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GM crunchers! The Query here — the data newsletter that's like a Python script for your data career, automating the flow of knowledge and empowering you to tackle any challenge with ease.

Here’s what we have for you today:

• What analytics and visualization at Netflix is like 🎥 

• A finance and accounting course portfolio project dataset

• A function for data cleaning in SQL

• A meme that’s too true 🤣

def learn_data_analysis(👨‍💻):

Analytics and Visualization at Netflix

If you’re like many readers of this newsletter, you’re working toward getting your first job as a data analyst.

As you prepare, it’s really helpful to read about what analytics is like at different companies because it’s like giving you a peak inside what your future will be like.

You can use this view into the future to help navigate the roles you apply for and eventually accept.

Netflix (part of the renowned FAANG) is world-class when it comes to technical roles, analytics included.

They have a technical blog where people that work in Analytics at Netflix write about what it’s like.

Give these two articles a read:

• Analytics at Netflix: Who We Are and What We Do

• A Day in the Life of a Content Analytics Engineer

select * from dataset-of-the-week

The dataset of the week is a doozie 😃 

It’s ~13k finance and accounting course data scraped from Udemy by Jil Kothari.

Here are the columns you have to work with:

I really like this dataset for a portfolio project if you worked on answering the following question:

How do the most popular courses differ from the least popular?

If you’re going to work on this problem and want our advice, reply to this email with how you’re thinking about approaching the analysis and we’ll respond with some advice to push you in the right direction.

class MiniLesson:

The REPLACE function in SQL

The REPLACE function in SQL is a handy tool for cleaning data.

It allows you to replace any characters you want with anything else.

It's commonly used to remove characters from a string of text by replacing them with an empty string "".

The syntax for the REPLACE function is as follows:

REPLACE(input_string, search_string, replacement_string)

Let's consider an example where we have a table named users with email addresses formatted with <> around them.

This is something I've run into as a data analyst.

The data looks something like the table below.

We want to remove the <> characters from the email addresses.

We can use the REPLACE function in a query to achieve this.

In this example, we use nested REPLACE functions to remove both < and > characters.

The inner REPLACE function removes the < character by replacing it with an empty string (''), and the outer REPLACE function removes the > character in the same way.

This can be particularly helpful when working with datasets that contain inconsistencies, typos, or specific formatting that needs to be adjusted before analysis.

While there are my customizable solutions (like Regex) for cleaning data, the REPLACE function offers a simple and efficient method for making these adjustments directly within your SQL queries, without the need for external tools or programming languages.

import memes as 😂 

Technical interview: solving complex algorithmic problems

Actual job: code janitor cleaning up misplaced semicolons

That’s it for today.

Stay crunchin’ folks and see you next week!

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