The Query
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🤓 how to excel in data

🤓 how to excel in data

The Query
April 06, 2023

Good morning crunchers! This is The Query — the newsletter that serves as the DATA WAREHOUSE for your data career. We store, analyze, and deliver the most valuable insights just for you.

Here’s what we have for you today:

A “Query-Approved” Excel for Beginners course
A realistic SQL interview questions video
A Spotify dataset to rock out to 🤘🎸
An awe-inspiring data visualization
An abundance of hilarity

select * from content-spotlight

1. A Great Excel for Beginners Course. We vetted a bunch of Excel for Beginners courses on YouTube. This one stood out as one of the best. He explains a lot of the basic functions of Excel you will use as a data analyst. If you want to learn Excel or up your game, check out this 1 hour video.

2. Data Analyst SQL Interview Questions. Interviews often give people major anxiety. To ease the discomfort it’s helpful to practice a bunch of potential interview questions to prepare. Most of the data analyst interview question lists I see out there are crap. This video goes over the basic SQL questions you should master before going into your next interview.

3. SQL in Google Sheets (sort of) - The QUERY Function. The QUERY function is one of the most interesting functions in Google Sheets for working with data. It doesn’t have all the functionality of SQL but allows you to use some of the basics like SELECT, WHERE, GROUP BY, ORDER BY, etc. to select and filter data in a sheet. This guide by Ben Collins should be all you need to get started.

class LearningResources: 📊

It's time to hunker down, hone your abilities, and get ready to master the realm of data.

You can become a data sorcerer quicker than you might imagine.

Discover our curated learning materials below:

three project-based learning resources

Here's the lowdown on these projects, ranked from beginner to more advanced…

The initial projects are guided tutorials, familiarizing you with nifty tools like Excel or Tableau.

But easy-peasy tutorials won't wow potential bosses.

That’s why project three provides just a dataset - similar to the real world.

1. Gantt Chart in Google Sheets/Excel

2. Full Project - SQL, Excel, Tableau

3. Spotify Tracks Dataset

two technical tips

Technical time!

One Python tip and one SQL tip for your analytics pleasure…

1. Python Tip 🐍: Easy Variable Assignment

When you want to assign values to multiple variables at once, you can use tuple unpacking.

This technique makes your code more concise and readable by allowing you to assign multiple variables in a single line.

Let's say you have three values that you want to assign to three variables: x, y, and z.

There is the usual way beginners learn variable assignment…

And a more elegant way to assign variables.

Check out the two methods below:

The tuple unpacking method makes your code look cleaner when you have many variables that need to be assigned!

There's no strict rule about how many variables you can or should use with tuple assignment.

Readability and maintainability should always be your priority.

2. SQL Tip 👨‍💻: ORDER BY at the end

When you're working with SQL, it's crucial to write efficient queries to retrieve the data you need as quickly as possible.

One way to optimize performance is by ensuring that you use the ORDER BY clause only at the end of your query, rather than in subqueries or derived tables.

The reason behind this is that sorting data can be computationally expensive, especially when dealing with large datasets.

Here is an example of an inefficient ORDER BY at the beginning of a query vs. an efficient ORDER BY at the end.

By using ORDER BY at the end of your query, you give the database engine the opportunity to filter, join, or aggregate the data before sorting it.

This reduces the amount of data that needs to be sorted, making the process more efficient and faster.

one tool 🔧

Meet JSON Formatter — In data analytics you often come across a data format called JSON.

JSON is the format in which nearly all APIs send data.

The problem?

It’s ugly and hard to read if not formatted.

Here is an example of unformatted vs. formatted JSON so you can see the difference.

Unformatted

{"name":"Alice","age":30,"address":{"street":"123 Main St","city":"New York","state":"NY","zip":"10001"},"hobbies":["reading","traveling","cooking"]}

Formatted

def data_jobs(👨‍💼👩‍💼):

remote, entry-level data jobs

Working remotely means you don’t need to worry about coworkers stealing your snacks from the office fridge 😉

Data Analyst Intern @ You.com (apply here)
Data Analyst, Quality @ Premier — $43-79k (apply here)
Data Analyst, Consulting @ Cambia Health — $68-112k (apply here)
Data Analyst, Healthcare @ Qlarant — $59-91k (apply here)
Data Analyst, Warehouse Ops @ Black Tux — $95-105k (apply here)

freelance data gigs

Freelancing is a GREAT way to get paid to improve your data analytics skill set.

Clean Up and Sort Data — $20 (apply here)
KPI Dashboard — $5,000 (apply here)
Analyze Survey Responses — $125 (apply here)
Data Analyst — $30-60/hour (apply here)

import entertainment as fun

meme of the week

data visualization of the week

Amazing views of world infrastructure…

Click the link and scroll through all the different infrastructure views

Airports
Seaports
Railroads
Cities
and more!

data tok

The interview vs. reality