Data Science Leadership

How Data Scientists and Security Teams Can Effectively Work Together

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

Why Your Data Science Team Might Need a Shiny Deployment Engineer

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

RStudio Voices - Julia Silge

For the first piece in our new RStudio Voices series, we decided to interview one of our open source package developers, Julia Silge

Democratizing Data with R, Python, and Slack

In connection with R, Python, Slack and RStudio Connect, CM Group built a system that serves custom, individualized insights directly to stakeholders, at the right time, where work already happens

R in Healthcare Meetup Q&A

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

A CEO’s View of Open Source Data Science in the Enterprise

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

Strategic Analytics at Monash University: How RStudio Accelerated the Transformation

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

Debunking the Myths of R vs. Python

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

Building Effective Data Science Teams: Answering Your Questions

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

Upcoming Webinar - Incorporating R into your Clinical Legacy Workflows

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

Building Effective Data Science Teams

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?"

Centralizing your Analytics Infrastructure with eoda and Covestro

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

Managing COVID Vaccine Distribution, With a Little Help From Shiny

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

Code-First Data Science for the Enterprise

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

RStudio and APIs

APIs can dramatically increase the impact of data analytics by making real time results accessible to a larger audience. RStudio empowers organizations to both create and use analytic APIs

Model Monitoring with R Markdown, pins, and RStudio Connect

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

Julia Silge

BI and Data Science: Deliver Insights Through Embedded Analytics

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

BI and Data Science: Collaboration Using Data Handoffs

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

BI and Data Science: Matching Approaches to Applications

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

BI and Open Source Data Science: Strengths and Challenges

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

BI and Data Science: The Best of Both Worlds

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

RStudio: A Single Home for R and Python Data Science

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

Custom Google Analytics Dashboards with R: Building The Dashboard

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

Exploring US COVID-19 Cases and Deaths

Arthur Steinmetz, former Chairman, CEO, and President of OppenheimerFunds, uses R and the tidymodels package to explore the relationship between COVID-19 cases and mortality in the US

Announcing the 2020 R Community Survey

Want data about how people learn and use R? If so, please fill out our 3nd annual survey so that we can better understand today's R community. We'll publish the results in February 2021 as free and open source data

Custom Google Analytics Dashboards with R: Downloading Data

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

How California Uses Shiny in Production to Fight COVID-19

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

Why RStudio Focuses on Code-Based Data Science

Michael Lippis of The Outlook podcast recently interviewed RStudio's Lou Bajuk to discuss open source data science, support for R and Python, and how leaders are getting value from data science

Where Does RStudio Fit into Your Cloud Strategy?

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

Why RStudio Supports Python for Data Science

When our customers ask us why RStudio's products support Python as well, we have a simple answer: It's because our data shows that our customers use R and Python for different tasks

Open Source Data Science in Investment Management

Using R to Drive Agility in Clinical Reporting: Questions and Answers

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

RStudio Named Strong Performer in the Forrester Wave™: Notebook-Based Predictive Analytics and Machine Learning, Q3 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

Ease Uncertainty by Boosting Your Data Science Team's Skills

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

Debunking R and Python Myths: Answering Your Questions

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

3 Ways to Expand Your Data Science Compute Resources

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

Why Package & Environment Management is Critical for Serious Data Science

The renv package helps create reproducible project environments that are critical for data science teams to deliver real, lasting value

R & RStudio - The Interoperability Environment for Data Analytics

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

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

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

3 Wild-Caught R and Python Applications

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

Interoperability: Getting the Most Out of Your Analytic Investments

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

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

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

Interoperability in July

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

Future-Proofing Your Data Science Team

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

Does your Data Science Team Deliver Durable Value?

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

Is Your Data Science Team Agile?

Data science teams struggle to deliver results quickly. Agile techniques such as rapid iteration and continuous collaboration with stakeholders can help overcome these challenges

Is Your Data Science Credible Enough?

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

The Role of the Data Scientist

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

Driving Real, Lasting Value with Serious Data Science

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

Equipping Your Data Science Team to Work from Home

Wrangling Unruly Data: The Bane of Every Data Science Team

Avoid Irrelevancy and Fire Drills in Data Science Teams

Getting to the Right Question

Effective Visualizations for Credible, Data-Driven Decision Making

RStudio, PBC

JJ Allaire

R vs. Python: What's the best language for Data Science?

We will briefly examine the debates over R vs. Python, and then share