1. I Learnded SQL And So Can You!

2. Background
Started with iovation as a Client Manager in After seven months, moved to a Data Analyst role. Before iovation, I had experience with data analysis, but no experience retrieving data.
Starting my Data Analyst role at iovation, I had…
No prior knowledge of SQL
No computer science background
Used Excel spreadsheets for data analysis
Never heard of Postgres
My first task: Get familiar with iovation’s Postgres QA databases

3. Me after one day…

4. Me after one day…

5. Learning SQL Takes Focus
”You must feel the force around you. Here… between you and me... The tree... The rock... The SELECT... The FROM... JOINING ON... everything.” - Yoda, The Empire Stikes Back (1980)
Luckily, the SQL syntax is pretty easy to understand 😄😄😄

6. How familiar are we with SQL?
I can build and maintain large scale optimized postgres databases
I can write fancy SQL queries that run efficiently
I regularly use relation databases and write SQL
I’ve used relational databases like Postgres and know enough SQL to get by
I’ve written a few SQL queries
I’ve heard of SQL

7. Basic Layout of a SQL Query
SELECT – The columns you want to see in the result set FROM – The table you are pulling data from JOIN – Other tables you are pulling data from, specifying how they are related to each other WHERE – Criteria to filter your result set GROUP BY – Summarize/compute results into groups ORDER BY – Sort the data

8. Reading SQL vs Understanding SQL
SELECT
FROM
JOIN
WHERE
GROUP BY
ORDER BY
FROM
JOIN
SELECT
GROUP BY
WHERE
ORDER BY

9. CONCEPT – JOINs
Basic Concept: JOINs combine rows from two (or more) tables, based on a related column.
Each row from the two related column is evaluated to see if the values match. When they match, the rows are combined into a temporary table.
Columns are then selected from the temporary table to be shown in the results.
Core concept of a relational database
Many types of JOINs to account for different ways of relating tables to one-another (can even JOIN a table to itself)
The database’s Query Optimizer is in charge of figuring out the most efficient way to join these tables together
SELECT
p.patient_nbr,
at.description,
p.age
FROM patients p
JOIN admission_type at ON p.admission_type_id = at.id

10. CONCEPT – GROUP BY Clause
SELECT p.age, COUNT(p.patient_nbr), FROM patients p JOIN admission_type at ON p.admission_type_id = at.id
Basic Concept: Group rows together based on matched values, and compute (summarize, rollup,) values based on the groups.
- Aggregation

11. Consistency … not data consistency (althought thats important too)
I’m talking about consistent process and organization for your work!

12. Consistency also means organized…
Whatever workflow process you have, be consistent and organized.
Consistent requirements
Keeps you and the requestor/reviewer on the same page (sometimes it’s hard to get consistent requirements)
Consistent validation steps
Reviewers can can understand why you did what you did
Consistent workflow
See Google Doc

13. Use Aliases No aliases 👎 Has aliases 👍👍👍
Aliases are your friend! Use them and be consistent when using them.
See examples. While this top query works without aliases, its confusing. We can’t tell which fields in the SELECT come from which table.
Aliases…
Simplify your work: don’t have to write out full table names
Allow you to rename fields to something more applicable
Help with confusion (there happens to be a ‘description’ field in multiple tables...)
If left out, often cause errors due to ambiguitiy
Give reviewers a helping hand
Has aliases 👍👍👍

14. Validation Extremely important. Often forgotten. Why?
Accuracy - You want to be sure that the result-set is the expected one.
Code Reviews - Reviewers might want to know what you did.
Data results can be surprising - Be prepared to defend your work!
Revisiting a query - If you wrote a query a month ago, you might not remember why you did that weird WHERE clause.
Know how to validate your query
Simple things I look at…
Row-counts match:
If the results looks odd, it probably isn’t correct.
Are there oddities in the data itself?

15. Tips, Tricks and Gotchas
Comments
Think ahead about how much data you’re retrieving
SELECT * FROM big_table is bad
COUNT(*) - Great for validation and learning about a table
SELECT 1;
SELECT 2+2;
SELECT function(); - Great for date math!
COALESCE();
WHERE 1 = 1
WHERE TRUE
BEGIN TRANSACTION
Gotchas
NULLs: are they truly NULL values, or are they empty strings?
Joining on different data types

16. Resources stackoverflow.com W3schools.com discuss.codecademy.com
Free
Not Free, but can be worth it
stackoverflow.com
W3schools.com
discuss.codecademy.com
Free Data Sources
NOAA climate datasets:
American Economic Ass. (AEA):
Google Finance:
EHDP Large Health Data Sets:
Airports and their locations: their-locations
Machine Learning Data Set Repository:
Facebook Social Networks (since 2007):
Lynda.com
Udemy.com
Corsera.com