The Query (aka Kyle and Cody) here 👋

Here’s what we have for you today:

• Handling a common data analyst task 📊

• A SQL function for helping with a pesky error message 👨‍💻

• Data analyst jobs that use SQL 💼

• Memes! 🤣

Looking to become a Data Analyst? START HERE.

select * from data-jobs

remote, data jobs

Open to exploring new job opportunities?

We cultivate the best data analyst jobs from around the internet to make your search easier.

Check out this week’s featured jobs here.

1. Data Analyst, Marketing @ Native Path — $50-60k per year

2. Data Analyst @ Erickson Senior Living — $100k per year

3. Senior Data Analyst, Product @ Spring Health — $125-145k per year

freelance gigs

Need work experience? Get real experience with real projects.

1. Junior Data Analyst — $13-30 per hour (apply here)

2. Data Analyst Needed — $intermediate (apply here)

3. Excel Data Analyst — $20 fixed price (apply here)

def content_spotlight(🔦):

As a data analyst, one crucial task you'll encounter is user segmentation.

Understanding your audience is essential in today's competitive business environment, and user segmentation is a powerful tool to achieve that.

This article dives into how to do user segmentation and how it can significantly enhance business strategies by improving personalization, targeting, and customer experiences.

class SQLMiniLesson:

A SQL Function to Deal with a Pesky Error

Kyle here 👋 — If you’ve been analyst for long you have probably run into this common error: Can’t divide by zero.

Here's how to deal with it.

SAFE_DIVIDE is a function in SQL that allows you to perform division operations while safely handling potential “divide-by-zero” errors.

If a divide-by-zero error is encountered, instead of causing your query to crash or return an error, the SAFE_DIVIDE function will return NULL.

Here's an example to illustrate its usage:

Suppose we have a table named sales with the following schema and data:

We want to calculate the average revenue per unit for each product. If we try to divide total_revenue by units_sold directly, we will encounter a divide-by-zero error for product_id 2.

To avoid this error and handle the division safely, we can use the SAFE_DIVIDE function.

In this example, the SAFE_DIVIDE function takes two arguments:

• The numerator (total_revenue)

• The denominator (units_sold)

When the denominator is zero, as in the case of product_id 2, the function returns NULL instead of causing an error.

As a data analyst, understanding how to use functions like SAFE_DIVIDE to handle potential errors in your SQL queries is important.

It helps you maintain the stability and reliability of your analysis, allowing you to work with data that may contain unexpected zero values or other edge cases that could otherwise cause your queries to fail.

Note: SAFE_DIVIDE is what this function is called in BigQuery. Other SQL environments may have a different name for this function but it is essentially the same thing. To find yours just google “Safe divide [insert platform i.e. Snowflow, Postgres, etc.]”

import memes as 😂 

content & resources 🤓 

1. Become a Data Analyst Guide: Our full guide on what it takes to land a job as a data analyst.

2. Open Data Analyst Jobs: Find your next data job here!

3. Download our SQL Cheatsheet as a PDF and desktop wallpaper here. 

4. LinkedIn: We create content on LinkedIn daily. You can follow Cody here and Kyle here.

That’s it for today.

Stay crunchin’ folks and see you next week!

— Kyle & Cody

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