When it comes to choosing a programming language, people love to debate endlessly on such topics.

Should you start with R or Python programming?

Why Python is seemingly preferred and not R?

Evidently, both R and Python play a significant role in the life of a data science professional. Both programming languages are mandatory and useful and are found amongst the most frequently required skillsets by top employers. However, each of these programming languages offers certain advantages and disadvantages for performing data science work. However, based on the kind of project, the required programming language can be chosen for further analysis.

According to a 2019 survey by Stack Overflow, Python continues to be the fastest-growing programming language today. Further on, Python topped to be the most wanted programming language by 25.7% while R remained to be at 4.9%.

Data science has become humanity’s sixth sense.

Ironically, it’s also probably the sense the average person understands the least.

For anyone hoping to learn more, three experts were asked to recommend their favorite data science books.

The panel included:

Many of today’s smart products are reliant on processing in the cloud and the growing adoption of natural voice interfaces, imaging and presence detection, for example, not only raise performance issues but will create further challenges in the form of reliability, privacy and cost.

According to market research, by 2025 there is expected to be 65 billion connected devices generating 180 zetabytes of data, all of which will require complex and diverse processing capabilities.

This will be a massive opportunity.

“There is a huge market opportunity for a device that is able to address the needs of a range of applications delivering both performance and functionality while, at the same time, offering ease of use, low power and real-time operation.

Learn all about WSL2, the new version of the Windows Subsystem for Linux, and what changes have been made to improve performance.

Craig Loewen shows you how to get things installed and set up a Linux development workflow that is integrated with Windows and VS Code.

Check out the faster IO performance and system call compatibility, then watch Craig run an app from inside a container using Docker Desktop for Windows and debug it using VS Code.

Here are some links to learn more: