data science

Open Source Data Science in Investment Management

2020-10-13 Art Steinmetz, former Chairman, CEO and President of OppenheimerFunds
Thumbnail thumbnail.jpg
Photo by Kelly Sikkema on Unsplash Surviving the Data Deluge Many of the strategies at my old investment shop were thematically oriented. Among them was the notion of the “data deluge.” We sought to invest in companies that were positioned to help other companies manage the exponentially growing torrent of data arriving daily and turn that data into actionable business intelligence. Ironically, we ourselves struggled to effectively use our own data. Read more →

Introducing torch for R

2020-09-29 The RStudio Multiverse Team
Thumbnail
As of this writing, two deep learning frameworks are widely used in the Python community: TensorFlow and PyTorch. TensorFlow, together with its high-level API Keras, has been usable from R since 2017, via the tensorflow and keras packages. Today, we are thrilled to announce that now, you can use Torch natively from R! This post addresses three questions: What is deep learning, and why might I care? What’s the difference between torch and tensorflow? Read more →

Ease Uncertainty by Boosting Your Data Science Team's Skills

2020-09-23 Carl Howe
Thumbnail thumbnail.jpg
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. Read more →

Learning Data Science with RStudio Cloud: A Student's Perspective

2020-09-17 Daniel Petzold, Carl Howe
Thumbnail thumbnail.jpg
Lara Zaremba, a student at Goethe University, shares her experiences using RStudio Cloud to learn data science and how it has empowered her to help teach others. Read more →

R and RStudio - The Interoperability Environment for Data Analytics

2020-08-17 Curtis Kephart and Lou Bajuk
Thumbnail thumbnail.png
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 →

Interoperability: Getting the Most Out of Your Analytic Investments

2020-07-15 Lou Bajuk, Carl Howe
Thumbnail thumbnail.jpg
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
Thumbnail thumbnail.jpg
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
Thumbnail thumbnail.jpg
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
Thumbnail thumbnail.jpg
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
Thumbnail thumbnail.jpg
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 →