The Query (aka Kyle and Cody) here 👋

Here’s what we have for you today:

• Remote jobs you should probably apply to 💼

• An awesome data visualization tutorial series 📊

• A lesson on running split tests as a data analyst 🖖

• Another Excel-ishious meme 🤣

Looking to become a Data Analyst? START HERE.

select * from data-jobs

remote, data jobs

Because who likes writing SQL from a busy office?

1. Junior Data Analyst @ Fitness Matrix — $60-74k per year (apply here)

2. Data Analyst, Automation @ Lionbridge — $75k per year (apply here)

3. Senior Data Analyst @ Thirty Madison — $140-192k per year (apply here)

freelance gigs

Need work experience? Get real experience with real projects.

1. Fix Excel Template for Medication Tracking — $17-50 per hour (apply here)

2. Power BI Help Needed — $15 fixed (apply here)

3. Entry-Level Data Analyst — $entry level (apply here)

def content_spotlight(🔦):

This week, we’re featuring a helpful series of tutorials on how to get started with Power BI.

There are many options for data visualization. One of the most common ones I often see on job applications is experience with Microsoft Power BI.

Many companies use the Microsoft stack to run their business, which means many of them are also using Power BI.

Power BI would probably be my #1 recommended data visualization tool of choice for a beginner (after learning how to make charts and graphs in Excel or Google Sheets).

class MiniLesson:

How to Run an A/B Test

Kyle here 👋 — In my early years as an analyst there were a lot of skills I learned beyond just technical SQL skills. This week, we dive into a crucial skill for your analytical arsenal: A/B testing.

Also known as split testing, A/B testing is a method to compare two versions of a webpage, email, or other marketing assets to determine which one performs better.

And split testing can go beyond analyzing marketing campaigns to virtually any aspect of a business where you are unsure about a decision.

Here are the basic steps involved to run an effective spit test…

1. Define Your Objective: The first step in an A/B test is crystal-clear: you must have a well-defined objective. This could be anything from increasing click-through rates on a webpage to enhancing email open rates. Specificity is key – your goal should be measurable and directly related to the elements you're testing.

2. Choose What to Test: Once your objective is set, decide what you will test. This could be anything from the color of a call-to-action button to the subject line of an email. Remember, only change one variable at a time. If you alter multiple aspects, it becomes difficult to ascertain which change influenced the results.

3. Create Two Variants: Next, create your 'A' and 'B' variants. 'A' is usually the currently used version (the control), while 'B' is the new version (the variant). Ensure that these two versions differ only in the one aspect you're testing.

4. Segment Your Audience: Your test subjects need to be randomly divided into two groups: one experiencing the 'A' variant and the other experiencing 'B'. It's crucial that the division is random to avoid any bias, ensuring the test's reliability.

5. Run the Test: With everything set, launch your A/B test. The duration depends on your specific context – it could be a few days or several weeks. You need sufficient data to make a statistically significant conclusion.

6. Analyze the Results: After collecting the data, analyze it to see which version performed better in achieving your predefined objective. Tools like Google Analytics can be instrumental here. Remember, it's not just about which version won, but understanding why it won.

7. Implement and Learn: Finally, use the insights from your test to make informed decisions. If 'B' outperformed 'A', consider implementing 'B'. Regardless of the outcome, each A/B test is a learning opportunity, offering valuable insights into user preferences and behavior.

A/B testing is a powerful tool in the data analyst's toolkit, providing evidence-based insights that drive better decision-making.

Happy testing, and may your data always guide you to the right decision!

import memes as 😂 

Excel God mode activated 🔥

content & resources 🤓 

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

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

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

4. 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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