The Best R Packages for Machine Learning
There is no doubt R is language of choice for the majority of data scientists who want to understand data, especially those looking to leverage its great machine learning packages.
The most frequently asked question in our data science training program is "what is the best programming language for machine learning?"
The resulting discussion, depending on the day, either ends in a hotly contested debate between R, Python, and MATLAB fans, or a full on WWE wrestling match.
Ultimately the programming language of choice for machine learning comes down to three criteria:
- The type of problem the data scientist is working with
- Personal programming preferences
- The type of machine learning they're looking to perform
In other words, it depends. However, there is no doubt R is language of choice for the majority of data scientists who want to understand data, especially those looking to leverage its great machine learning packages. R also boasts being open source which is great for anyone looking to get started with machine learning in their spare time.
About the report
At The Data Incubator we pride ourselves on having the latest data science curriculum. Much of our course material is based on feedback from corporate and government partners about the technologies they are looking to learn. However, we wanted to develop a more data-driven
approach to what we teach in our data science corporate training and our free fellowship for Data Science masters and PhDs looking to begin their careers in the industry.
This report is the first in a series analyzing data science related topics. We thought it would be useful to the data science community to rank and analyze a variety of topics related to the profession in a simple, easy to digest cheat sheet, rankings or reports. This first report ranks R packages for machine learning, and we're hoping to stir the pot a bit and get our colleagues to join the discussion. Our discoveries here aren't final, but rather serve to showcase the depth, and the breadth, of knowledge available to the data science community.