R and Python are frequently cited as the most common programming languages for data scientists due to their intuitive nature and robust analytics capabilities. For example, a G2 reviewer of a data science and machine learning platform, said:

"Implementing data science or machine learning can be challenging using programming languages like R or python for a beginner."

Although the popularity of R and Python are in part driven by them being open-source languages, it also raises concerns about data privacy. Therefore, buyers will likely have questions about a software's ability to prevent hacks and maintain data integrity.

If vendors are going to pay for a machine learning software, they will look for robust algorithms that come with the purchase. The bottom line of working with data is the efficiency and effectiveness of algorithms that help the machine learn and make predictions. Similarly, vendors must determine whether the tool and its algorithms can handle enormous data sets. With businesses now having millions (and even billions) of data points, the software must function seamlessly when processing copious amounts of information.

Finally, vendors are also considering the ease of use of data science and machine learning software. An intuitive interface that provides clean and attractive data processing tools is vital, and the ability to integrate with other popular programming tools and machine learning libraries will be influential in the software selection process.