Hello fellow data cruncher!

The Query here (aka Kyle and Cody) 👋

Here’s what we have for you today:

• Analyze time series with SQL 📊

• How to design the perfect dashboard 👨‍💻

• Newly posted Data analyst jobs 💼

• I love this meme 🤣

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 @ Albert Einstein College — $54-63k per year

2. Senior Marketing Data Analyst @ Splice — $132-157k per year

3. BI & Data Analyst @ Charles River — $120-140k per year

freelance gigs

Need work experience? Get real experience with real projects.

1. Help preparing for data analysis exam — $18-25 per hour (apply here)

2. Marketing analytics with R — $100 fixed price (apply here)

3. Google Sheets help needed — $50-75 per hour (apply here)

def content_spotlight(🔦):

Picking the right visualization to create and how to design a dashboard are key concepts you’ll need to understand in your data analyst learning journey.

Prince Patni created an EPIC guide that goes super deep into building dashboards and even provides a checklist for the next dashboard you develop.

Check it out here.

class SQLMiniLesson:

Analyze Time Series with LAG()

Kyle here 👋 — Time series analysis is one of the most important skills you can have as a data analyst.

This is because most important insights are related to a change in some metric over time.

The LAG() function in SQL is a window function that is helpful for this.

Using the LAG() function, you can calculate period-to-period changes, such as the difference in sales between consecutive months or the growth in user signups from week to week.

It can seem a bit confusing at first since it’s a window function but I promise you’ll get the hang of it.

Below I show how to use it to calculate a month-over-month change in sales column.

Let's consider an example where we have a table named monthly_sales with the following monthly sales data:

In this example, the LAG() function is first used within the OVER() clause, which defines the order of rows to be used in the calculation.

This retrieves the sales value from the previous row, relative to the current row, when sorted by the month column (previous_month_sales).

Then, we subtract the previous month's sales from the current month's sales to get the sales change.

Understanding how to use the LAG() function in SQL is essential, as it allows you to perform period-to-period comparisons and calculations within your queries.

By using the LAG() function, you can gain insights into how values change over time, helping you identify trends, growth rates, and patterns in your data.

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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