Blog | AI and Data Science Articles

DataCamp Donates very first digest of 2022 shows strong results thanks to an acceleration in the number of partners joining our program. This quarter, we broke records and we hope to reach for even higher achievements for the remainder of the year.

Looking to expand your data science stack in 2022? This guide highlights 10 of the fastest-growing Python packages that solve various problems you might encounter.

Learn about the key differences between the two most popular data science roles, including which skill sets are required, key duties, project life cycles, and earning potential.

In this article, we'll break down the different data roles practitioners can pursue that go beyond just "data scientist".

With the rise of large language models like GPT-3, NLP is producing awe-inspiring results. In this article, we discuss how NLP is driving the future of data science and machine learning, its future applications, risks, and how to mitigate them.

Is it worth learning Julia in 2026? To answer the question, we will look at the core language, its features, growth statistics, and how it fares against its competitors.

This week we take a look at the tech behind Kendrick Lamar’s latest music video in which he uses deepfakes, how an AI is helping to get rid of annoying web cookie popups, Meta's new large language model, and more!

GPT-3 is transforming the way businesses leverage AI to empower their existing products and build the next generation of products and software.

Discover the inspiring story of Amanda Wade, a DataCamp Donates scholarship recipient, through our nonprofit partner BreakLine, who attained a Data Analyst job at Google with no prior experience.

Find out how much you can earn and how to boost your existing salary in data science, one of the most exciting and in-demand fields in the job market.

In this blog, we delve into what data science is and explore the answers to frequently asked questions about the various aspects of data science.

Creating and showcasing a solid data science portfolio is an essential step in bringing what you learn into reality. Conceptualizing, developing, and publishing your work requires consistency and persistence, but your hard work can help you position yours better.