There’s an old saying (at least old in data scientist years) that goes, “90% of data science is data wrangling.” This rings particularly true for data science leaders, who watch their data scientists spend days painstakingly picking apart ossified corporate datasets or arcane Excel spreadsheets. Does data science really have to be this hard? And why can’t they just delegate the job to someone else? Data Is More Than Just Numbers The reason that data wrangling is so difficult is that data is more than text and numbers. Read more →

Balancing the twin threats of data science development Data science leaders naturally want to maximize the value their teams deliver to their organization, and that often means helping them navigate between two possible extremes. On the one hand, a team can easily become an expensive R&D department, detached from actual business decisions, slowly chipping away only to end up answering stale questions. On the other hand, teams can be overwhelmed with requests, spending all of their time on labor intensive, manual fire-drills, always creating one more “Just in Time” Powerpoint slide. Read more →

The Root Problem: We Don’t All Speak the Same Language Organizations across the modern business world recognize the critical importance of Data Science for competitive advantage. That recognition has driven Glassdoor to rate Data Scientist as one of the 25 top paying jobs in America in 2020. However, many organizations struggle to put these data scientists’ knowledge to work in their businesses where they can actually have an impact on success. Read more →

Recently, we were joined by the smart folks at Roche & Novartis to present a webinar on effective data visualization. You can watch the recording of the full presentation here. It was the latest installment in a series of webinars highlighting industry leaders in the Pharmaceutical and Life Science spaces that are doing world-changing data science work. They presented many great insights, most of which are relevant to data scientists in every industry, and we wanted to share our learnings. Read more →

We started the RStudio project because we were excited and inspired by R. The creators of R provided a flexible and powerful foundation for statistical computing; then made it free and open so that it could be improved collaboratively and its benefits could be shared by the widest possible audience. It’s better for everyone if the tools used for research and science are free and open. Reproducibility, widespread sharing of knowledge and techniques, and the leveling of the playing field by eliminating cost barriers are but a few of the shared benefits of free software in science. Read more →

We will briefly examine the debates over R vs. Python, and then share why we believe R and Python can, should and do work beautifully together. Read more →