October 26, 2021
At a recent RStudio Enterprise Meetup, Gordon Shotwell from Socure shared advice on resolving common tensions between data science and security teams. Through continuous conversation, closed systems for data, and streamlined tools, organizations can set up the relationships and systems needed to be successful.
October 14, 2021
We interviewed Vergil Weatherford from Guidehouse to learn why they are planning to hire a Senior Shiny Deployment Engineer. Weatherford believes data science teams can benefit from someone who can apply software development best practices to support the deployment of high-quality R and Python applications into production.
August 3, 2021
The team at Children’s Wisconsin is using R and RStudio’s suite of tools to enable forecasting, modeling, and data mining among other data science activities. During the meetup, Chris Bumgardner shared a few examples of the applications that have been created to support their vision that the kids of Wisconsin will be the healthiest in the nation.
July 15, 2021
We recently sat down with Art Steinmetz, former CEO of OppenheimerFunds, to get his unique perspective on the value and viability of code-first, open source data science for the enterprise.
June 24, 2021
Similar to any other large and complex organisation, Monash University employs a wide range of data to inform decision making and monitor different aspects of their operations.
June 15, 2021
Data science teams sometimes believe that they must standardize on R or Python for efficiency, at the cost of forcing individual data scientists to give up their preferred, most productive language. RStudio’s professional products provide the best single home for R and Python data science, so teams can optimize the impact their team has, not the language they use.
June 10, 2021
From establishing your first data science team, to growing your team, and stepping into leadership roles, check out the Q&A from the Building Effective Data Science Teams webinar audience questions
June 8, 2021
When it comes to adopting open source tools into clinical processes, there are many questions that have to be addressed, such as how do we migrate legacy workflows over to new programming languages? How do we upskill a workforce to use these new tools? Join us June 22nd for a discussion with Michael Rimmler about how GSK was able to do this.
June 3, 2021
Last month’s webinar featured data science leaders from Caliber Home Loans, The Looma Project, Saturn Cloud, T-Mobile, and Warner Music Group to start to answer the question, "What does it take to build a successful data science team?"
May 25, 2021
On June 16th, we’ll be teaming up with eoda and Covestro to discuss—in German—how they put their data science into production by developing a unified infrastructure.
May 18, 2021
Data Driven West Virginia, in collaboration with the WV Army National Guard, built a COVID vaccine management system using Shiny to help quickly and efficiently distribute vaccines throughout the state.
May 12, 2021
While you may already be a practitioner of code-first data science with R or Python, chances are you work within a larger organization with many analytic tools. In this post, we give you some advice on navigating this landscape, and convincing others in your organization of the value of code-first data science.
April 8, 2021
Model monitoring is a key component of ModelOps, and the R ecosystem offers flexible, code-first solutions that meet the model monitoring needs of data science practitioners.
April 1, 2021
Continuing our series on self-service BI tools and code-first, open source data science, we explore embedded analytics and why they are a critical way for a data science team to deliver insights. We also discuss the unique needs that will be demanded of data science teams as they deliver insights that are secure, scalable, and flexible to multiple end user’s needs.
March 25, 2021
One powerful approach for BI and data science collaboration is to share augmented data. In this post we cover the details of this technique and include an example where data scientists supply BI teams with forecasts and calculated columns.
March 18, 2021
In this post, we'll provide some insights from organizations who have used both types of tools and give some guidance about which you should use when.
March 11, 2021
Continuing our series on self-service BI tools and code-friendly, open source data science, in this post we dive into the strengths and challenges of the different approaches.
March 4, 2021
In many large organizations, Business Intelligence and Data Science teams compete for budget and mindshare. By focusing on how data science can both augment and complement BI tools such as Tableau and PowerBI, these teams can unite on their common goal: delivering data-driven insights for better decision making.
January 13, 2021
Over the past year, RStudio has invested in making our pro and open source offerings the best common home for both R- and Python-based Data Science. In this blog post, we explain why we support both Python and R, review these recent features, and encourage readers to attend our upcoming webinar.
January 6, 2021
In this post, I continue our series on how to create your own Google Analytics dashboard in R. Using the data we downloaded in our last post, we'll now create a simple dashboard showing blog page views over time and highlighting the most popular ones.
November 27, 2020
This article, the first of three, describes how to use a code-oriented data science approach to Google Analytics data from a blog. It creates custom views of raw GA data while hiding the complexity of the Google Analytics data and interface
November 19, 2020
RStudio analyzes how the California Department of Public Health built a COVID-19 dashboard in R and Shiny that now serves millions of California citizens.
November 12, 2020
Over the last few years, more companies have begun migrating their data science work to the cloud. As they do, they naturally want to bring along their favorite data science tools, including RStudio, R, and Python. In this blog post, we discuss the various ways RStudio products can be a part of that journey.
October 8, 2020
Andy Nicholls and Michael Rimler from healthcare firm GlaxoSmithKline plc (GSK) answer questions posed during their recent webinar, Using R to Drive Agility in Clinical Reporting.
September 25, 2020
Read The Forrester Wave™: Notebook-Based Predictive Analytics and Machine Learning, Q3 2020 Report to learn why RStudio was named a Strong Performer by this independent research firm, and received the highest scores possible in the evaluation criteria of security, apps, open source and platform infrastructure.
September 23, 2020
To help address some of the uncertainty data science leaders may be feeling heading into the fall planning season, we note three new resources to help your team learn new skills and communicate their value better.
September 10, 2020
In this post, we answer questions raised by participants and attendees during our recent Debunking R & Python Myths webinar. Our bottom line was to use the tools that let you be most productive in the shortest amount of time.
August 27, 2020
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
August 20, 2020
The renv package helps create reproducible project environments that are critical for data science teams to deliver real, lasting value.
August 17, 2020
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.
August 5, 2020
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.
July 15, 2020
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.
July 9, 2020
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.
June 24, 2020
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.
June 2, 2020
For Data Science to be applied, decision makers must trust and accept the insights. Your team needs the tools to find relevant insights, and to communicate these insights in a way that builds trust and understanding.
May 27, 2020
A slew of new vendors believe that no-code analytics and visualization tools can replace the role of the traditional data scientist. This brief describes why we believe organizations will demand pro-code data scientists for years to come.
May 19, 2020
Driving lasting value in an organization with data science is critical but difficult. The truth is most projects fail. What’s the answer? Serious Data Science is credible, agile and durable.