Serious Data Science
Set your team up for success
The building blocks of serious data science
By adopting an open source core, you make it easier to recruit and retain data scientists, while the comprehensive nature of open source ensures you will always have the right tool for any analytic problem, including the ability to connect to all your other analytic investments. You also avoid putting yourself at the mercy of any specific vendor, since your core data science work is based in R or Python.
Complex, sometimes vaguely-defined analytic problems require the power of code. Code is flexible, without any black box constraints, and enables you to access, transform and combine ALL of your data. Code enables fast iteration and updates in response to feedback, or new circumstances. And most importantly, by its very nature code is reusable, extensible and inspectable, allowing you to modify and apply it to new problems, and track where changes occurred. Code becomes a core source of intellectual property in your organization, the value of which grows over time.
By centralizing your data science infrastructure, you break down the siloes which impede your productivity. This allows you to reduce unnecessary time spent maintaining individual data scientist's environments, and promotes collaboration. Deploying your team's data science work to your stakeholders gives them self-service access to the insights when and where they need them, greatly increasing the impact of your team's work. Centralizing your development and deployment environments makes administration, security and management far easier, and package management promotes reproducibility over time.
"We use RStudio for analytics, data science, reporting, and statistical modeling for business clients in all enterprise functional groups. RStudio is the premier statistical workbench and development environment for professionals. It is well suited for serious data science and statistical analysis The Azure offerings help with data management and workflows."
Senior Manager, Automotive | 10,001+ employees
"Posit (formerly RStudio) is very well suited for data analysts and statisticians. Writing and designing predictive data models is very efficient Its compatibility with other platforms like SQL databases, Salesforce, Tableau , etc is amazing and makes it worth the investment. Positive impact is when you automate excel reports using Shiny applications, it ends up saving a lot of time and money."
Authenticated Reviewer, Analyst
"RStudio is vital to what we do. We run R and Python models in production and deliver them to multiple organisations. It's the only platform that I would consider for code first data science We using Python with reticulate to feed NLP analyses done in Python to an R/ Shiny powered dashboard. RStudio has enabled us to launch data science projects in the cloud at scale, which has brought in at least 10x more money than we spent on... "
Senior Analyst, Nottinghamshire Healthcare NHS Foundation Trust Hospital & Health Care | 10,001+ employees