DataCamp recently migrated RStudio workspaces to DataCamp’s Notebook Editor which features a JupyterLab interface. This post is designed to help R users get started and be productive with JupyterLab.

DataCamp invites you to Radar, a free online event to help you navigate the future of data careers. Learn more about what we have planned and what you will learn at this action-packed event on June 23, 2022.

DataCamp has launched an exclusive community platform for all learners who have become certified through our certification program to make the most of their newly acquired skills. Get certified today to access this exciting new platform!

Learn about the Zen of Python and discover the principles of writing clean, elegant code in Python.

Learn what a data architect does and how to launch a lucrative career in this rapidly expanding niche.

Discover how DataCamp is helping candidates land their dream jobs and letting recruiters find the best data talent in one place.

Your monthly roundup of the best new DataCamp resources, including a Microsoft Power BI partnership, a smarter way to hire, and your Q2 product roadmap!

We're launching a four-day DataFramed Careers Series covering the ins and outs of building a career in data, and the different aspects of standing out from the crowd in the job hunt.

Recruiting for data roles has become extremely difficult. From a scarcity of qualified talent to time consuming assessments, the hurdles are endless. In this article, we'll go through the current state of hiring for data roles and how DataCamp can help you find and hire you data teams better.

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.