Good morning fellow data cruncher!

The Query here 👋

Here’s what we have for you today:

• A simple yet in-depth XLOOKUP tutorial 📊

• Using PERCENT_RANK in SQL 👨‍💻

• Data analyst Freelance gigs 💼

• More silly memes 🤣

Looking to become a Data Analyst? START HERE.

def content_spotlight(🔦):

Understanding XLOOKUP is the gateway from beginner to intermediate spreadsheets user.

And it’s listed on tons of job descriptions.

In this week’s video, I break down how to use XLOOKUP.

We start with simple examples, then get more advanced.

I show you how to use the optional 4th, 5th, and 6th parameters, which most people don’t know how to use.

The 5th parameter has some cool uses so make sure to watch the video if you haven’t used it before!

You’ll be an advanced XLOOKUP user in no time!

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freelance gigs

Need work experience? Get real experience with real projects.

1. Beginner Data Analyst — $50 fixed price (apply here)

2. Looker Studio Help — $100 fixed price (apply here)

3. Power BI Analyst — $10-30 per hour (apply here)

class SQLMiniLesson:

Calculating Percentiles with SQL

Kyle here 👋 — Performing percentile analysis is useful for understanding the distribution of data within a dataset.

In SQL, the PERCENT_RANK window function is great for calculating the relative rank of a value as a percentage.

PERCENT_RANK computes the relative rank of a row within the result set, expressed as a percentage.

The value ranges from 0 to 1, where 0 represents the lowest value, and 1 represents the highest value.

This function can be especially useful when analyzing large datasets, as it helps you quickly identify how a particular value compares to the rest of the dataset.

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

We want to calculate the percentile rank of each student's score. In this case, we can use the PERCENT_RANK window function.

In this example, PERCENT_RANK() calculates the percentile rank of each student's score based on the sorted scores.

As a result, we can see how each student's score compares to the rest of the dataset in terms of percentiles.

By using this technique, you can quickly identify trends, outliers, and other important characteristics of your dataset, which can be crucial for effective data analysis.

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content & resources 🤓 

1. Youtube Channel: Click here for Videos on SQL and data analytics.

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

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

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

5. 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

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