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Hey crunchers! The Query here — we’re running out of jokes so let’s just import some more: import data-jokes as jokes

Here’s what we have for you today:

• A Tableau dashboard guided tutorial for beginners 🤓 

• A dataset for current or future HR analysts 🙆 

• Using PERCENT_RANK in SQL 🔢 

• A Simpsons meme 🟨 

def learn_data_analysis(👨‍💻):

1. Tableau beginner project by Chris French

If you’re learning Tableau, but don’t feel comfortable completing projects on your own, guided projects are the perfect middle ground.

You get real, hands-on experience building, but won’t get stuck and frustrated.

Chris French has an awesome 45-minute guided Tableau project where you’ll create a dashboard for a retail store’s performance.

Check it out here!

select * from dataset-of-the-week

This week’s dataset of the week by Aman Chauhan is perfect if you’re looking to land a role in HR analytics.

It contains employee data with attributes like:

• Age

• Attrition

• Daily Rate

• Department

• Satisfaction scores

• And a lot more (35 columns in total)

An awesome portfolio project idea would be to look into answering the question:

What attributions do your most satisfied employees have in common? What about your least satisfied?

If you create this project, tag us on LinkedIn in a post discussing your project and we’ll share it with our audience!

class MiniLesson:

PERCENT_RANK in SQL

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.

As a beginner data analyst, understanding how to use the PERCENT_RANK function is essential, as it allows you to perform percentile analysis and gain insights into the distribution of your data.

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.

import memes as 😂 

Our Content & Resources 🤓 

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

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

3. Courses: Our course on showcasing your data portfolio is live!

That’s it for today.

Stay crunchin’ folks and see you next week!

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