subgroups for more granular analysis. He goes over statistics that are based on sorted data within groups, such as the median value, the first and third quartiles of a dataset, the most frequent value, and more. Adam also covers ranking, hypothetical sets, percentile functions, and conditional expressions for further manipulating query result sets. He concludes with some additional querying techniques that you may find useful in solving common problems.

Concepts

0. Introduction

01 - Gain additional insights from your PostgreSQL data
02 - What you should know
03 - Using the exercise files

1. Obtain Summary Statistics by Grouping Rows

04 - Using GROUP BY to aggregate data rows
05 - Obtain general-purpose aggregate statistics
06 - Evaluate columns with Boolean aggregates
07 - Find the standard deviation and variance of a dataset
08 - Include overall aggregates with ROLLUP
09 - Return all possible combinations of groups with CUBE
10 - Segmenting groups with aggregate filters
11 - Challenge - Group statistics
12 - Solution - Group statistics

2. Use Window Functions to Perform Calculations across Row Sets

13 - Create a window function with an OVER clause
14 - Partition rows within a window
15 - Streamline partition queries with a WINDOW clause
16 - Ordering data within a partition
17 - Calculate a moving average with a sliding window
18 - Return values at specific locations within a window
19 - Challenge - Leverage window functions
20 - Solution - Leverage window functions

3. Statistics Based on Sorted Data within Groups

21 - Calculate the median value of a dataset
22 - Calculate the first and third quartiles of a dataset
23 - Find the most frequent value within a dataset with MODE
24 - Determine the range of values within a dataset
25 - Challenge - Retrieve statistics of a dataset with groups
26 - Solution - Retrieve statistics of a dataset with groups

4. Ranking Data with Windows and Hypothetical Sets

27 - Rank rows with a window function
28 - Find a hypothetical rank
29 - View top performers with percentile ranks
30 - Evaluate probability with cumulative distribution
31 - Challenge - Evaluate rankings within a dataset
32 - Solution - Evaluate rankings within a dataset

5. Define Output Values with Conditional Expressions

33 - Define values with CASE statements
34 - Merge columns with COALESCE
35 - Convert values to null with NULLIF

6. Additional Querying Techniques for Common Problems

36 - Output row numbers with query results
37 - Cast values to a different data type
38 - Move rows within a result with LEAD and LAG
39 - Use an IN function with a subquery
40 - Define WHERE criteria with a series
41 - Challenge - Calculations across rows
42 - Solution - Calculations across rows
43 - Pattern matching with regular expressions

Conclusion

44 - Next steps