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Hey crunchers! This is The Query — the data newsletter that's like the perfect visualization for your data career. We'll make complex insights easy to understand and engaging to explore.

Here’s what we have for you today:

• Learn SQL for free with challenges and projects

• An EPIC Marketing analytics dataset

• A rare but powerful SQL join type

• A hearty laugh 🤣

def learn_data_analysis(👨‍💻):

1. 15 basic Excel formulas data analysts need to know 🧠 

If you’re just dipping your toes into data analytics, these 15 Excel functions are a great way to get your feet wet.

When I think back to my first data analytics internship, I remember learning more than half of these in the first week.

Check it out here!

2. Free, comprehensive SQL course at Khan Academy!

At The Query, we're all about project-based learning.

It's hands down the best way to learn. Period.

That’s why I recommend the Khan Academy course on SQL.

It has multiple challenges and a killer project for each section. If you're new to SQL and haven’t started a course yet, give this one a look.

Check it out here!

select * from dataset-of-the-week

This week’s dataset is the Marketing Funnel dataset by Olist!

As an analyst that works with marketing data, I ❤️ this dataset.

It’s perfect for a portfolio project, especially if you join it with last week’s ecom dataset using seller_id (see the Data Schema section on the Kaggle page for more details.)

Also, if you want to work in growth or marketing analytics, it’s an epic dataset for learning marketing origin data.

Here are some questions to get your analysis started:

• Assuming all the MQLs have either been lost or closed, what is the MQL to Closed % by origin?

• Create a stacked bar chart showing the distribution of MQLs by origin by month.

class MiniLesson:

The FULL OUTER JOIN — Rare But Powerful

In SQL, we all know the common joins like LEFT JOIN and INNER JOIN.

But there is a lesser used, but powerful join most beginners don’t know about.

It is the FULL OUTER JOIN. 

Like the mythical Dodo bird, it's rare to see one in the wild.

But it’s super helpful in the right scenario.

To review the basics of SQL joins:

• INNER JOIN returns only the rows where there's a match between the joining columns in both tables.

• LEFT JOIN returns all the rows from the left table and the matched rows from the right table. If no match is found, NULL values are used for the columns from the right table.

FULL OUTER JOIN is a type of join operation that returns all rows from both tables. 

And if there's no match between the tables, NULL values are used where no match is found.

This join operation is useful when you want to retain all the data from both tables, even if there's no direct match between them.

Let's look at an example.

Suppose you have two tables: ad spend and orders.

The ad_spend table stores information about the advertising costs.

The orders table stores information about orders that are attributed to the ad spend.

To analyze this marketing campaign, we want to join these tables together.

But if you use a LEFT JOIN or INNER JOIN your data will be incomplete.

If you use a LEFT JOIN your output will be missing any orders that weren’t attributed to ads.

And if you use an INNER JOIN you will be missing ad spend that didn’t lead to an order.

To make sure we include all the data from both tables we use a FULL OUTER JOIN like this:

The FULL OUTER JOIN includes all the rows from both tables, even when there's no match between the ad_id columns.

The unmatched row from the orders table (with order_id 5) have NULL values for the columns from the ad_spend table.

Now when we aggregate our data for analysis, we are dealing with all the data we need.

Whenever you come across this type of scenario, remember the FULL OUTER JOIN is your friend.

import memes as 😂 

SQL Evolution: From rookie rake accidents to seasoned rake face-slaps.

That’s it for today.

Stay crunchin’ folks and see you next week!

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