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GM crunchers! The Query here — the data newsletter that's like the Pandas 🐼 library for your data career, helping you manipulate, analyze, and transform your data with ease.

Here’s what we have for you today:

• Learn statistics for data analysis 🟰 fun!

• A business problem and dataset for your portfolio project

• Working with JSON data in SQL

• A good ole’ fashion meme-er-oo 😆 

def learn_data_analysis(👨‍💻):

1. HackerRank’s 10 Days of Statistics Course ➗ 

To be a data analyst, you don’t need advanced statistics skills.

You just need to know the basics.

And you can learn the basics in about 10 days by completing the “10 Days of Statistics” course by HackerRank.

Check it out here.

And then to supplement your learning of statistics, check out these two resources:

• Descriptive Statistics: Definition, Charts, and Graphs

• Inferential Statistics: Definition and Uses

Each of the above articles links to other resources that go deeper into specific stats topics.

Happy learning 😁 

select * from dataset-of-the-week

This week’s dataset is an Airline Passenger Satisfaction dataset by TK Klein.

This is a great dataset for a customer success/service analyst portfolio project.

Here are the columns you have to work with:

Business Problem: What customer segments are the MOST satisfied? Why? What customer segments are the LEAST satisfied? Why?

class MiniLesson:

Working with JSON Data

JSON is a simple yet powerful data format that's integral to the work we do as data analysts.

What is JSON?

You’re probably used to seeing data formatted as rows and columns — like in a spreadsheet or SQL table.

JSON is just a different way to format data that’s not rows and columns.

JSON data is represented as key-value pairs, similar to a dictionary in Python.

Why is JSON important?

In today's interconnected world, data is everywhere and it's often served to us via APIs.

APIs use JSON to deliver data because it's light, easy to understand, and can be used by many programming languages.

Now, let's imagine you've received a JSON file from an API and you want to work with this data in SQL.

How do you do that? Let's explore.

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

SQL has functions to help us handle JSON data.

One common SQL function is for working with JSON is the JSON_VALUE function.

This let’s us extract things from our JSON data.

In this example, the JSON_VALUE function takes two arguments:

1. The JSON data column (profile_data)

2. A JSON path expression ('$.name') that specifies the key to extract the value from.

The $.name JSON path expression refers to the "name" key in the JSON data.

Knowing how to extract specific values or manipulate JSON data can greatly enhance your ability to analyze and work with such data.

Remember, JSON and SQL might seem like different languages, but with a little practice, you'll be able to translate between them with ease. It's all part of your journey to becoming a versatile data analyst.

So don't be afraid of JSON!

Embrace it, play around with it, and soon, you'll be extracting and analyzing data like a pro. Happy analyzing!

import memes as 😂 

That’s it for today.

Stay crunchin’ folks and see you next week!

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