Data Science Leadership

3 Ways to Expand Your Data Science Compute Resources

2020-08-27 Carl Howe
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Data scientists frequently have computational needs that stretch far beyond their laptops. Data science leaders should embrace features of RStudio Server that give data scientists access to shared IT resources without breaking the bank Read more →

Why Package and Environment Management is Critical for Serious Data Science

2020-08-20 Mike Garcia, ProCogia
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The renv package helps create reproducible project environments that are critical for data science teams to deliver real, lasting value. Read more →

R and RStudio - The Interoperability Environment for Data Analytics

2020-08-17 Curtis Kephart and Lou Bajuk
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From design philosophies to current development priorities, R with RStudio is a wonderful environment for anyone who seeks understanding through the analysis of data. Here's why. Read more →

Do, Share, Teach, and Learn Data Science with RStudio Cloud

2020-08-05 Lou Bajuk
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RStudio is proud to announce the general availability of RStudio Cloud, its cloud-based platform for doing, teaching, and learning data science. WIth RStudio Cloud, there's nothing to configure and no dedicated hardware or installation required. Individual users, instructors, and students only need a browser. Read more →

3 Wild-Caught R and Python Applications

2020-07-28 Carl Howe
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In this post I present three "wild-caught" examples solicited from the R community of how they use interoperability between R, Python and other languages to solve real-world problems. Read more →

Interoperability: Getting the Most Out of Your Analytic Investments

2020-07-15 Lou Bajuk, Carl Howe
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No single platform meets all the analytic needs of every organization. To avoid productivity-sapping complexity and underutilized infrastructure, encourage Interoperability so that your data scientists can access everything they need from their native tools. Read more →

Why You Need a World Class IDE to Do Serious Data Science

2020-07-09 Daniel Petzold
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Data Science presents challenges in the iteration of new research, unique business requirements, multiple technologies, accountability of results, and finding lasting solutions. Learn how an Integrated Development Environment (IDE) built for Serious Data Science tackles these issues head-on. Read more →

Interoperability in July

2020-07-07 Carl Howe
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RStudio will be focusing on interoperability in this blog during the month of July, highlighting how data scientists are using other tools with R to perform their work. Read more →

Future-Proofing Your Data Science Team

2020-06-30 Dean Wood, Mango Solutions
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Data science today requires allowing employees to work from home. Mango Solutions believes that a centralized cloud-based platform and collaborative communication are key to making data science teams productive. Read more →

Does your Data Science Team Deliver Durable Value?

2020-06-24 Lou Bajuk, Carl Howe
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Delivering persistent value over the long haul from your data science team requires reusability, reproducibility, and up-to-date insights, built on a sustainable platform. Read more →