The Dataquest Download

I’m excited to welcome 2,830 new learners this week!

In this edition, we look at our own CEO’s data science learning journey, we unveil our next Zero to GPT lesson, and we highlight some standout projects from the Dataquest community.

As always, we’d love to hear your feedback. If you have questions, or if there’s a topic you’d like us to cover, just reply to this newsletter

I was a college graduate with a history degree and few prospects a little over a decade ago. Then, I became a successful machine learning engineer, data science consultant, and, finally, CEO of Dataquest.

This is not an overnight success story. My learning journey was long, inefficient, and frequently discouraging.

If I could do it over again, I would do one big thing differently. It would have fast-tracked my career, saved thousands of hours of wasted time, and prevented a lot of stress.

When I started learning, I wanted to do the things that excited me, like making websites. Unfortunately, the course I was taking forced me to spend multiple months on syntax. It was agony.

After many failed attempts, I stopped trying to learn syntax and immediately dove headfirst into a project I actually found interesting.

Applying your knowledge right away will help you retain everything you’ve learned. It’s better to begin by following structured projects until you feel comfortable doing your own projects. Here at Dataquest, we’ve strategically included structured projects in virtually all of our courses. That way, you can immediately apply what you’ve learned.

Projects are crucial. They stretch your capabilities, help you learn new concepts, and allow you to showcase your abilities to potential employers. Once you’ve done a few structured projects, you can start working on your own.

This week, we have three impressive Community Champions — all of whom have completed one of our guided projects  . . .

@FilipeFava shared a project on Profitable apps profiles for Google and Apple mobiles, wherein he explored an uncharted area when it comes to using different tools to analyze the most profitable apps. The project is a great example of using seaborn to generate visually appealing figures, as well as telling a cohesive and insightful narrative.

In his project, Optimizing Model Prediction Using Forest Fire Dataset, @m.awon used forest fire data to build a reliable machine learning model. He applied various machine learning techniques, created compelling data visualizations, and provided in-depth reasoning to achieve excellent predictive results.

@giorgia shared a stunning project on Building a Spam Filter with Naive Bayes. Her project stands out for its clear structure, thorough explanations of  technical details, and robust code.

This week, we have more exciting release news for our Zero to GPT skill path! The latest course in the series, “Optimizing Network Parameters,” is now live with its first lesson, “Backpropagation in Depth.” So far, we’ve taken only a few quick glances at neural network architecture in this skill path, but in this lesson, we’ll begin building a miniature version of PyTorch to better understand backpropagation.

We’re also excited to announce that we’ve finished drafting a new Windows Functions course! It’ll be headed into beta testing in a couple of weeks, so keep your eyes peeled for updates!

“The methodology on Dataquest was ideal. I get distracted easily mid-task and will start another task. So, when I tried videos, I found myself not listening and having to re-watch, whereas reading and coding helped my concentration and retention.” —David Rushton, Sales Engineer, Cyber Security Works

See you next week!

—Vik and the rest of the Dataquest Team