Join me at the useR!2021 virtual conference starting July 5
The annual R Conference useR! 2021 begins on Monday, July 5, and runs through July 9. "The useR! 2021 conference will be the first R conference that is global by...continue reading.
Author: Mark Hornick
R Consortium Infrastructure Steering Committee Call for Proposals – Now Open!
One focus of the R Consortium is to strengthen the R community by improving infrastructure and building for long term ecosystem stability. The R Consortium’s Infrastructure Steering Committee (ISC) funds...continue reading.
Data Scientists – scale your R applications with Oracle Machine Learning
You may have seen that Oracle R Distribution 3.6.1 was recently released along with compatibility for Oracle Machine Learning for R (OML4R), formerly Oracle R Enterprise, version 1.5.1. What you...continue reading.
Are all algorithm implementations created equal?
Unfortunately, not all machine learning algorithm implementations are the same, which can have significant impact on data science project success. Too often, a data science project that shows promise in...continue reading.
New Release of ROracle 1.3-2 now available
We are pleased to announce the latest update of the open source ROracle package, version 1.3-2, with the following enhancements: Compatibility with R 3.6.0 Use ora.encoding for DML when specified...continue reading.
To sample or not to sample…part 2
In my previous post To sample or not to sample, we discussed some of the issues involved in sampling data for use in machine learning. In this post, we look...continue reading.
To sample or not to sample…
Ideally, we would know the exact answer to every question. How many people support presidential candidate A vs. B? How many people suffer from H1N1 in a given state? Does this batch...continue reading.
Loading “wide data” in Oracle Database using Oracle R Enterprise
Data sets come in many shapes and sizes. Some are tall and thin, others are short and wide. Some take on the form of dense data, a.k.a., single-record case, where...continue reading.
R to Oracle Database Connectivity: Use ROracle for both Performance and Scalability (2018)
R users have a few choices of how to connect to Oracle Database. The most commonly seen include: RODBC, RJDBC, and ROracle. However, these three packages have significantly different performance...continue reading.
Should R Consortium Recommend CII Best Practices Badge for R Packages? Latest Survey Results
Based on our Fall 2017 survey, where the R Consortium asked about opportunities, concerns, and issues facing the R community, the R Consortium conducted a new survey this past month...continue reading.
Data Science Maturity Model – Summary Table for Enterprise Assessment (Part 12)
This installment of the Data Science Maturity Model (DSMM) blog series contains a summary table of the dimensions and levels. Enterprises embracing data science as a core competency may want...continue reading.
Data Science Maturity Model – Deployment (Part 11)
In this next installment of the Data Science Maturity Model (DSMM) dimension discussion, I focus on ‘deployment’: How easily can data science work products be placed into production to meet...continue reading.
Data Science Maturity Model – Tools Dimension (Part 10)
In this next installment of the Data Science Maturity Model (DSMM) dimension discussion, I focus on ‘tools’: What tools are used within the enterprise for data science? Can data scientists...continue reading.
Data Science Maturity Model – Asset Management Dimension (Part 9)
In this next installment of the Data Science Maturity Model (DSMM) dimension discussion, I focus on ‘asset management’: How are data science assets managed and controlled? Assets are typically both...continue reading.
Data Science Maturity Model – Scalability Dimension (Part 8)
In this next installment of the Data Science Maturity Model (DSMM) dimension discussion, I focus on ‘scalability’: Do the tools scale and perform for data exploration, preparation, modeling, scoring, and...continue reading.
Data Science Maturity Model – Data Access Dimension (Part 7)
In this next installment of the Data Science Maturity Model (DSMM) dimension discussion, I focus on ‘data access’: How do data analysts and data scientists request and access data? How...continue reading.
R Consortium solicits feedback on R package best practices
With over 12,000 R packages on CRAN alone, the choice of which package to use for a given task is challenging. While summary descriptions, documentation, download counts and word-of-mouth may...continue reading.
Data Science Maturity Model – Data Awareness Dimension (Part 6)
In this next installment of the Data Science Maturity Model (DSMM) dimension discussion, I focus on ‘data awareness’: How easily can data scientists learn about enterprise data resources? Generally speaking,...continue reading.
Data Science Maturity Model – Methodology Dimension (Part 5)
In this next installment of the Data Science Maturity Model (DSMM) dimension discussion, I focus on ‘methodology’: What is the enterprise approach or methodology to data science? The most often...continue reading.
Data Science Maturity Model – Collaboration Dimension (Part 4)
In this next installment of the Data Science Maturity Model (DSMM) dimension discussion, I focus on ‘collaboration’: How do data scientists collaborate among themselves and with others in the enterprise,...continue reading.
