The Dataquest Download

I’m excited to welcome 2,916 new learners this week!

In this edition, we look at some of hottest new releases from Dataquest, we unveil our next Zero to GPT lesson, we highlight some standout projects from our community, and we have some cool news from the first No-Lose Lottery.

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.

Over the last few months, we’ve been working hard behind the scenes on a variety of new courses to help round out your data science education, including some updates and reissues. So, in case anything slipped by you during all the excitement, we wanted to take a minute and highlight four recent course releases that no data science learner should miss . . .

Neural Network Fundamentals

First up is the lead course in our new “Zero to GPT” skill path. Neural Network Fundamentals introduces you to one of the hottest things on the web today, GPT models (or Generative Pre-Trained Transformer). We’ll help you brush up on your deep learning, we’ll show you how to use gradient descent to train a linear regression model, and we’ll prepare you to move onto advanced topics like Convolutional Neural Networks and Recurrent Neural Networks. By the time you’ve finished this course, you’ll be solidly versed in deep learning and ready to start training models!

Network Architectures

Also from our “Zero to GPT” skill path, our next showcase is Network Architectures. This snappy follow-up to Neural Network Fundamentals shows you how to put your new skills to work. You’ll build neural networks from scratch using Python to make predictions. When you’re finished here, you’ll be able to train a neural network on a classification task, and you’ll be able to predict sequence data using Recurrent Neural Networks.

We’ve got more exciting courses in the Zero to GPT path on the way, so stay tuned, but for now, here are two other standout releases from the past few months.

Introduction to Deep Learning in TensorFlow

If you’re not already familiar with it, TensorFlow is a very-cool, open-source machine learning platform available to everyone. This introductory course concentrates on the concepts and terms you need to know to start working with sequential models in TensorFlow. Like its cousins in Zero to GPT, Introduction to Deep Learning in TensorFlow will revisit Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), then, you’ll learn how to build, train, evaluate, and improve a basic RNN to predict song popularity using regression.

Optimizing Machine Learning Models in Python

Last but not least, we present a whole bundle of cool machine learning concepts in one convenient course! Here, you’ll learn how to select a model, and you’ll explore cross-validation in machine learning — as well as how to use k-fold and LOOCV cross-validation techniques to check performance. But, we don’t stop there. Next up, you’ll learning how to use regularization in machine learning, then, you’ll go beyond linear models and round out the course by implementing polynomial regression in scikit-learn.

So, there you have it — the latest cutting-edge, interactive, learn-by-doing courses from us here at Dataquest. And these are only four courses from our incredible catalog. Do yourself a favor and explore all the offerings — there’s a whole lot of data science just waiting for eager learners ready to change their lives and careers.

This week, we have some exciting news from the Dataquest Community! First, we have four impressive Community Champions — all of whom have completed one of our guided projects — and we also have our first winner of the Community No-Lose Lottery and some big news from our Learning Assistants . . .

@feelingcxld examined the survey’s results in his project, Clean and Analyze Employee Exit Surveys, and conducted an exhaustive analysis to answer two questions helpful to the institutions involved. The project stands out for its efficient structure, clear goal and methodology, and informative narrative.

In his project on Predicting Employee Productivity Using Tree Models, @m.awon used tree classification models to develop curious insights into garment industry productivity, and he shared his learning experience. The project showcases in-depth technical details and can be used as a reference by anyone interested in machine learning and classification trees.

@kmitchell88‘s project on Finding the Best Markets to Advertise In stands out for its well-structured storytelling, compelling visualizations, to-the-point images, and a great conclusion.

@cayodey shared a Power BI project on Life Expectancy and GDP Variation Viz, which explores demographic and economic trends over the years. The figures are elegant and visually appealing, and they communicate insights clearly and coherently.

@chuawt won the first iteration of the Community No-Lose Lottery. The second iteration of the lottery (4/1/2023 – 5/31/2023) has already started — join us!

We also have four new and four promoted Learning Assistants, among whom are two Community Moderator Interns — check them all here. Also, read here how to become a Learning Assistant or a Community Moderator Intern at Dataquest!

We’re back with the launch of our next Zero to GPT lesson, “Recurrent Neural Networks.” In this lesson, you’ll learn how RNNs are optimized to process sequences of data — and how they’re used for tasks like translation and text classification.

We’re also excited to announce our new goal-setting feature. This easy-to-use function will help you stay on target and complete your learning path when you want to finish. Come check it out!

“I can’t believe how easily and clearly complex material is presented on Dataquest. Things like statistics and programming are not easy to learn. Dataquest explains them more clearly than all other resources. Even beginners can learn easily on Dataquest.” —Viktoria Jorayeva, Business Analyst, Fractal

See you next week!

—Vik and the rest of the Dataquest Team