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 →

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 →

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 →

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 →

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

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. Read more →

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. Read more →

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. Read more →

Photo by Djurdjica Boskovic on Unsplash If your data science team experienced an abrupt transition to working at home, it may be a good time to rethink their development tools. In this post, I’ll talk about why laptop-centric data science gets in the way of strong data science teams and why you should consider deploying development and publishing servers. Working from Home Has Affected Both People and Data Like tigers and koalas, we data scientists are fairly solitary creatures. Read more →

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 →