Data scientist using R and Python

Data Scientists

Increase your impact on decision making by collaborating with BI teams

As a Data Scientist, you grapple daily with novel, potentially valuable, sometimes vaguely-defined problems that require extensive data preparation and a wide range of advanced analytic methods and visualizations to solve. That's why you use languages like R and Python.

But when BI tools are used widely in your organization, you might find yourself competing with those tools to get your insights out to the wider organization where they can have an impact on decision making, or find yourself forced to awkwardly use the unfamiliar data science integrations that the BI tools provide.

By leveraging RStudio, you can effectively collaborate with your BI colleagues and increase the visibility and impact of your work.

Learn more:

  • Create data pipelines that combine, transform and analyze disparate structured, unstructured and nontraditional data sets, and make that enriched data available for exploration and visualization in BI tools using Data Handoffs.
  • Make your predictive models and other analyses available as secure R and Python APIs which can be used in calculated fields in Tableau, to provide on-demand access to your crucial data insight.
  • Increase the reach of your Shiny visualizations and applications by embedding them in Tableau dashboards to support corporate reporting and visualizations.
  • By building a collaborative relationship with your BI colleagues, you can help validate analyst's findings through reproducible code and rigorous methods, help your organization manage some of the challenges of self-service BI tools, and when needed, collaboratively solve the most challenging analytic problems with fully custom, R- or Python-based analytic applications using RStudio Connect.

Augment your BI tools with Serious Data Science

While data science and BI teams share the same goals, to help their organizations make better decisions by getting the most valuable insights possible out of their data, each approach has distinct strengths and challenges. By leveraging Data Science tools to augment your BI investments, you can maximize the value of the insights you deliver to your decision-makers.

RStudio helps break down analytic silos and enhance an organization's analytic investments, so that all members of the data organization (BI analysts, data scientists, ML engineers, data engineers) can collaborate, get deeper insights, support better decision making and drive results.

RStudio Team not only provides a single home for data science teams using R and Python, it enables Data Science teams to augment the great work that BI teams provide:

  • Enrich your data using R and Python, for visualization and exploration in BI tools. Read more about Data Handoffs
  • Build tailored applications using open source frameworks such as Shiny, Dash and Streamlit, and share them with your business users to complement standard BI reporting. Read more about RStudio Connect
  • Securely call R and Python APIs from Tableau dashboards, to make the models, forecasts and other insights from your data science team available on demand. Read more about RStudio Connect and Tableau
  • Easily embed interactive Shiny applications in Tableau, to augment Tableau's standard visualizations and leverage the visual insights your data science team develops. Read more about ShinyTableau
  • Augment your BI tools existing R and Python integrations: Many BI tools have some form of integration with R or Python. RStudio Teams provides a secure, scalable, collaborative development environment for your data science teams to develop the open source code for deployment in your BI tools, as well as a way of managing your open source package use to ensure consistency and reproducibility across all your analytics platforms.

To learn more...

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