This week, R users from around the world convene in San Francisco for rstudio::conf 2020. With a packed agenda of new package announcements and case studies highlighting successful applications of R across different industries, it’s evident that R and the ecosystem of tools around it make up a vital part of the data science and machine learning landscape.

One of the really interesting topics in business intelligence is the desire for organizations to take action from data. While most use dashboards as a source of information, it certainly doesn't prompt or drive them to act. This means you can’t close the loop on your insights and the decisions that they drive.

When working on web and graphic designs there can be so much back and forth in emails and messages. There will be lots of email threads to keep up with. The review and approval process taking several turns. So, what do you do? Ask any designer about the pain they go through when it comes to the approval process. Luckily, the technology clearly will never disappoint any of us.

Enterprise software companies sell very differently today than just a decade ago. Previously, most software was shrink-wrapped, required a lot of effort to distribute and implement, and was sold to an executive who would have to deploy it throughout their department or organization. The buying process involved a long sales cycle and often included pilots, cost-benefit analyses, procurement, and legal reviews.

Have you ever dealt with a unicode bug? Where plain text — the substance you work with all day — can no longer be trusted? It can be disorienting to say the least! This article will help prepare you so that the next time that happens you’ll be able to spend less time hyperventilating and more time troubleshooting.

We are excited to release Deep Learning for Anomaly Detection, the latest applied machine learning research report from Cloudera Fast Forward Labs. Anomalies, often referred to as outliers, are data points or patterns in data that do not conform to a notion of normal behavior. Anomaly detection, then, is the task of finding those patterns in data that do not adhere to expected norms. The capability to recognize or detect anomalous behavior can provide highly useful insights across industries.