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Editor's Note: The following is an article written for and published in DZone's 2021 Low-Code Development Trend Report.
If you ride on a sailboat in a steady breeze, it glides through the water effortlessly. Few things can compare to crossing a bay without the noise and commotion of a thundering internal combustion engine.
Last week, I assembled a Raspberry Pi-based car kit. I bought the kit from Amazon. (Affiliate link.) The car has a live video camera, a sonic collision sensor, and a line-following sensor. You'll need a Raspberry Pi to go with it. I recommend a 3 B+.
The car works with an Android or iOS app, and the apps are functional enough, but where's the fun in simply relying on them?
Notebooks are becoming the standard for prototyping and analysis for data scientists. Many cloud providers offer machine learning and deep learning services in the form of Jupyter notebooks. Other players have now begun to offer cloud-hosted Jupyter environments, with similar storage, compute and pricing structures. One of the main differences can be multi-language support and version control options that allow data scientists to share their work in one place.
Jupyter notebook environments are now becoming the first destination in the journey to productizing your data science project. The notebook environment allows us to keep track of errors and maintain clean code. One of the best features although simple is that the notebook would stop compiling your code if it spots an error. Regular IDE’s do not stop compilation even if an error is detected and, depending on the amount of code, it can be a waste of time to go back and manually detect where the error is located.
Since its advent in 1991, Python has been continued to increase in popularity due to its simple features, short learning curve, and code readability. There have been lots of changes in the codebase over time due to the contributions from developers worldwide. It is used to create websites and to perform data processing, manipulation, and analysis.
To make the task of running Python programs easier, many IDEs have been developed for programmers. These environments are equally suitable for beginners and advanced coders with a variety of in-built extensions, libraries, and plugins to ease the task of program execution.
R is an interpreted programming language for statistical computing and graphics supported by the R Foundation. It is widely used among statisticians and data miners for developing statistical software and data analysis.
R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows, and macOS.